Bandung Conference Series: Statistics
type: channel
description: Bandung Conference Series: Statistics merupakan wadah publikasi hasil-hasil penelitian dan pengabdian masyarakat dalam bidang Ilmu Statistik yang telah dipresentasikan pada Seminar Nasional UNISBA yang diselenggarakan tahunan oleh UPT Publikasi Ilmiah Universitas Islam Bandung. BCSS ini dipublikasikan pertamanya 2021 dengan eISSN 2828-206X yang diterbitkan oleh UPT Publikasi Ilmiah, Universitas Islam Bandung. Semua artikel diperiksa plagiasinya dengan perangkat lunak anti plagiarisme. Jurnal ini ter-indeks di Google Scholar, Garuda, Crossref, dan <a title="DOAJ" href="https://doaj.org/search/journals?ref=quick-search&source=%7B%22query%22%3A%7B%22filtered%22%3A%7B%22filter%22%3A%7B%22bool%22%3A%7B%22must%22%3A%5B%7B%22terms%22%3A%7B%22bibjson.publisher.name.exact%22%3A%5B%22Universitas%20Islam%20Bandung%22%5D%7D%7D%5D%7D%7D%2C%22query%22%3A%7B%22query_string%22%3A%7B%22query%22%3A%22universitas%20islam%20bandung%22%2C%22default_operator%22%3A%22AND%22%2C%22default_field%22%3A%22bibjson.publisher.name%22%7D%7D%7D%7D%7D" target="_blank" rel="noopener">DOAJ. Terbit setiap Maret dan September.
publisher: UNISBA Press
language: en-US
publication name: Bandung Conference Series: Statistics
issn: 2828-206X
items: Klasifikasi Sentimen Publik terhadap Program MBG di Media Sosial X dengan IndoBERTweet
items: Klasifikasi Citra Sampah Daur Ulang Menggunakan CNN dengan Fungsi Aktivasi ReLU untuk Mendukung Sistem RVM
items: Pemodelan Kasus Tuberkulosis di Jawa Barat Menggunakan Metode Generalized Poisson Regression
items: Klasifikasi Sentimen Publik Terhadap Banjir di Sumatera pada Teks Berita dan Media Sosial X Menggunakan IndoBERT
items: Analisis Agglomerative Hierarchical Clustering dalam Pengelompokan Penyandang Disabilitas di Kota Bandung Tahun 2025
items: Penerapan Metode ARIMA Untuk Meramalkan Harga Logam Mulia ANTAM
items: Hubungan Korean Wave dengan Fashion Style Mahasiswa Statistika Unisba Angkatan 2022 – 2024
items: Prediksi Harga Penutupan IHSG Menggunakan LSTM dengan Optimizer Adam dan Grid Search Hyperparameter
items: Peramalan Pendapatan PT Telkom Indonesia (Persero) Tbk Berdasarkan Data Periode 2016-2024 Menggunakan Metode Double Exponential Smoothing-Holt.
items: Pengelompokan Wilayah Pulau Jawa Berdasarkan Kendaraan Bermotor Menggunakan K-Medoids Dengan Euclidean Distance Tahun 2026
items: Penerapan Metode K-Medoids dengan jarak manhattan untuk Pengelompokan Wilayah Berdasarkan Faktor Risiko DBD di Jawa Barat
items: Peramalan Tingkat Pengangguran Terbuka Di Provinsi Jawa Barat Menggunakan Metode DMA Dan DES
items: Analisis Segmentasi Mahasiswa Program Studi Statistika Universitas Islam Bandung Tahun 2020-2025 Menggunakan Algoritma K-Modes Clustering
items: Pengelompokan Kabupaten/Kota di Pulau Jawa Berdasarkan Indikator Kesejahteraan Rakyat Menggunakan Fuzzy C-Means
items: Klasifikasi Provinsi di Indonesia Berdasarkan IPM 2025 Menggunakan K-Medoids Clustering
items: Analisis Sentimen Sistem Pembayaran BI-FAST Menggunakan Metode Semantic Orientation CALculator (SO-CAL)
items: Peramalan Jumlah Kunjungan Wisatawan Mancanegara di Indonesia Periode Pra-Pandemi COVID-19 Menggunakan Model SARIMA
items: Analisis Korespondensi Berganda pada Korban Kekerasan terhadap Perempuan di Pulau Jawa
items: Pengelompokan Wilayah Rentan Kebakaran Hutan di Indonesia menggunakan Algoritma I-CLARANS
items: K-Medoids Clustering untuk Pengelompokan Faktor Perceraian di Jawa Barat Tahun 2023
items: Klasifikasi Kabupaten/Kota di Jawa Barat Berdasarkan Tingkat Kemiskinan Menggunakan K-Means Clustering
items: Analisis Korelasi Valid Day, Duration, dan Diamond Dalam Live Streaming TikTok
items: Pengelompokan Kabupaten/Kota di Jawa Barat Berdasarkan Akses Air Minum dan Sanitasi Layak Menggunakan Metode Ward
items: Analisis Value at Risk Dengan Metode Historical, Variance-Covariance, dan Monte Carlo pada Saham Sektor Konsumsi Primer
items: Pendugaan Sebaran Nilai Dissolved Oxygen (DO) dengan Metode Inverse Distance Weighting (IDW) pada Sungai di DKI Jakarta Tahun 2023
items: Pemodelan Kemiskinan di Provinsi Jawa Barat dengan Geographically Weighted Regression (GWR)
items: Peningkatan Automatic Speech Recognition Dialek Bugis-Makassar Menggunakan Data Ucapan Sintetis
items: Perbandingan Model VAR Tanpa dan Dengan Seasonal Dummy untuk Peramalan Inflasi
items: Penerapan Metode K-Medoids Clustering pada Kasus Stunting di Kota Bandung Tahun 2024
items: Reduksi Noise Data Curah Hujan di Kota Bandung Menggunakan Variational Mode Decomposition (VMD)
items: Pengelompokan Provinsi Berdasarkan Karakteristik Pendidikan Pemuda Menggunakan K-Medoids
items: Pemodelan Harga Penutupan Saham Bank Jago Menggunakan Metode ARIMA Box-Jenkins
items: Peramalan Harga Emas Menggunakan Geometric Brownian Motion
items: Clustering Kualitas Udara DKI Jakarta dengan Algoritma K-Medoids Berdasarkan Indeks Standar Pencemar Udara
items: Pengelompokan Tingkat Kesejahteraan Kabupaten/Kota di Jawa Barat Tahun 2024 Menggunakan Metode K-Medoids
items: Analisis Deskriptif Skor Pola Pangan Harapan (PPH) Per Kabupaten/Kota di Provinsi Jawa Barat Tahun 2020-2024
items: Pemodelan Regresi Gamma pada Data Indeks Pembangunan Manusia Kabupaten/Kota di Provinsi Jawa Barat Tahun 2025
items: Pemodelan Topik Skripsi Statistika Universitas Islam Bandung Tahun 2020–2025 Menggunakan BERTopic KernelPCA–K-Means
items: Peramalan Harga Saham PT Bank Rakyat Indonesia (Persero) Tbk Menggunakan Model Autoregressive Integrated Moving Average (ARIMA)
items: Pemodelan Tingkat Kemiskinan di Jawa Timur Menggunakan Regresi Ridge Hoerl–Kennard–Baldwin (HKB)
items: Perbandingan Metode Double Exponential Smoothing Holt, Fuzzy Time Series Lee, Dan Fuzzy Time Series Stevenson-Porter Pada Peramalan Harga Penutupan Saham BBRI
items: Analisis Perbedaan Jumlah Migrasi Penduduk Kota Bandung menggunakan Uji T² Hotelling

Klasifikasi Sentimen Publik terhadap Program MBG di Media Sosial X dengan IndoBERTweet
type: item
description: Abstract. The Free Nutritious Food Program (MBG) is Indonesia's national school feeding program, developed in line with the World Food Programme (WFP) framework. Since its launch on January 6, 2025, the program has generated diverse public responses, one of which has been expressed through social media platform X. The high volume of MBG-related tweets makes manual sentiment analysis inefficient, necessitating a computational approach based on Natural Language Processing (NLP). This study applies IndoBERTweet, a pre-trained language model based on the BERT architecture developed specifically for processing Indonesian Twitter text, to classify public sentiment regarding the MBG program. A total of 3,372 tweets were collected using web scraping with keyword "MBG" in December 2025. Data underwent preprocessing, manual sentiment labeling by three annotators using majority voting, tokenization, training, validation, testing data splitting, and fine-tuning using the IndoBERTweet model. After preprocessing, sentiment distribution consisted of 1,332 negative, 927 neutral, and 497 positive tweets from 2,756 total tweets. Evaluation results on testing data yielded accuracy of 73.67%, weighted precision of 73.73%, weighted recall of 73.67%, and weighted F1-score of 73.35%, categorized as "Good" performance. The negative sentiment class achieved the best performance with an F1-score of 80.19%, while neutral class had the lowest F1-score of 64.94%. Abstrak. Program Makan Bergizi Gratis (MBG) merupakan program pemberian makan nasional di Indonesia yang mengacu pada kerangka World Food Programme (WFP). Sejak diluncurkan pada 6 Januari 2025, berbagai respons masyarakat terhadap program tersebut muncul di berbagai media, salah satunya melalui media sosial X. Tingginya volume cuitan terkait MBG menyebabkan analisis sentimen secara manual menjadi kurang efisien sehingga diperlukan pendekatan komputasional berbasis Natural Language Processing (NLP). Penelitian ini menerapkan IndoBERTwee…
creator: Faliq Fairuz Al Faza
creator: Abdul Kudus
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-07-13
publication date: 2026-07-13
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.23802

Klasifikasi Citra Sampah Daur Ulang Menggunakan CNN dengan Fungsi Aktivasi ReLU untuk Mendukung Sistem RVM
type: item
description: Abstract. The total amount of waste generated nationwide in Indonesia reaches 144,839 metric tons per day, while only about 25% is managed optimally. This situation underscores the need for technologies capable of supporting automated waste management and sorting processes. One such technology is the Reverse Vending Machine (RVM), a machine that can accept and sort recyclable waste while providing incentives to users. To perform these functions optimally, RVM must be able to automatically and accurately recognize various types of trash. Therefore, this study aims to classify images of recyclable waste using a Convolutional Neural Network (CNN) with a Rectified Linear Unit (ReLU) activation function. The dataset used consists of five waste classes: paper/cardboard packaging, beverage cans, dented beverage cans, plastic bottles, and glass bottles. The CNN architecture used consists of three convolutional layers with a 3×3 kernel size and 32, 64, and 128 filters, respectively, and is supplemented by max pooling, flatten, and fully connected layers. The model was trained using the Stochastic Gradient Descent (SGD) algorithm with a mini-batch approach. The research results show that the model is capable of classifying images of recyclable waste with good performance, achieving an accuracy of 84.52%, a precision of 86.18%, a recall of 84.52%, and an F1-score of 84.46%. Abstrak. Jumlah timbulan sampah nasional di Indonesia mencapai 144.839 ton per hari, sementara hanya sekitar 25% yang terkelola secara optimal. Kondisi tersebut mendorong perlunya penerapan teknologi yang mampu mendukung proses pengelolaan dan pemilahan sampah secara otomatis. Salah satu teknologi yang dapat diterapkan adalah Reverse Vending Machine (RVM), yaitu mesin yang mampu menerima dan memilah sampah daur ulang sekaligus memberikan insentif kepada penggunanya. Untuk menjalankan fungsi tersebut secara optimal, RVM memerlukan kemampuan untuk mengenali berbagai jenis sampah secara otomatis dan akurat. Ol…
creator: Akmal Nufus
creator: Reny Rian Marliana
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-07-30
publication date: 2026-07-30
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.24217

Pemodelan Kasus Tuberkulosis di Jawa Barat Menggunakan Metode Generalized Poisson Regression
type: item
description: Abstract. Poisson regression is widely used to model count data, but this method assumes the variance and the mean are equal, known as equidispersion. In practice, this assumption is often not met due to overdispersion, a condition where the variance is much greater than the mean, making parameter estimates less accurate when ordinary Poisson regression is used. This study applies the Generalized Poisson Regression (GPR) method, an extension of Poisson regression with an added dispersion parameter to handle overdispersion, to model tuberculosis (TB) cases in 27 regencies/cities in West Java Province in 2024. The data used are secondary data from Statistics Indonesia and West Java Open Data, with population density, poverty percentage, sanitation access, number of HIV/AIDS cases, and number of health facilities as predictors. Parameters were estimated using Maximum Likelihood Estimation with the Newton-Raphson iteration, while significance testing used the Likelihood Ratio Test and the Wald test. The Pearson Chi-Square dispersion test showed a value of 682.744, far above 1, meaning the data experienced overdispersion so GPR was appropriately used. Simultaneous testing showed all predictors had a significant joint effect, while partial testing showed only population density and health facilities had a significant effect on tuberculosis cases in West Java. Abstrak. Regresi poisson umum digunakan untuk memodelkan data cacah, namun metode ini mengasumsikan nilai varians dan rata-rata sama, atau disebut equidispersi. Pada praktiknya, asumsi tersebut sering tidak terpenuhi karena terjadi overdispersi, yaitu kondisi ketika nilai varians jauh lebih besar dari rata-ratanya, sehingga estimasi parameter menjadi kurang akurat apabila tetap menggunakan regresi poisson biasa. Oleh karena itu, penelitian ini menerapkan metode Generalized Poisson Regression (GPR) yaitu pengembangan dari regresi poisson dengan tambahan parameter dispersi untuk mengatasi overdispersi, guna memodelkan …
creator: Tiara Yulianisah
creator: Nusar Hajarisman
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-07-31
publication date: 2026-07-31
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.24314

Klasifikasi Sentimen Publik Terhadap Banjir di Sumatera pada Teks Berita dan Media Sosial X Menggunakan IndoBERT
type: item
description: Abstract. Flood disasters on Sumatra Island have triggered diverse public responses through online news coverage and social media platform X. This study applies IndoBERT, a BERT-based model that uses pre-trained weights and is subsequently fine-tuned to classify public sentiment regarding the disaster. Data were collected through web scraping from three national news portals, namely Detik, CNBC Indonesia, and Kompas, as well as from social media X using the keyword “banjir Sumatera” between November 28, 2025, and January 9, 2026. After data selection, 2,687 records were obtained, consisting of 1,494 news texts and 1,193 X posts. This study aims to evaluate IndoBERT performance on two data types with different linguistic characteristics. Initial sentiment labeling was conducted using the InSet lexicon, producing three classes: negative, neutral, and positive. On news data, IndoBERT achieved an accuracy of 0.81, precision of 0.81, recall of 0.80, and F1-score of 0.80. On social media X data, it achieved an accuracy of 0.79, precision of 0.78, recall of 0.78, and F1-score of 0.78. These findings indicate that IndoBERT performs better on news texts because their formal language characteristics are more consistent with the model’s pre-training corpus. The results demonstrate that corpus compatibility influences model performance across different Indonesian textual domains. Abstrak. Bencana banjir di Pulau Sumatera memunculkan beragam respons publik melalui pemberitaan media berita online dan media sosial X. Penelitian ini menerapkan IndoBERT berbasis arsitektur BERT dengan memanfaatkan bobot pre-trained yang kemudian digunakan pada proses fine-tuning untuk mengklasifikasikan sentimen publik terkait bencana tersebut. Data dikumpulkan melalui web scraping dari tiga portal berita nasional, yaitu Detik, CNBC Indonesia, dan Kompas, serta media sosial X menggunakan kata kunci “banjir Sumatera” pada periode 28 November 2025 hingga 9 Januari 2026. Setelah proses seleksi, diperol…
creator: Marini Salmonia Kisa
creator: Fauziah Roshafara
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-07-31
publication date: 2026-07-31
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.24414

Analisis Agglomerative Hierarchical Clustering dalam Pengelompokan Penyandang Disabilitas di Kota Bandung Tahun 2025
type: item
description: Abstract. Persons with disabilities are a population group that requires special attention in the formulation of public policy. Variation in the number of persons with disabilities across sub-districts (kecamatan) in Bandung City indicates the need for a regional grouping so that policies can be more precisely targeted. This study applies Agglomerative Hierarchical Clustering to group 30 sub-districts in Bandung City in 2025 based on six types of disability, namely physical, visual, hearing/speech, mental, physical-and-mental, and other disabilities. Data were obtained from the Bandung City Population and Civil Registration Office. The variables were standardized using the Z-score before the Euclidean Distance was calculated. Four linkage methods, Single Linkage, Complete Linkage, Average Linkage, and Ward's Method, were compared, and the Cophenetic Correlation Coefficient was used to select the best linkage method, followed by the Dunn Index to determine the optimal number of clusters. The results show that Average Linkage produced the highest Cophenetic Correlation Coefficient (0.8053), and the Dunn Index indicated that six clusters produced the best cluster quality (0.7641). The six clusters show distinct characteristics in the number of persons with disabilities, ranging from the lowest to the highest average. These results are expected to support the Bandung City Government in policy formulation, equitable services, and program prioritization for persons with disabilities. Keywords: Agglomerative Hierarchical Clustering, Cophenetic Correlation Coefficient, Dunn Index, Disability, Bandung City. Abstrak. Penyandang disabilitas merupakan kelompok masyarakat yang memerlukan perhatian dalam penyusunan kebijakan dan penyediaan pelayanan publik. Perbedaan karakteristik jumlah penyandang disabilitas pada setiap kecamatan di Kota Bandung menyebabkan perlunya pengelompokan wilayah agar kebijakan yang disusun dapat lebih tepat sasaran. Salah satu metode yang dapat digunak…
creator: Najwa Anisa Fitri
creator: Teti Sofia Yanti
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-07-31
publication date: 2026-07-31
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.24443

Penerapan Metode ARIMA Untuk Meramalkan Harga Logam Mulia ANTAM
type: item
description: Abstract. Forecasting is a method of predicting future events based on historical data. The Autoregressive Integrated Moving Average (ARIMA) is a forecasting method that does not require independent variables in generating predictions. Instead, ARIMA utilizes historical and current values of the dependent variable to produce accurate short-term forecasts. The objective of this study is to forecast the price of Antam precious metals over several future periods, with the results expected to support investment decision-making in precious metals. Precious metals are one of the most popular investment instruments because they have relatively stable values and are often regarded as a safe-haven asset during periods of economic uncertainty. Based on the forecasting results of the daily price of one gram of Antam precious metals using the ARIMA method, the best forecasting model obtained was ARIMA(2,1,1), with the equation . The model produced an Akaike Information Criterion (AIC) value of -11833.17. The forecasting results indicate that the price of one gram of Antam precious metals on 3 July 2026 is predicted to be IDR 2,637,892, while on 15 July 2026 the price is expected to experience slight fluctuations, with an average price of approximately IDR 2,636,474 per gram. Abstrak. Peramalan merupakan metode prakiraan tentang peristiwa di masa yang akan datang yang memperlihatkan data masa lalu. ARIMA merupakan metode peramalan yang tidak menggunakan variabel bebas saat melakukan prakiraan. ARIMA menggunakan data masa lalu dan saat ini dari variabel dependen untuk membuat peramalan jangka pendek akurat. Tujuan peramalan pada penelitian ini untuk melihat hasil ramalan harga Logam Mulia Antam pada beberapa periode kedepan yang berguna dalam pengambilan keputusan investasi emas logam mulia. Logam Mulia merupakan salah satu instrumen investasi yang banyak diminati masyarakat karena memiliki nilai yang relatif stabil dan sering dijadikan safe haven pada kondisi ekonomi yang tidak …
creator: Isnani Nur Annisa
creator: Teti Sofia Yanti
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-07-31
publication date: 2026-07-31
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.24455

Hubungan Korean Wave dengan Fashion Style Mahasiswa Statistika Unisba Angkatan 2022 – 2024
type: item
description: Abstract Korean Wave or Hallyu, refers to the spread of South Korean pop culture that has made a significant impact in various countries, including Indonesia. This phenomenon includes not only music and drama, but also fashion which has become a major attraction among young people. Among Statistics students of Universitas Islam Bandung (Unisba) batch 2022-2024, the influence of the Korean Wave is evident in the change of their dressing style. This study aims to explore the relationship between Korean Wave and fashion style among these students. This research is expected to provide insight into how outside culture, particularly from South Korea, is able to influence the identity and self-expression of students in Indonesia, as well as highlighting the dynamics of interaction between cultural globalization and locality in the context of fashion. Abstrak. Korean Wave, atau Hallyu, merujuk pada penyebaran budaya pop Korea Selatan yang telah memberikan dampak signifikan di berbagai negara, termasuk Indonesia. Fenomena ini tidak hanya mencakup musik atau drama, tetapi juga fashion yang menjadi daya tarik utama di kalangan anak muda. Di kalangan mahasiswa Statistika Universitas Islam Bandung (Unisba) angkatan 2022-2024, pengaruh Korean Wave terlihat jelas dalam perubahan gaya berpakaian mereka. Penelitian ini bertujuan untuk mengeksplorasi hubungan antara Korean Wave dan fashion style di kalangan mahasiswa tersebut. Penelitian ini diharapkan dapat memberikan wawasan tentang bagaimana budaya luar, khususnya dari Korea Selatan, mampu mempengaruhi identitas dan ekspresi diri mahasiswa di Indonesia, serta menyoroti dinamika interaksi antara globalisasi budaya dan lokalitas dalam konteks fashion.
creator: Rusdian Mulyana
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-07-31
publication date: 2026-07-31
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.24517

Prediksi Harga Penutupan IHSG Menggunakan LSTM dengan Optimizer Adam dan Grid Search Hyperparameter
type: item
description: Abstract. The Indonesia Composite Index (ICI) is the primary indicator of the Indonesian capital market and exhibits highly fluctuating movements, making an accurate forecasting method essential. This study aims to develop a forecasting model for the ICI closing price using the Long Short-Term Memory (LSTM) network with the Adam optimizer and to determine the optimal hyperparameter combination using Grid Search. The data consisted of daily ICI closing prices from January 2020 to June 2026 obtained from Yahoo Finance. The dataset was divided into training (80%) and testing (20%) sets and subsequently normalized using Min-Max Normalization. Hyperparameter optimization was performed by evaluating 81 combinations of sequence length, hidden units, mini-batch size, and epochs. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE) and the coefficient of determination (R²). The results indicate that the best model was achieved with a sequence length of 60, 128 hidden units, a mini-batch size of 16, 100 epochs, and a learning rate of 0.001, resulting in a testing MAPE of 1.0429% and an R² of 0.9839. The model successfully captured the historical patterns of the ICI and generated a 30-trading-day forecast that indicated a gradual upward trend in the closing price. These findings demonstrate that the LSTM model with the Adam optimizer and Grid Search-based hyperparameter optimization provides strong predictive performance for forecasting the ICI closing price. Keywords: Long Short-Term Memory (LSTM), Adam, ICI. Abstrak. Indeks Harga Saham Gabungan (IHSG) merupakan indikator utama kondisi pasar modal Indonesia yang memiliki pergerakan fluktuatif sehingga diperlukan metode peramalan yang mampu menghasilkan prediksi secara akurat. Penelitian ini bertujuan mengembangkan model peramalan harga penutupan IHSG menggunakan Long Short-Term Memory (LSTM) dengan optimizer Adam serta menentukan kombinasi hyperparameter terbaik menggunakan Grid Search. Data yang di…
creator: Nafa Nurhanifah
creator: Sutawanir Darwis
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-07-31
publication date: 2026-07-31
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.24596

Peramalan Pendapatan PT Telkom Indonesia (Persero) Tbk Berdasarkan Data Periode 2016-2024 Menggunakan Metode Double Exponential Smoothing-Holt.
type: item
description: Abstract. Revenue is one of the primary indicators of a company’s financial performance and serves as a basis for strategic business planning. Therefore, an accurate forecasting method is essential to support decision-making. This study aims to forecast the revenue of PT Telkom Indonesia (Persero) Tbk using the Double Exponential Smoothing-Holt method based on quarterly revenue data from 2016 to 2024 obtained from the company’s financial statements. The smoothing parameters for level and trend were determined through a trial and error approach by selecting the combination that produced the lowest Mean Absolute Percentage Error (MAPE). The results showed that the optimal parameters were and , yielding a MAPE value of 2,84%, indicating excellent forecasting accuracy. The forecast for the next four periods showed a stable upward trend in revenue, increasing from IDR 38.989 trillion in period 37 to IDR 39.736 trillion in period 40. These findings indicate that the Double Exponential Smoothing-Holt method is effective for forecasting the revenue of PT Telkom Indonesia (Persero) Tbk and can support financial planning and strategic decision-making. Abstrak. Pendapatan merupakan salah satu indikator utama yang mencerminkan kinerja keuangan perusahaan dan menjadi dasar dalam penyusunan strategi bisnis. Oleh karena itu, diperlukan metode peramalan yang akurat untuk mendukung pengambilan keputusan. Penelitian ini bertujuan meramalkan pendapatan PT Telkom Indonesia (Persero) Tbk menggunakan metode Double Exponential Smoothing-Holt berdasarkan data pendapatan kuartalan periode 2016-2024 yang diperoleh dari laporan keuangan perusahaan. Parameter pemulusan level dan tren ditentukan melalui pendekatan trial and error dengan memilih kombinasi yang menghasilkan nilai Mean Absolute Percentage Error (MAPE) terkecil. Hasil penelitian menunjukkan bahwa parameter optimum diperoleh pada dan dengan nilai MAPE sebesar 2,84%, yang termasuk dalam kategori sangat baik. Hasil peramalan empat per…
creator: Jasmine Devanti Bilbina Heriaty
creator: Fitri Amanah
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-01
publication date: 2026-08-01
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.24628

Pengelompokan Wilayah Pulau Jawa Berdasarkan Kendaraan Bermotor Menggunakan K-Medoids Dengan Euclidean Distance Tahun 2026
type: item
description: Abstract. Java Island recorded the highest number of registered motor vehicles in Indonesia, reaching 103,564,339 units in 2026 across 118 regencies/cities, making it necessary to identify the spatial distribution pattern of vehicle ownership to support well-targeted transportation policy. This study applies the K-Medoids algorithm with Euclidean distance to group regencies/cities in Java Island based on five motor vehicle count variables (motorcycles, passenger cars, trucks, buses, and special vehicles), with the optimal number of clusters determined using the Silhouette Coefficient. K-Medoids was chosen for its robustness to outliers compared to K-Means, since it uses an actual data object (medoid) as the cluster center. Secondary data for 2026 were obtained from the Electronic Registration and Identification (ERI) Dashboard of the Indonesian National Police Traffic Corps and were processed through descriptive analysis, outlier detection (IQR), a multicollinearity test (VIF), Z-score normalization, optimal cluster determination, and K-Medoids clustering using R software. The Silhouette evaluation produced k = 2 as the optimal number of clusters with a score of 0.7656, converging at the third iteration with a total cost of 139.871151 and forming two balanced clusters of 59 regions each. Cluster 1 (high vehicle count) includes DKI Jakarta and major cities, averaging 1,311,029 units per region, while Cluster 2 (low vehicle count) covers semi-urban and rural areas averaging 430,554 units per region. Abstrak. Pulau Jawa merupakan wilayah dengan jumlah kendaraan bermotor tertinggi di Indonesia, mencapai 103.564.339 unit pada tahun 2026 yang tersebar di 118 Kabupaten/Kota, sehingga diperlukan analisis untuk mengidentifikasi pola persebarannya guna mendukung kebijakan transportasi yang tepat sasaran. Penelitian ini menerapkan algoritma K-Medoids dengan Euclidean Distance untuk mengelompokan wilayah di Pulau Jawa berdasarkan lima variabel jumlah kendaraan bermotor (sepeda …
creator: Salma Nur Hamidah
creator: Reny Rian Marliana
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-01
publication date: 2026-08-01
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.24634

Penerapan Metode K-Medoids dengan jarak manhattan untuk Pengelompokan Wilayah Berdasarkan Faktor Risiko DBD di Jawa Barat
type: item
description: Abstract. In 2024, dengue hemorrhagic fever (DHF) cases in West Java Province reached more than 50,000, the highest number in Indonesia. This high figure is influenced by several factors, namely the number of DHF cases, population size, population density, and rainfall, whose characteristics differ across regions so that DHF risk is not evenly distributed across regencies/cities. This research applies the K-Medoids method with Manhattan Distance to group 27 regencies/cities in West Java based on their DHF risk factors, using 2024 secondary data from the West Java Health Profile and the West Java Statistics Agency (BPS). The analysis stages consist of descriptive analysis, outlier detection using the Interquartile Range (IQR), a multicollinearity test using the Variance Inflation Factor (VIF), determination of the optimal number of clusters using the Silhouette Coefficient, and clustering using the K-Medoids algorithm. The results show that the optimal number of clusters is k = 2, with a Silhouette Coefficient of 0.4216. The iteration process converged at the second iteration, with Kuningan and Sukabumi Regencies selected as the final medoids. Cluster 1 (High Risk) consists of 18 regencies/cities with an average of 2,809.56 DHF cases and an average population density of 4,633.06 people/km2, while Cluster 2 (Low Risk) consists of 9 regencies/cities with an average of 1,205.67 DHF cases and an average density of 2,466.67 people/km2. These results indicate that regions with higher population size and density tend to have greater DHF risk, while rainfall shows no consistent relationship with DHF risk in West Java. Abstrak. Demam berdarah dengue (DBD) merupakan salah satu penyakit menular yang masih menjadi permasalahan kesehatan masyarakat di Indonesia, khususnya di Provinsi Jawa Barat yang tercatat sebagai wilayah dengan jumlah kasus tertinggi secara nasional. Pada tahun 2024, jumlah kasus DBD di Jawa Barat mencapai lebih dari 50.000 kasus. Tingginya kasus tersebut dipe…
creator: Tutik Maya Saroh
creator: Reny Rian Marliana
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-01
publication date: 2026-08-01
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.24674

Peramalan Tingkat Pengangguran Terbuka Di Provinsi Jawa Barat Menggunakan Metode DMA Dan DES
type: item
description: Abstract. The Open Unemployment Rate is an important part in describing the employment and economic conditions of a region. West Java Province has shown a decline in recent years, but in 2025 it will be in the top three provinces with the highest unemployment rates in Indonesia. Therefore, forecasting is needed to provide an overview of the condition of the Open Unemployment Rate in the future period as supporting material for planning and evaluating employment policies. This study aims to apply the Double Moving Average (DMA) and Double Exponential Smoothing (DES) methods, determine the best model, and predict the Open Unemployment Rate of West Java Province in 2026–2027. The study uses a quantitative approach with secondary data on the percentage of the Open Unemployment Rate in 2009–2025 obtained from the Central Bureau of Statistics. The analysis was carried out through descriptive statistics, the Augmented Dickey-Fuller (ADF) test, DMA modeling, Brown's DES, and Holt's DES, then model evaluation using the Mean Absolute Percentage Error (MAPE). The results of the ADF test show that the data is non-stationary with a p-value of 0.3596. The best model was obtained from Holt's Double Exponential Smoothing with α = 0.9 and γ = 0.05, resulting in a MAPE of 6.184% and an accuracy of 88.28%, categorized as very good. The forecast results indicate an estimated Open Unemployment Rate of 6.299% in 2026 and 5.872% in 2027. These results indicate a downward trend in the Open Unemployment Rate and can be used as supporting information in employment policy planning in West Jawa. Abstrak. Tingkat Pengangguran Terbuka (TPT) merupakan salah satu bagian penting dalam menggambarkan kondisi ketenagakerjaan dan perekonomian suatu wilayah. Provinsi Jawa Barat dalam beberapa tahun terakhir menunjukkan penurunan namun pada tahun 2025 berada di tiga besar Provinsi dengan angka pengangguran tertinggi di Indonesia, sehingga diperlukan peramalan untuk memberikan gambaran kondisi TPT pada pe…
creator: Erin Heristian
creator: Teti Sofia Yanti
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-01
publication date: 2026-08-01
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.24757

Analisis Segmentasi Mahasiswa Program Studi Statistika Universitas Islam Bandung Tahun 2020-2025 Menggunakan Algoritma K-Modes Clustering
type: item
description: Abstract. Student segmentation is an important approach for understanding student characteristics and supporting higher education institutions in developing more effective and targeted promotional strategies. This study aims to segment students of the Statistics Study Program at Universitas Islam Bandung using the K-Modes Clustering algorithm. The dataset consisted of 376 student records described by four categorical variables, namely city of origin, high school background, admission pathway, and admission wave. The clustering process was performed by evaluating the number of clusters from k= 2 to k = 5. The clustering performance was assessed using the Silhouette Coefficient based on the Simple Matching Dissimilarity measure. The results indicated that the optimal clustering solution was obtained at k = 2, yielding the highest average Silhouette Coefficient value of 0.2301556. Furthermore, each cluster exhibited distinct characteristics in terms of city of origin, high school background, admission pathway, and admission wave. The findings of this study provide useful insights into student characteristics and may assist the Statistics Study Program at Universitas Islam Bandung in developing more effective and targeted promotional strategies. Abstrak. Segmentasi mahasiswa merupakan salah satu pendekatan yang penting untuk memahami karakteristik mahasiswa serta mendukung perguruan tinggi dalam menyusun strategi promosi yang lebih efektif dan tepat sasaran. Penelitian ini bertujuan untuk melakukan segmentasi mahasiswa Program Studi Statistika Universitas Islam Bandung menggunakan algoritma K-Modes Clustering. Data yang digunakan terdiri dari 376 data mahasiswa dengan empat variable kategori, yaitu asal kota, asal sekolah, jalur seleksi, dan gelombang seleksi. Proses clustering dilakukan dengan menguji jumlah clustermulai dari k = 2 hingga k = 5. Kinerja hasil clustering dievaluasi menggunakan Silhouette Coefficient berdasarkan ukuran Simple Matching Dissimilarity. Hasi…
creator: Safira Nur Latifah
creator: Ilham Faishal Mahdy
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-01
publication date: 2026-08-01
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.24868

Pengelompokan Kabupaten/Kota di Pulau Jawa Berdasarkan Indikator Kesejahteraan Rakyat Menggunakan Fuzzy C-Means
type: item
description: Abstract.Regencies and cities in Java exhibit diverse public welfare characteristics, making regional clustering necessary to identify areas with similar public welfare characteristics. This study aims to cluster regencies and cities in Java based on public welfare indicators in 2024 using the Fuzzy C-Means (FCM) method and to evaluate the clustering quality using the Modified Partition Coefficient (MPC). This study uses secondary data covering 119 regencies and cities in Java based on eight public welfare indicators. The analysis stages include Exploratory Data Analysis (EDA), data standardization using the z-score method, clustering using FCM, selecting the best clustering result based on the MPC value, and interpreting the characteristics of each cluster. The results show that the best clustering result consists of two clusters with an MPC value of 0.70169, indicating good clustering quality. The first cluster consists of 38 regencies and cities with a better level of public welfare, while the second cluster consists of 81 regencies and cities with a lower level of public welfare. The clustering results are expected to serve as supporting information for the formulation of development policies tailored to the characteristics of each region. Abstrak.Kabupaten/kota di Pulau Jawa memiliki karakteristik kesejahteraan rakyat yang beragam sehingga diperlukan pengelompokan wilayah untuk mengidentifikasi daerah yang memiliki karakteristik kesejahteraan yang serupa. Penelitian ini bertujuan mengelompokkan kabupaten/kota di Pulau Jawa berdasarkan indikator kesejahteraan rakyat tahun 2024 menggunakan metode Fuzzy C-Means (FCM) serta mengevaluasi kualitas hasil pengelompokan menggunakan Modified Partition Coefficient (MPC). Penelitian ini menggunakan data sekunder yang mencakup 119 kabupaten/kota di Pulau Jawa dengan delapan indikator kesejahteraan rakyat. Tahapan analisis meliputi Exploratory Data Analysis (EDA), standarisasi data menggunakan metode z-score, proses clusteri…
creator: Novianda Dwi Tanti Ramdani
creator: Fauziah Roshafara
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-01
publication date: 2026-08-01
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.24779

Klasifikasi Provinsi di Indonesia Berdasarkan IPM 2025 Menggunakan K-Medoids Clustering
type: item
description: Abstract. The rapid development of information technology and the availability of large-scale data have encouraged the application of analytical methods to better understand social conditions and human development. This study aims to classify the provinces of Indonesia based on the 2025 Human Development Index (HDI) indicators using the K-Medoids Clustering method. Secondary data obtained from Statistics Indonesia (Badan Pusat Statistik) include four variables: Life Expectancy at Birth (LEB), Mean Years of Schooling (MYS), Expected Years of Schooling (EYS), and Adjusted Per Capita Expenditure (APCE). The analysis involved data preprocessing, including missing value detection, outlier identification using the Z-Score method, data standardization. The results showed no missing values or multicollinearity, although several outliers were identified. Based on the highest silhouette coefficient (0.263), three optimal clusters were obtained: a good cluster (5 provinces), a moderate cluster (16 provinces), and a poor cluster (17 provinces). These clustering results can serve as a reference for formulating more targeted regional development policies to improve equity in education, health, and community welfare across Indonesia. Abstrak. Perkembangan teknologi informasi dan ketersediaan data dalam skala besar mendorong penerapan berbagai metode analisis untuk memahami kondisi sosial dan pembangunan manusia. Penelitian ini bertujuan mengelompokkan provinsi di Indonesia berdasarkan indikator Indeks Pembangunan Manusia (IPM) tahun 2025 menggunakan metode K-Medoids Clustering. Data yang digunakan merupakan data sekunder dari Badan Pusat Statistik yang meliputi Usia Harapan Hidup (UHH), Rata-Rata Lama Sekolah (RRLS), Harapan Lama Sekolah (HLS), dan Pengeluaran Per Kapita yang Disesuaikan (PPKD). Tahapan analisis meliputi pemeriksaan missing value, penanganan outlier menggunakan metode Z-Score, standarisasi data. Hasil penelitian menunjukkan tidak terdapat missing value maupun mult…
creator: Zakiyah Romadhoni
creator: Reny Rian Marliana
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-01
publication date: 2026-08-01
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.24814

Analisis Sentimen Sistem Pembayaran BI-FAST Menggunakan Metode Semantic Orientation CALculator (SO-CAL)
type: item
description: Abstract. The development of digital payment systems has encouraged an increase of the number of user of Bank Indonesia Fast Payment (BI-FAST) users as a real-time fund transfer infrastructure that has become the public's top priority in transferring funds. The increasing use of BI-FAST has also encouraged people to share experiences, opinions, and complaints through social media. Thus, it is important to get the tendency or sentiment of the opinion of BI-FAST’s users in order to improve BI-FAST services in the future. In this study, a sentiment analysis was conducted using the Semantic Orientation CALculator (SO-CAL) method to classify user opinions into positive and negative sentiment categories. The SO-CAL method was chosen because it considers the linguistic elements such as negation, intensifiers, and text-level features so such that it can determine the sentiment sentences that have complex structures or meanings. This study used 1.700 user reviews from social media X, collected during the period of January 1 to December 31 2025 and after preprocessing it was obtained 1.087 1,087. The results showed that user opinion was dominated by negative sentiments in 797 posts or 73.3%, with the highest spike in complaints in August. Meanwhile, positive sentiments amounted to 297 posts or 26,7%, with the highest spike in February. Abstrak. Perkembangan sistem pembayaran digital telah mendorong peningkatan jumlah pengguna Bank Indonesia Fast Payment (BI-FAST) sebagai infrastruktur transfer dana secara real-time yang kini menjadi pilihan utama masyarakat dalam melakukan transfer dana. Meningkatnya penggunaan BI-FAST juga mendorong masyarakat untuk berbagi pengalaman, pendapat, dan keluhan melalui media sosial. Oleh karena itu, penting untuk mengetahui tren atau sentimen pendapat pengguna BI-FAST guna meningkatkan layanan BI-FAST di masa depan. Dalam penelitian ini, analisis sentimen dilakukan dengan menggunakan metode Semantic Orientation CALculator (SO-CAL) untuk mengklas…
creator: Risa Rindu Ananda
creator: Suliadi
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-01
publication date: 2026-08-01
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.24815

Peramalan Jumlah Kunjungan Wisatawan Mancanegara di Indonesia Periode Pra-Pandemi COVID-19 Menggunakan Model SARIMA
type: item
description: Abstract. The number of international tourist arrivals is an important indicator in the tourism sector that needs to be forecast to support planning and policy-making. Analyzing pre-COVID-19 data is important because it represents the normal pattern of tourist arrivals without external disturbances and provides a baseline for evaluating the impact of the pandemic and developing intervention models. This study aimed to obtain the best Seasonal Autoregressive Integrated Moving Average (SARIMA) model and forecast international tourist arrivals in Indonesia. A quantitative approach was employed using secondary monthly data from January 2010 to February 2020 obtained from Statistics Indonesia (BPS). The analysis included stationarity testing, model identification using ACF and PACF plots, parameter estimation, significance testing, residual diagnostic checking, model selection based on the Akaike Information Criterion (AIC), and ten-period forecasting. The results showed that SARIMA was the best model because all parameters were significant, the residuals satisfied the white noise and normality assumptions, and the model had the lowest AIC value. The model produced an RMSE of 71,770.53. The forecasts followed the historical seasonal pattern but could not represent actual conditions after the COVID-19 pandemic. Therefore, they provide a baseline for subsequent intervention analysis. Abstrak. Jumlah kunjungan wisatawan mancanegara merupakan salah satu indikator penting dalam sektor pariwisata yang perlu diprediksi untuk mendukung perencanaan dan pengambilan kebijakan. Analisis terhadap data sebelum pandemi COVID-19 penting dilakukan karena dapat menggambarkan pola normal kunjungan wisatawan tanpa adanya gangguan eksternal serta menjadi baseline dalam mengevaluasi dampak pandemi dan mengembangkan model intervensi pada penelitian selanjutnya. Penelitian ini bertujuan memperoleh model Seasonal Autoregressive Integrated Moving Average terbaik serta menghasilkan peramalan jumla…
creator: Tiara Ambarwati Rahman
creator: Reny Rian Marliana
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-02
publication date: 2026-08-02
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.25012

Analisis Korespondensi Berganda pada Korban Kekerasan terhadap Perempuan di Pulau Jawa
type: item
description: Abstract. Multiple Correspondence Analysis (MCA) is an exploratory method for multivariate categorical data used to identify association patterns among categories simultaneously. Classical MCA based on the Burt Matrix enables relationships among categories of several categorical variables to be represented in a low-dimensional space through a correspondence map. This study aims to apply classical MCA based on the Burt Matrix to explore association patterns in data on victims of violence against women in Java in 2025 based on province, place of occurrence, and age group. The data are secondary data obtained from the Ministry of Women’s Empowerment and Child Protection (KemenPPPA). The analysis began with an association test using the chi-square statistic, followed by the construction of an indicator matrix and Burt Matrix, matrix normalization, construction of a standardized residual matrix, and Singular Value Decomposition (SVD). The results indicate an association among the three variables. The SVD decomposition produced ten nonzero singular dimensions, with the first two dimensions accounting for 29,25% of the total inertia. The correspondence map was used to explore proximity patterns among categories and revealed several association tendencies between province, place of occurrence, and age group. The results demonstrate that classical MCA based on the Burt Matrix can be applied to explore association structures in multivariate categorical data. Abstrak. Multiple Correspondence Analysis (MCA) merupakan salah satu metode eksplorasi data kategorik multivariat yang digunakan untuk mengidentifikasi pola asosiasi antar kategori secara simultan. MCA klasik berbasis Matriks Burt memungkinkan hubungan antar kategori dari beberapa variabel kategorik direpresentasikan dalam ruang berdimensi rendah melalui visualisasi peta korespondensi. Penelitian ini bertujuan menerapkan MCA klasik berbasis Matriks Burt untuk mengeksplorasi pola asosiasi pada data korban kekerasan terhada…
creator: Firdha Hanifah Suparlan
creator: Suwanda
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-02
publication date: 2026-08-02
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.24669

Pengelompokan Wilayah Rentan Kebakaran Hutan di Indonesia menggunakan Algoritma I-CLARANS
type: item
description: Abstract. Wildfire was a serious environmental problem in Indonesia, causing ecosystem damage, biodiversity loss, health problems from haze, reduced air quality, and economic losses. Wildfire monitoring widely utilizes satellite-based hotspot data from global monitoring systems. The objective of study is to find some region clusters in Indonesia based on wildfire vulnerability levels using hotspot data including three characteristics which are confidence, brightness temperature, and Fire Radiative Power (FRP). The Improved Clustering Large Applications based on Randomized Search (I-CLARANS) method was demonstrated to fo find the region clusters. The advantage of method was on initial medoid selection quality improvements, clustering efficiency on large datasets, and robustness againts outliers. The searching of cluster optimal number was tried by 2, 3, 4, 5 and 6 clusters by using silhouette coefficient criteria. The results show that 2 clusters was an optimal number of cluster with a maximum silhouette coefficient which is 0.5249, indicating good cluster structure. The first cluster consists of 2,411 hotspots with relatively low brightness, confidence, and FRP values. This cluster was labeled as low to moderate wildfire vulnerability. The second cluster consists of 742 hotspots with higher values, labeled as areas with high wildfire vulnerability. Abstrak. Kebakaran hutan dan lahan (karhutla) merupakan permasalahan lingkungan serius di Indonesia yang berdampak pada kerusakan ekosistem, hilangnya keanekaragaman hayati, gangguan kesehatan akibat kabut asap, penurunan kualitas udara, dan kerugian ekonomi. Pemantauan karhutla banyak memanfaatkan data titik panas (hotspot) berbasis satelit dari sistem pemantauan global. Penelitian ini bertujuan mengelompokkan wilayah di Indonesia berdasarkan tingkat kerentanan karhutla menggunakan data hotspot dengan variabel confidence, brightness temperature, dan Fire Radiative Power (FRP). Metode yang digunakan adalah Improved Cluste…
creator: Nanda Annisa Tsaniya
creator: Abdul Kudus
creator: Iing Lukman
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-02
publication date: 2026-08-02
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.24994

K-Medoids Clustering untuk Pengelompokan Faktor Perceraian di Jawa Barat Tahun 2023
type: item
description: Abstract. Divorce rates in West Java, Indonesia, continue to rise annually, driven primarily by economic issues and disputes. To support targeted policy solutions, this study categorizes districts and cities in West Java based on 2023 divorce data using the K-Medoids Clustering algorithm. The clustering process begins with collecting and pre-processing divorce data, addressing missing values, outliers, and standardizing the variables. The Kaiser-Meyer-Olkin (KMO) test was conducted to verify sample adequacy and ensure the absence of multicollinearity. The optimal cluster number was evaluated using the Silhouette Coefficient, after which the K-Medoids algorithm was applied to group regions according to their divorce profiles. Results were visualized through scatter plots and thematic maps. The findings categorize West Java regions into three distinct clusters: stable areas with low divorce rates, high-divorce regions driven predominantly by economic hardship, and high-divorce regions primarily caused by household disputes. These actionable insights provide a data-driven foundation for local governments to design targeted intervention programs, such as financial management education and conflict mediation services. Keywords: K-Medoids Clustering, Divorce Rate, West Java. Abstrak. Tingkat perceraian di Jawa Barat, Indonesia, terus meningkat setiap tahunnya yang utamanya dipicu oleh faktor masalah ekonomi dan perselisihan. Untuk mendukung solusi kebijakan yang tepat sasaran, penelitian ini mengelompokkan kabupaten dan kota di Jawa Barat berdasarkan data perceraian tahun 2023 menggunakan algoritma K-Medoids Clustering. Proses pengelompokan dimulai dengan pengumpulan dan pra-pemrosesan data perceraian, yang meliputi penanganan data hilang (missing values), pencilan, dan standarisasi variabel. Uji Kaiser-Meyer-Olkin (KMO) dilakukan untuk memverifikasi kecukupan sampel dan memastikan tidak terdapat multikolinearitas. Jumlah cluster optimal dievaluasi menggunakan Silhouette …
creator: Amelia Ayu Izzaty
creator: Sutawanir Darwis
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-02
publication date: 2026-08-02
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.25188

Klasifikasi Kabupaten/Kota di Jawa Barat Berdasarkan Tingkat Kemiskinan Menggunakan K-Means Clustering
type: item
description: Abstract. Poverty remains a persistent socio-economic problem in Indonesia, and its distribution varies considerably across regions. This study aims to classify 27 districts/cities in West Java Province based on their poverty characteristics using the K-Means clustering algorithm. Secondary data were obtained from the Central Statistics Agency (BPS), consisting of four indicators: the percentage of poor population, the poverty line, the poverty gap index (P1), and the poverty severity index (P2). Prior to clustering, the data were standardized using Z-score transformation to equalize the scale of variables, and the optimal number of clusters was determined using the silhouette method. The analysis produced two clusters with a silhouette score of 0.421, indicating a reasonable cluster structure. Cluster 1 consists of 15 regions, mostly regencies, characterized by a high percentage of poor population and deeper, more severe poverty. Cluster 2 consists of 12 regions, mostly cities, characterized by a lower percentage of poor population despite a higher poverty line. These findings can serve as a basis for local governments in designing region-specific poverty alleviation policies. Abstrak. Kemiskinan masih menjadi permasalahan sosial-ekonomi yang persisten di Indonesia, dengan tingkat penyebaran yang bervariasi antarwilayah. Penelitian ini bertujuan untuk mengelompokkan 27 kabupaten/kota di Provinsi Jawa Barat berdasarkan karakteristik kemiskinannya menggunakan algoritma K-Means. Data sekunder diperoleh dari Badan Pusat Statistik (BPS), terdiri atas empat indikator, yaitu persentase penduduk miskin, garis kemiskinan, indeks kedalaman kemiskinan (P1), dan indeks keparahan kemiskinan (P2). Sebelum dilakukan clustering, data terlebih dahulu distandarisasi menggunakan transformasi Z-score untuk menyetarakan skala antarvariabel, dan jumlah klaster optimal ditentukan menggunakan metode silhouette. Hasil analisis menghasilkan dua klaster dengan nilai silhouette sebesar 0,421,…
creator: Rida Ashari
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-02
publication date: 2026-08-02
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.24820

Analisis Korelasi Valid Day, Duration, dan Diamond Dalam Live Streaming TikTok
type: item
description: Abstract. TikTok is a social networking application used as an entertainment platform that offers various features, one of which is live streaming, where creators can interact directly with viewers in real time. This study aims to analyze the relationship among the variables valid day, duration, and diamond in TikTok live streaming. Valid day refers to the number of verified days within a month during which a creator conducts live streaming. Duration refers to the total amount of time accumulated over one month while a creator performs live streaming. Diamond refers to the accumulation of virtual gifts received by creators during live streaming. The data used in this study are secondary data which were obtained from one of the agency's worksheets under PT Media Generasi Group for June 2025. The method employed in this study is the Spearman Rank correlation, a nonparametric statistical method used to determine the direction and strength of the relationship between variables when the data are not normally distributed. The results indicate significant relationships between valid day and diamond, duration and diamond, as well as valid day and duration. Therefore, it can be concluded that these three variables are positively correlated and serve as supporting factors in increasing the number of diamonds earned by creators during live streaming. Abstrak. TikTok merupakan aplikasi jejaring sosial yang digunakan sebagai media hiburan yang memiliki beberapa fitur salah satunya live streaming, dimana kreator dapat berinteraksi secara langsung dengan penonton dalam waktu nyata. Penelitian ini bertujuan untuk menganalisis hubungan antara variabel valid day, duration, dan diamond dalam live streaming TikTok. Valid day merupakan jumlah hari yang tervalidasi selama satu bulan apabila kreator melakukan live streaming. Duration merupakan akumulasi waktu yang tercapai selama satu bulan ketika kreator melakukan live streaming. Diamond merupakan akumulasi dari hadiah virtual yang didap…
creator: Nadya Ayu Aprilia
creator: Fitri Amanah
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-02
publication date: 2026-08-02
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.25072

Pengelompokan Kabupaten/Kota di Jawa Barat Berdasarkan Akses Air Minum dan Sanitasi Layak Menggunakan Metode Ward
type: item
description: Abstract. Access to adequate drinking water and sanitation remains uneven across regions in West Java Province. This study aims to cluster the 27 regencies/cities in West Java Province based on the percentage of households with access to adequate drinking water sources and adequate sanitation in 2024 using Agglomerative Clustering with Ward’s Method. The data used were obtained from Statistics Indonesia (BPS). The analysis stages included outlier detection using the Interquartile Range (IQR), multicollinearity testing using the Variance Inflation Factor (VIF), Euclidean distance calculation, and determination of the optimal number of clusters based on the Silhouette Index (SI) and Calinski–Harabasz Index (CHI). The results showed that there were no outliers or multicollinearity in the two variables. Evaluation of cluster numbers from 2 to 5 indicated that three clusters were the optimal configuration, with an SI value of 0.55 and a CHI value of 84.87. The clustering results using Ward’s Method formed Cluster 0, consisting of 13 regencies/cities with high and relatively even access to adequate drinking water and sanitation; Cluster 1, consisting of 7 regencies/cities with good access to adequate drinking water but moderate access to adequate sanitation; and Cluster 2, consisting of 7 regencies/cities with the lowest access to adequate drinking water and sanitation. These findings can serve as a basis for formulating more targeted policies for drinking water and sanitation infrastructure development in West Java Province. Keywords: Ward’s Method, adequate drinking water, adequate sanitation. Abstrak. Akses terhadap air minum dan sanitasi layak masih menunjukkan ketimpangan antarwilayah di Provinsi Jawa Barat. Penelitian ini bertujuan mengelompokkan 27 kabupaten/kota di Provinsi Jawa Barat berdasarkan persentase rumah tangga dengan akses sumber air minum layak dan sanitasi layak tahun 2024 menggunakan Agglomerative Clustering dengan Ward’s Method. Data yang digunakan b…
creator: Zelfa Syta Najjiny
creator: Aceng Komarudin Mutaqin
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-02
publication date: 2026-08-02
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.25291

Analisis Value at Risk Dengan Metode Historical, Variance-Covariance, dan Monte Carlo pada Saham Sektor Konsumsi Primer
type: item
description: Penelitian ini bertujuan mengestimasi kerugian maksimum pada tiga saham defensif sektor konsumsi primer, yaitu PT Indofood Sukses Makmur Tbk (INDF), PT Indofood CBP Sukses Makmur Tbk (ICBP), dan PT Mayora Indah Tbk (MYOR) untuk memitigasi risiko investasi. Data yang digunakan adalah sekunder berupa harga penutupan mingguan periode 3 Januari 2025 hingga 10 April 2026. Pengukuran risiko dihitung menggunakan metode Value at Risk (VaR) dengan pendekatan Historical, Variance-Covariance, dan Monte Carlo. Spesifikasi risiko ditetapkan pada tingkat kepercayaan 95% dengan asumsi penempatan modal awal Rp10.000.000, di mana metode Monte Carlo dibangkitkan melalui 10.000 iterasi. Uji Kolmogorov-Smirnov diaplikasikan untuk memvalidasi kelayakan distribusi normal dari data return. Hasil penelitian menunjukkan variasi estimasi kerugian maksimum dari ketiga metode yang digunakan. Pada metode Historical, nilai VaR pada saham INDF sebesar -5,79% (Rp579.193), ICBP sebesar -5,78% (Rp578.218) dan MYOR sebesar -7,42% (Rp742.092). Pada metode Variance-Covariance, nilai VaR pada saham INDF sebesar -5,29% (Rp529.397), ICBP sebesar -5,68% (Rp568.235) dan MYOR sebesar -7,89% (Rp788.577). Sementara itu, estimasi melalui metode Monte Carlo nilai VaR pada saham INDF sebesar -5,25% (Rp524.740), ICBP sebesar -6,10% (Rp609.990), dan MYOR sebesar -8,12% (Rp812.188). Estimasi VaR komparatif ini dapat menjadi landasan bagi investor dalam membatasi toleransi kerugian secara terukur.
creator: Nur Sugih Aji
creator: Fitri Amanah
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-02
publication date: 2026-08-02
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.25338

Pendugaan Sebaran Nilai Dissolved Oxygen (DO) dengan Metode Inverse Distance Weighting (IDW) pada Sungai di DKI Jakarta Tahun 2023
type: item
description: Abstract. River water quality is a crucial indicator for maintaining the balance of aquatic ecosystems, particularly in densely populated areas such as DKI Jakarta. One of the key parameters used to assess water quality is Dissolved Oxygen (DO), which reflects the ability of a water body to support aquatic organisms. This study aimed to estimate the spatial distribution of DO values at unmeasured river locations in DKI Jakarta using the Inverse Distance Weighting (IDW) interpolation method. The dataset consisted of 120 measured DO observation points and 12 unmeasured locations where DO values were estimated. The optimal power parameter was determined using Leave-One-Out Cross-Validation (LOOCV) by testing power values ranging from 0.5 to 10 with an interval of 0.5. The results indicated that the optimal power parameter was p = 2, yielding a Root Mean Square Error (RMSE) of 2.993590 mg/L. Using this optimal parameter, the interpolated DO values at the 12 unmeasured locations ranged from 8.855522 to 13.803675 mg/L. The resulting interpolation map showed that the spatial distribution of DO values in the rivers of DKI Jakarta generally followed the pattern of neighboring observation points, consistent with the underlying principle of the IDW interpolation method. Abstrak. Kualitas air sungai merupakan salah satu indikator penting dalam menjaga keseimbangan ekosistem perairan, terutama di wilayah padat penduduk seperti DKI Jakarta. Salah satu parameter yang digunakan untuk menilai kualitas air adalah Dissolved Oxygen (DO), yang menunjukkan kemampuan perairan dalam mendukung kehidupan organisme akuatik. Penelitian ini bertujuan untuk menduga sebaran nilai DO pada titik-titik sungai di DKI Jakarta yang belum dilakukan pengukuran menggunakan metode Inverse Distance Weighting (IDW). Data yang digunakan terdiri dari 120 titik data DO terukur dan 12 titik yang akan diduga nilainya, tersebar pada jaringan Sungai Ciliwung, Angke Pesanggrahan, dan Cakung. Penentuan parameter powe…
creator: Shinta Dewi Widyaningrum
creator: Dwi Agustin Nuriani Sirodj
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-03
publication date: 2026-08-03
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.25347

Pemodelan Kemiskinan di Provinsi Jawa Barat dengan Geographically Weighted Regression (GWR)
type: item
description: Abstract. Poverty in West Java Province in March 2024 was recorded at 7.46 percent, or 3.89 million people, with patterns that vary across regions due to spatial effects. This condition indicates that classical regression, which assumes constant parameters across all regions, is less appropriate to use, making it necessary to apply Geographically Weighted Regression (GWR), a method capable of accommodating local variations in relationships between regions. This study aims to model the number of poor population across 27 regencies/cities in West Java Province in 2024 using GWR, with independent variables including the Gini ratio, GRDP growth rate, per capita expenditure, the Community Literacy Development Index (IPLM), minimum wage, and the Labor Force Participation Rate (TPAK). The spatial weighting used an adaptive bisquare kernel function, with the optimum bandwidth determined through the Cross Validation method, and data processing was carried out using R Studio. The model fit test results show an F-value (2.3704) greater than the F-table value (2.235) with a p-value of 0.03764, indicating that GWR is more appropriate than OLS. The GWR model produced different equations for each region, with an AICc value of 33.7708, and significant variables that varied across regions, grouped into six clusters of regencies/cities. Abstrak. Kemiskinan di Provinsi Jawa Barat pada Maret 2024 tercatat sebesar 7,46 persen atau 3,89 juta jiwa, dengan pola yang bervariasi antarwilayah akibat adanya pengaruh spasial. Kondisi ini menunjukkan bahwa regresi klasik yang mengasumsikan parameter konstan di seluruh wilayah kurang tepat digunakan, sehingga diperlukan metode Geographically Weighted Regression (GWR) yang mampu mengakomodasi variasi hubungan antarwilayah secara lokal. Penelitian ini bertujuan memodelkan jumlah penduduk miskin di 27 kabupaten/kota Provinsi Jawa Barat tahun 2024 menggunakan GWR, dengan variabel independen meliputi rasio gini, laju pertumbuhan PDRB, pengeluaran per …
creator: Kurnia Ardi Ferdianto
creator: Fauziah Roshafara
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-03
publication date: 2026-08-03
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.25414

Peningkatan Automatic Speech Recognition Dialek Bugis-Makassar Menggunakan Data Ucapan Sintetis
type: item
description: Abstract. Speech in the Bugis-Makassar dialect poses a major challenge for Automatic Speech Recognition (ASR) systems, particularly in detecting clitic particles that are essential to utterance meaning but do not exist in standard Indonesian. This study investigates the effect of synthetic speech data augmentation using OpenAI's gpt-4o-mini-tts on ASR performance for this dialect, testing four ratios of synthetic-to-real data (0%, 50%, 100%, and 150%) through fine-tuning of the wav2vec2-large-xlsr-indonesian model. Evaluation was conducted using Word Error Rate (WER), Character Error Rate (CER), and Particle Detection Rate (PDR) as a dedicated metric for clitic particle detection. Results show that augmentation at a 50% ratio produced consistent improvements across all metrics compared to baseline, with WER decreasing by 3.47 percentage points and PDR increasing by 3.62 percentage points. Further increases in ratio progressively reduced these gains, forming an inverted-U pattern, with performance at a 150% ratio returning to near-baseline levels. Acoustic analysis identified a significant pitch distribution gap between synthetic and real speech (a difference of 58.09 Hz, or 24.8%) as a contributing factor to performance degradation at higher ratios. These findings indicate that TTS-based synthetic data augmentation effectively supports low-resource dialectal ASR, but only when applied at a controlled, moderate ratio. Abstrak. Tuturan dialek Bugis-Makassar merupakan tantangan besar bagi sistem Automatic Speech Recognition (ASR), khususnya dalam mendeteksi partikel klitik yang penting bagi makna ujaran namun tidak terdapat dalam Bahasa Indonesia standar. Penelitian ini menginvestigasi pengaruh augmentasi data sintetis menggunakan OpenAI gpt-4o-mini-tts terhadap performa ASR dialek tersebut, dengan menguji empat rasio data sintetis terhadap data asli (0%, 50%, 100%, dan 150%) melalui fine-tuning model wav2vec2-large-xlsr- indonesian. Evaluasi dilakukan menggunakan Word…
creator: Muh Fatwah Fajriansyah M
creator: Herdianti Darwis
creator: Huzain Azis
creator: Abdul Rachman Manga
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-03
publication date: 2026-08-03
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.25398

Perbandingan Model VAR Tanpa dan Dengan Seasonal Dummy untuk Peramalan Inflasi
type: item
description: Abstract. Amid persistent global economic uncertainty and elevated inflationary pressure in 2026, accurate inflation forecasting has become essential for effective monetary policy. This study forecasts monthly (Month-over-Month) inflation in Indonesia, South Korea, and Thailand for the period January 1986–June 2026 using a Vector Autoregression (VAR) model, comparing specifications without and with the addition of seasonal dummies. Optimal lag selection based on the Akaike Information Criterion (AIC) produced a VAR(13) model. Diagnostic tests show that the model without seasonal dummies still exhibits residual autocorrelation, while the model with seasonal dummies is free of autocorrelation and shows a reduced ARCH effect, particularly for South Korea. Both models are confirmed stable based on inverse roots of the AR characteristic polynomial. Granger causality tests reveal that only the relationship from Thailand to South Korea remains consistently significant across both models, while four other causal directions change in significance after seasonal dummies are added, indicating that some previously identified relationships were influenced by shared seasonal patterns rather than genuine economic causality. Twelve-month-ahead forecasting results (July 2026–June 2027) show that the VAR(13) model with seasonal dummies consistently produces lower MAE and RMSE for all three countries, making it a more reliable model for monthly inflation forecasting. Abstrak. Di tengah ketidakpastian ekonomi global yang masih berlanjut dan tekanan inflasi yang meningkat pada 2026, kemampuan meramalkan inflasi secara akurat menjadi hal penting bagi efektivitas kebijakan moneter. Penelitian ini bertujuan meramalkan inflasi bulanan (Month-over-Month) Indonesia, Korea Selatan, dan Thailand periode Januari 1986–Juni 2026 menggunakan model Vector Autoregression (VAR), dengan membandingkan model tanpa dan dengan penambahan seasonal dummy. Penentuan lag optimal berdasarkan Akaike Information …
creator: Muhammad Wildan Ramadhan
creator: Reny Rian Marliana
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-03
publication date: 2026-08-03
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.25442

Penerapan Metode K-Medoids Clustering pada Kasus Stunting di Kota Bandung Tahun 2024
type: item
description: Abstract. Stunting is a chronic nutritional problem that directly affects the quality of human resources, and sub-district (kelurahan) level analysis is needed to identify at-risk areas in greater detail. This study aims to group 151 kelurahan in Bandung City in 2024 based on three stunting indicators, namely exclusive breastfeeding, immunization, and sanitation, using K-Medoids Clustering, a method that is robust to outliers. Prior to clustering, an outlier check was performed using the Interquartile Range method and a non-multicollinearity test using the Variance Inflation Factor (VIF), which showed all VIF values below 10. The optimal number of clusters was determined based on the highest Silhouette Coefficient, resulting in two clusters. The results show that Cluster 1 consists of 112 kelurahan with average exclusive breastfeeding of 82.31%, immunization of 96.33%, and sanitation of 81.56%, categorized as Good. Cluster 2 consists of 39 kelurahan with average exclusive breastfeeding of 82.29%, immunization of 63.95%, and sanitation of 82.21%, categorized as Poor. A Silhouette Coefficient of 0.4767 indicates a fairly good cluster structure. Immunization coverage is the most distinguishing indicator between the two clusters, so kelurahan in Cluster 2 should be prioritized for immunization program interventions to reduce stunting risk in Bandung City. Keywords: Stunting, K-Medoids Clustering, Sub-district. Abstrak. Stunting merupakan permasalahan gizi kronis yang berdampak langsung pada kualitas sumber daya manusia, dan analisis pada level kelurahan diperlukan untuk mengidentifikasi wilayah berisiko secara lebih rinci. Penelitian ini bertujuan mengelompokkan 151 kelurahan di Kota Bandung tahun 2024 berdasarkan tiga indikator stunting, yaitu ASI eksklusif, imunisasi, dan sanitasi, menggunakan metode K-Medoids Clustering yang robust terhadap outlier. Sebelum clustering, dilakukan pemeriksaan outlier dengan metode Interquartile Range dan uji non-multikolinearitas mengg…
creator: Ayu Sulastri
creator: Marisza Herlina
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-03
publication date: 2026-08-03
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.25517

Reduksi Noise Data Curah Hujan di Kota Bandung Menggunakan Variational Mode Decomposition (VMD)
type: item
description: Abstract. Daily rainfall data in Bandung City exhibit nonlinear and nonstationary characteristics caused by seasonal patterns and extreme fluctuations, making accurate forecasting a challenging task. To address this issue, this study applies Variational Mode Decomposition (VMD) as a preprocessing technique to reduce data complexity and noise before forecasting. VMD decomposes the original rainfall signal into several Intrinsic Mode Functions (IMFs) through an adaptive variational optimization process, enabling a more stable decomposition while overcoming the mode-mixing problem commonly found in Empirical Mode Decomposition (EMD). The optimal number of decomposition modes (K) was determined using three quantitative evaluation criteria proposed by Wu et al. (2020), namely energy entropy, minimum gap center frequency, and minimum energy proportion, with K values ranging from 2 to 6. The results indicate that K = 2 is the optimal choice, producing the lowest energy entropy (0.2480), the largest minimum gap center frequency (0.16486), and no redundant IMF, as indicated by the minimum energy proportion of 6.78%, which exceeds the 1% threshold. The decomposition separates the rainfall signal into IMF1, which captures the dominant long-term trend, and IMF2, which represents high-frequency fluctuations associated with noise. These findings demonstrate that VMD effectively simplifies complex rainfall data into more homogeneous signal components, providing a more suitable representation for subsequent forecasting models. Keywords: Variational Mode Decomposition, rainfall, signal decomposition Abstrak. Data curah hujan harian di Kota Bandung memiliki karakteristik nonlinier dan non-stasioner akibat pengaruh variasi musiman serta fluktuasi yang tinggi, sehingga proses prediksi menjadi lebih kompleks. Untuk mengatasi permasalahan tersebut, penelitian ini menerapkan Variational Mode Decomposition (VMD) sebagai tahap preprocessing untuk mereduksi kompleksitas dan noise sebelum pro…
creator: Riska Juniarti Anugrah
creator: Reny Rian Marliana
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-03
publication date: 2026-08-03
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.25669

Pengelompokan Provinsi Berdasarkan Karakteristik Pendidikan Pemuda Menggunakan K-Medoids
type: item
description: Abstract. Youth are valuable assets in achieving the vision of Golden Indonesia 2045. Differences in youth characteristics across provinces, particularly in educational aspects, indicate the need to classify regions in order to identify patterns and similarities among provinces. This study aims to classify provinces in Indonesia based on the educational characteristics of youth using the K-Medoids clustering method. The study employed secondary data obtained from the Indonesian Youth Statistics publication issued by Statistics Indonesia (BPS). The unit of analysis consisted of 38 provinces, while the variables included literacy rate, mean years of schooling, percentage of youth completing senior high school/vocational high school, and internet access. The analytical procedures comprised data standardization, dimensionality reduction using Principal Component Analysis (PCA), determination of the optimal number of clusters using the Silhouette method, and clustering using the K-Medoids algorithm. The results showed that the optimal number of clusters was two, with an average silhouette value of 0.8388, indicating a very good clustering quality. The first cluster consisted of 36 provinces characterized by relatively higher educational attainment and internet access, whereas the second cluster consisted of Central Papua and Highland Papua, which exhibited relatively lower educational attainment and internet access. These findings are expected to provide useful information for policymakers in formulating more targeted youth development policies to support the realization of Golden Indonesia 2045. Abstrak. Pemuda merupakan aset penting dalam mewujudkan visi Indonesia Emas 2045. Perbedaan karakteristik pemuda di setiap wilayah Indonesia, khususnya pada aspek pendidikan, menunjukkan perlunya pengelompokan wilayah untuk mengetahui pola kesamaan antarprovinsi. Penelitian ini bertujuan mengelompokkan provinsi di Indonesia berdasarkan karakteristik pendidikan pemuda menggunakan…
creator: Ramadhanni Firman
creator: Ilham Faishal Mahdy
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-03
publication date: 2026-08-03
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.25707

Pemodelan Harga Penutupan Saham Bank Jago Menggunakan Metode ARIMA Box-Jenkins
type: item
description: Abstract. Time series analysis has been widely used to model data observed over time in various fields, particularly economics and finance. One of the most commonly applied time series methods has been the Autoregressive Integrated Moving Average (ARIMA) model, which is capable of representing the characteristics of time series data through the Box–Jenkins approach. This study used the ARIMA method to model the closing stock prices of Bank Jago from January 2021 to July 2025. The Box–Jenkins procedure consisted of model identification, parameter estimation, and diagnostic checking to obtain the most appropriate model. The results show that the best model is ARIMA(1,1,1) with an Akaike Information Criterion (AIC) value of 15598,52. The diagnostic tests indicate that the residuals satisfy the white noise assumption, suggesting that the model adequately represents the characteristics of the data. Therefore, ARIMA(1,1,1) is the most appropriate model for the closing stock prices of Bank Jago during the study period. Abstrak. Analisis deret waktu merupakan salah satu metode statistik yang digunakan untuk menganalisis data yang diamati secara berurutan berdasarkan waktu. Analisis ini bertujuan untuk mengidentifikasi karakteristik serta pola yang terkandung dalam data sehingga dapat dibentuk suatu model yang mampu merepresentasikan data dari waktu ke waktu. Salah satu metode yang umum digunakan dalam pemodelan deret waktu adalah Autoregressive Integrated Moving Average (ARIMA), yaitu model yang menggabungkan komponen Autoregressive (AR), Integrated (I), dan Moving Average (MA) untuk memodelkan data deret waktu yang telah memenuhi kondisi stasioner melalui proses diferensiasi (differencing). Penelitian ini bertujuan untuk memodelkan data harga penutupan saham menggunakan metode ARIMA melalui pendekatan Box-Jenkins, yang meliputi tahap identifikasi model, estimasi parameter, serta pengujian diagnostik terhadap residual. Data yang digunakan dalam penelitian ini adalah harga p…
creator: Refina Mutia
creator: Nur Azizah Komara Rifai
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-03
publication date: 2026-08-03
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.25721

Peramalan Harga Emas Menggunakan Geometric Brownian Motion
type: item
description: Abstract. Gold is one of the most popular investment instruments because its price fluctuates due to various economic factors. Therefore, a method capable of modeling stochastic price movements is needed. Geometric Brownian Motion (GBM) has been widely used to model financial asset prices because it incorporates both deterministic and stochastic components. This study aims to determine the accuracy of the GBM model in forecasting gold prices based on the Mean Absolute Percentage Error (MAPE) and to forecast gold prices for the next five periods. Daily gold price data from 1 January 2024 to 31 December 2025, obtained from Investing.com, were used in this study. The data were divided into 415 training observations and 104 testing observations. Model parameters were calculated based on return values, and Monte Carlo simulations were performed using 100, 500, and 1000 iterations. Model accuracy was evaluated using MAPE, and the model with the smallest MAPE value was selected for forecasting. The results showed that the Monte Carlo simulation with 500 iterations produced the best model with a MAPE value of 7.8838%. Based on this model, gold prices were projected to increase from 4,321.53 USD/oz in the first period to 4,344.99 USD/oz in the fifth period. These findings indicate that the GBM model provides good forecasting accuracy and is a suitable alternative for short-term gold price forecasting. Abstrak. Emas merupakan salah satu instrumen investasi yang nilainya berfluktuasi akibat berbagai faktor ekonomi, sehingga diperlukan metode yang mampu memodelkan pergerakan harga emas secara stokastik. Penelitian ini bertujuan untuk mengetahui tingkat akurasi model Geometric Brownian Motion (GBM) dalam melakukan peramalan harga emas berdasarkan nilai Mean Absolute Percentage Error (MAPE) serta memperoleh hasil peramalan harga emas untuk lima periode ke depan. Data yang digunakan berupa harga emas harian periode 1 Januari 2024–31 Desember 2025 yang diperoleh dari Investing.com.…
creator: Rifa Fadhila
creator: Fauziah Roshafara
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-03
publication date: 2026-08-03
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.25726

Clustering Kualitas Udara DKI Jakarta dengan Algoritma K-Medoids Berdasarkan Indeks Standar Pencemar Udara
type: item
description: Abstract. Air quality in DKI Jakarta exhibits considerable variation in pollutant concentrations, making clustering methods necessary to identify air quality patterns based on pollutant characteristics. This study aims to cluster air quality conditions in DKI Jakarta using the K-Medoids algorithm based on Air Pollution Standard Index. Daily ISPU data from January 2024 to November 2025 were obtained from the Satu Data Jakarta platform, covering five air quality monitoring stations and six pollutant parameters: PM₁₀, PM₂.₅, SO₂, CO, O₃, and NO₂. After data preprocessing, 3,090 observations were retained for analysis. Data preprocessing included missing value inspection, duplicate removal, outlier detection, and Z-score standardization. The optimal number of clusters was determined using the Silhouette Coefficient, followed by clustering with the K-Medoids algorithm. The results indicate that the optimal configuration consists of three clusters with a Silhouette Coefficient of 0.1944. The clusters exhibit distinct pollutant characteristics and different air quality patterns across monitoring stations. These findings provide an overview of air quality patterns and may support air quality monitoring and pollution control strategies in DKI Jakarta. Keywords: K-Medoids, Air Pollution Standard Index, air quality. Abstrak. Kualitas udara di DKI Jakarta menunjukkan variasi konsentrasi parameter pencemar yang tinggi, sehingga diperlukan metode pengelompokan untuk mengidentifikasi pola kualitas udara berdasarkan karakteristik pencemar. Penelitian ini bertujuan mengelompokkan kondisi kualitas udara di DKI Jakarta berdasarkan Indeks Standar Pencemar Udara (ISPU) menggunakan algoritma K-Medoids. Data yang digunakan berupa data harian ISPU periode Januari 2024 hingga November 2025 yang diperoleh dari platform Satu Data Jakarta, mencakup lima stasiun pemantauan kualitas udara dengan enam parameter pencemar, yaitu PM₁₀, PM₂.₅, SO₂, CO, O₃, dan NO₂. Setelah melalui tahap data preproce…
creator: Rahma Elia Nurazizah
creator: Nusar Hajarisman
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-03
publication date: 2026-08-03
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.25833

Pengelompokan Tingkat Kesejahteraan Kabupaten/Kota di Jawa Barat Tahun 2024 Menggunakan Metode K-Medoids
type: item
description: Abstract. Disparities in regional welfare across West Java Province require the classification of regions based on their socioeconomic characteristics to support more targeted development policies. This study aims to classify the 27 regencies and municipalities in West Java Province using three welfare indicators, namely Life Expectancy (LE), Mean Years of Schooling (MYS), and the Poverty Rate, through the K-Medoids clustering method. Prior to the clustering process, the data were standardized using the Z-score method, and a multicollinearity test was conducted to ensure that the variables were suitable for analysis. The optimal number of clusters was initially determined using the Silhouette Coefficient, which indicated the highest value at k = 2. However, based on substantive considerations of regional characteristics, four clusters were selected to better capture the diversity of welfare conditions across regions. The K-Medoids algorithm converged after nine iterations and produced four clusters with distinct characteristics. Cluster 1 represents regions with a moderate level of welfare, Cluster 2 consists of regions with relatively good welfare conditions, Cluster 3 comprises regions with comparatively lower welfare, characterized by lower life expectancy, lower mean years of schooling, and higher poverty rates, while Cluster 4 includes major urban areas with the highest level of welfare. The findings reveal substantial disparities in welfare among regions in West Java. Therefore, the resulting regional classification can serve as a valuable reference for policymakers in designing more effective and region-specific development strategies. Abstrak. Ketimpangan tingkat kesejahteraan antarwilayah di Provinsi Jawa Barat memerlukan pengelompokan wilayah berdasarkan karakteristik sosial ekonomi sebagai dasar penyusunan kebijakan pembangunan yang lebih tepat sasaran. Penelitian ini bertujuan mengelompokkan 27 kabupaten/kota di Provinsi Jawa Barat berdasarkan tiga indik…
creator: Keysha Jauzi Althaff
creator: Reny Rian Marliana
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-03
publication date: 2026-08-03
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.25831

Analisis Deskriptif Skor Pola Pangan Harapan (PPH) Per Kabupaten/Kota di Provinsi Jawa Barat Tahun 2020-2024
type: item
description: Abstract. Kualitas konsumsi pangan dapat dinilai melalui Indeks Pola Pangan Harapan (PPH), yang mencerminkan keragaman dan keseimbangan konsumsi pangan masyarakat. Analisis deskriptif terhadap skor PPH, disertai visualisasi spasial, penting dilakukan untuk memahami kondisi ketahanan pangan di Provinsi Jawa Barat serta mengidentifikasi perbedaan kualitas konsumsi pangan antarwilayah yang dapat menjadi dasar kebijakan pangan yang lebih terarah. Penelitian ini bertujuan untuk mendeskripsikan skor PPH di Provinsi Jawa Barat selama periode 2020–2024 serta memvisualisasikan secara spasial perbedaan skor PPH antar kabupaten/kota pada tahun 2024, menggunakan data sekunder dari Open Data Jabar yang dianalisis dengan metode statistik deskriptif. Hasil analisis menunjukkan bahwa rata-rata skor PPH di Jawa Barat meningkat secara konsisten dari 83,794 poin pada tahun 2020 menjadi 91,283 poin pada tahun 2024, disertai penurunan simpangan baku dan rentang yang mengindikasikan semakin meratanya kualitas konsumsi pangan antarwilayah. Meskipun demikian, hasil visualisasi peta pada tahun 2024 memperlihatkan bahwa sebaran skor PPH antar kabupaten/kota masih belum sepenuhnya merata. Kabupaten Indramayu, Kabupaten Ciamis, dan Kota Banjar memiliki skor PPH tertinggi, sementara Kabupaten Bogor, Kota Bogor, dan Kabupaten Tasikmalaya memiliki skor PPH terendah. Perbedaan tersebut mencerminkan karakteristik yang beragam di setiap wilayah, baik dari segi geografis, tingkat urbanisasi, ketersediaan pangan lokal, daya beli masyarakat, maupun kebiasaan konsumsi. Abstrak. The quality of food consumption can be assessed using the Desired Diet Index (PPH), which reflects the diversity and balance of the population’s food consumption. A descriptive analysis of PPH scores, accompanied by spatial visualization, is essential for understanding food security conditions in West Java Province and identifying regional differences in the quality of food consumption, which can serve as the basis for more targe…
creator: Khalisha Azzahra
creator: Nusar Hajarisman
creator: Dwi Agustin Nuriani Sirodj
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-03
publication date: 2026-08-03
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.26103

Pemodelan Regresi Gamma pada Data Indeks Pembangunan Manusia Kabupaten/Kota di Provinsi Jawa Barat Tahun 2025
type: item
description: Abstract. Gamma Regression is a method of Generalized Linear Model (GLM) for analyzing the relationship between a positive, non-normally distributed response variable and predictor variables. This study aimed to model the factors affecting the Human Development Index (HDI) of regencies/cities in West Java Province 2025. The study used secondary data from BPS of West Java Province, covering 27 regencies/cities. The response variable was HDI, while the predictor included the percentage of poor population, access to improved drinking water, life expectancy, real expenditure per capita, school participation rate for individuals aged 19–23 years, and the open unemployment rate. The analysis included descriptive statistics, Gamma distribution testing, multicollinearity testing, and Gamma Regression using Maximum Likelihood Estimation (MLE) with the Fisher Scoring algorithm. The results showed that the HDI data followed a Gamma distribution and exhibited no multicollinearity. Partially, the percentage of poor population, real expenditure per capita, and the school participation rate for individuals aged 19–23 years significantly affected HDI at the 5% significance level. Simultaneously, the model was significant (likelihood ratio statistic (= 91.456), p < 0.0001). The coefficient of determination was 96.62%, indicating the model explained most of the HDI variation of regencies/cities in West Java Province in 2025. Abstrak. Regresi Gamma merupakan salah satu metode dalam Generalized Linear Model (GLM) yang digunakan untuk menganalisis hubungan antara variabel respon yang bernilai positif dan berdistribusi tidak simetris dengan variabel prediktor. Penelitian ini bertujuan memodelkan faktor-faktor yang memengaruhi data Indeks Pembangunan Manusia (IPM) Kabupaten/Kota di Provinsi Jawa Barat tahun 2025. Penelitian menggunakan data sekunder dari BPS Provinsi Jawa Barat Tahun 2025 sebanyak 27 kabupaten/kota. Variabel respon dalam penelitian ini adalah IPM, sedangkan variabel predi…
creator: Salma Nera Hanifah
creator: Fitri Amanah
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-03
publication date: 2026-08-03
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.26122

Pemodelan Topik Skripsi Statistika Universitas Islam Bandung Tahun 2020–2025 Menggunakan BERTopic KernelPCA–K-Means
type: item
description: Abstract. This study models and examines the development of undergraduate thesis topics in the Statistics Study Program at Universitas Islam Bandung during 2020–2025 using a modular BERTopic architecture with IndoBERT, Kernel Principal Component Analysis (KernelPCA), and K-Means. The initial corpus contained 491 title-and-abstract documents. After data preparation and text preprocessing, 481 complete documents were retained for analysis. IndoBERT converted each document into a 768-dimensional contextual embedding, which was reduced to five components using KernelPCA with a radial basis function kernel. The number of clusters was determined through the Elbow Method, and K-Means produced six clusters with a within-cluster sum of squares of 8.974795. Topic representation using class-based TF-IDF indicated themes related to process and quality control; insurance, claims, and risk; regional and population data; relationship testing and intervariable modelling; time series and forecasting; and regression, parameter estimation, and probability distributions. The overall coherence score was 0.546040. Annual distributions fluctuated across the observation period: Cluster 5 had the largest overall membership, while Clusters 2 and 4 gained relative prominence toward the end of the period. The 2025 pattern must be interpreted cautiously because fewer documents were available. The approach provides an interpretable overview of the thematic development of statistics theses. Abstrak. Penelitian ini memodelkan dan menganalisis perkembangan topik skripsi mahasiswa Program Studi Statistika Universitas Islam Bandung tahun 2020–2025 menggunakan arsitektur BERTopic yang mengintegrasikan IndoBERT, Kernel Principal Component Analysis (KernelPCA), dan K-Means. Korpus awal terdiri atas 491 dokumen judul dan abstrak. Setelah tahap data preparation dan text preprocessing, sebanyak 481 dokumen lengkap dipertahankan untuk analisis. IndoBERT mengubah setiap dokumen menjadi embedding kontekstual …
creator: Muhammad Rivaldi Yusuf
creator: Ilham Faishal Mahdy
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-04
publication date: 2026-08-04
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.26152

Peramalan Harga Saham PT Bank Rakyat Indonesia (Persero) Tbk Menggunakan Model Autoregressive Integrated Moving Average (ARIMA)
type: item
description: Abstract. Stock price is an important indicator reflecting a company's performance and serves as a basis for investment decision-making. The fluctuating movement of stock prices requires a forecasting method capable of producing accurate short-term predictions. This study aims to determine the best Autoregressive Integrated Moving Average (ARIMA) model for forecasting the stock price of PT Bank Rakyat Indonesia (Persero) Tbk (BBRI). The data used consist of daily closing stock prices from January 1, 2025 to August 15, 2025, totaling 143 observations. The analysis stages include stationarity testing using the Box-Cox transformation and the Augmented Dickey-Fuller (ADF) test, model identification through the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF), parameter estimation using Maximum Likelihood Estimation (MLE), model evaluation based on Residual Mean Square (MS) and the Ljung-Box diagnostic test, and forecasting accuracy measurement using Mean Absolute Percentage Error (MAPE). The results indicate that the ARIMA(0,1,2) model is the best forecasting model with the smallest Residual Mean Square (MS) value of 10,132.8 and residuals satisfying the white noise assumption. Forecasting results show that the stock price of PT Bank Rakyat Indonesia (Persero) Tbk tends to remain stable over the next seven periods with excellent forecasting accuracy based on the MAPE value. Keywords: ARIMA, forecasting, stock price, time series, BBRI. Abstrak. Harga saham merupakan salah satu indikator penting yang mencerminkan kinerja perusahaan serta menjadi dasar pengambilan keputusan investasi. Pergerakan harga saham yang bersifat fluktuatif menyebabkan diperlukan metode peramalan yang mampu menghasilkan prediksi jangka pendek secara akurat. Penelitian ini bertujuan untuk menentukan model Autoregressive Integrated Moving Average (ARIMA) terbaik dalam meramalkan harga saham PT Bank Rakyat Indonesia (Persero) Tbk (BBRI). Data yang digunakan merupakan data har…
creator: Muhamad Arifianto Darmawan
creator: Fitri Amanah
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-05
publication date: 2026-08-05
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.26220

Pemodelan Tingkat Kemiskinan di Jawa Timur Menggunakan Regresi Ridge Hoerl–Kennard–Baldwin (HKB)
type: item
description: Poverty remains a multidimensional challenge in East Java, with a rate of 10.35% in 2023 and significant disparities across districts. This study applies Ridge Regression with the Hoerl-Kennard-Baldwin (HKB) estimator to model poverty determinants while addressing multicollinearity. Using secondary data from BPS East Java (38 districts), six predictors were analyzed: Mean Years of Schooling (X₁), Open Unemployment Rate (X₂), GRDP (X₃), Human Development Index (X₄), Literacy Rate (X₅), and Poverty Depth Index (X₆). OLS estimation revealed severe multicollinearity (VIF X₁=33.6, X₄=29.1). The HKB method produced a penalty parameter k=1.134964. The final Ridge model is Ŷ=31.8732−0.1023X₁−0.1655X₂+0.0041X₃−0.2185X₄−0.0918X₅+3.0626X₆. Partial significance tests (effective df=32.46) identified only X₄ and X₆ as significant at α=5%. X₄ negatively affects poverty (−0.2185%), while X₆ has the largest positive impact (3.0626%). Other variables were statistically insignificant. These findings confirm the effectiveness of Ridge-HKB in stabilizing coefficient estimates under multicollinearity. Policy priorities should focus on reducing the Poverty Depth Index and improving Human Development Index. Kemiskinan merupakan tantangan multidimensi di Jawa Timur, dengan angka 10,35% pada tahun 2023 dan disparitas antarkabupaten/kota yang signifikan. Penelitian ini menerapkan Regresi Ridge dengan estimator Hoerl-Kennard-Baldwin (HKB) untuk memodelkan determinan kemiskinan sambil mengatasi multikolinearitas. Menggunakan data sekunder BPS Jawa Timur (38 kabupaten/kota), enam prediktor dianalisis: Rata-rata Lama Sekolah (X₁), Tingkat Pengangguran Terbuka (X₂), PDRB (X₃), Indeks Pembangunan Manusia (X₄), Angka Melek Huruf (X₅), dan Indeks Kedalaman Kemiskinan (X₆). Estimasi MKT menunjukkan multikolinearitas berat (VIF X₁=33,6; X₄=29,1). Metode HKB menghasilkan parameter penalti k=1,134964. Model Ridge akhir adalah Ŷ=31,8732−0,1023X₁−0,1655X₂+0,0041X₃−0,2185X₄−0,0918X₅+3,0626X₆. Uji signifikan…
creator: Herdian Syah
creator: Suliadi Suliadi
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-05
publication date: 2026-08-05
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.26201

Perbandingan Metode Double Exponential Smoothing Holt, Fuzzy Time Series Lee, Dan Fuzzy Time Series Stevenson-Porter Pada Peramalan Harga Penutupan Saham BBRI
type: item
description: Abstract. Forecasting is an approach used to estimate future conditions based on historical data patterns. In investment, stock price forecasting provides important information to support decision-making because stock price movements are dynamic and fluctuating. This study aims to compare the performance of the Double Exponential Smoothing Holt, Fuzzy Time Series Lee and, Fuzzy Time Series Stevenson-Porter methods in forecasting BBRI stock closing prices and determining the method with the best accuracy. The research uses quantitative methods with secondary data in the form of daily BBRI stock closing prices from January 2, 2026, to June 30, 2026, obtained from Yahoo Finance. The data processing involves applying three forecasting methods and evaluating their accuracy using the Mean Absolute Percentage Error (MAPE). The results show that the MAPE values of the Double Exponential Smoothing Holt, Fuzzy Time Series Lee, and Fuzzy Time Series Stevenson-Porter methods are 1.65%, 1.78%, and 0.49%, respectively. The Fuzzy Time Series Stevenson-Porter method achieves the best performance with the smallest MAPE value. The forecasting result for the 117th period using this method is Rp2,713.07 per share. Abstrak. Peramalan merupakan pendekatan untuk memperkirakan kondisi masa mendatang berdasarkan pola data historis. Dalam bidang investasi, peramalan harga saham menjadi informasi penting karena pergerakan harga saham bersifat dinamis dan fluktuatif. Penelitian ini bertujuan untuk membandingkan kinerja metode Double Exponential Smoothing Holt, Fuzzy Time Series Lee, dan Fuzzy Time Series Stevenson-Porter dalam meramalkan harga penutupan saham BBRI serta menentukan metode dengan tingkat akurasi terbaik. Data yang digunakan merupakan data sekunder berupa harga penutupan harian saham BBRI periode 2 Januari 2026 hingga 30 Juni 2026 yang diperoleh dari Yahoo Finance. Metode peramalan diterapkan pada data saham yang memiliki pola fluktuatif dengan kecenderungan tren menurun, kemudia…
creator: Nur Indah Lutfira
creator: Teti Sofia Yanti
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-05
publication date: 2026-08-05
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.26198

Analisis Perbedaan Jumlah Migrasi Penduduk Kota Bandung menggunakan Uji T² Hotelling
type: item
description: Abstract. Population migration is a key social phenomenon for understanding the demographic dynamics of a region. The city of Bandung, as an economic and educational hub, experienced significant fluctuations in migration, particularly in 2022 and 2023. In 2022, many people moved to Bandung in search of job opportunities, education, and a better quality of life. However, socioeconomic conditions and government policies following the COVID-19 pandemic likely influenced migration patterns in 2023. To understand the differences in inbound and outbound migration between 2022 and 2023, the Hotelling’s T² multivariate statistical test was used. The analysis results indicate that there is a significant difference between the numbers of in-migration and out-migration in the two years, as evidenced by a p-value below the 5% significance level. This information can assist the government in formulating more effective policies for population management and regional development in the city of Bandung. Keywords: Migrasi, T² Hotelling Test, Citizen. Abstrak. Migrasi penduduk merupakan fenomena sosial penting dalam memahami dinamika demografi suatu wilayah. Kota Bandung, sebagai pusat ekonomi dan pendidikan, mengalami fluktuasi migrasi yang signifikan, khususnya pada tahun 2022 dan 2023. Pada tahun 2022, banyak penduduk yang datang ke Bandung mencari peluang kerja, pendidikan, dan kualitas hidup yang lebih baik. Namun, kondisi sosial ekonomi dan kebijakan pemerintah setelah pandemi COVID-19 kemungkinan memengaruhi pola migrasi pada tahun 2023. Untuk memahami perbedaan migrasi masuk dan keluar antara tahun 2022 dan 2023, digunakan uji statistik multivariat T² Hotelling. Hasil analisis menunjukkan bahwa terdapat perbedaan signifikan antara jumlah migrasi masuk dan keluar di kedua tahun tersebut, berdasarkan hasil uji yang menunjukkan nilai p-value lebih kecil dari tingkat signifikansi 5%. Informasi ini dapat membantu pemerintah dalam merumuskan kebijakan yang lebih efektif dalam menge…
creator: Aulia Athhar Zakiyyan
creator: Suliadi
rights: Copyright (c) 2026 Bandung Conference Series: Statistics
date: 2026-08-05
publication date: 2026-08-05
volume: 6
number: 2
doi: 10.29313/bcss.v6i2.26245