Peramalan Harga Saham PT Bank Rakyat Indonesia (Persero) Tbk Menggunakan Model Autoregressive Integrated Moving Average (ARIMA)

Authors

  • Muhamad Arifianto Darmawan Prodi Statistika, Fakultas Farmasi dan Sains, Universitas Islam Bandung, Indonesia
  • Fitri Amanah Prodi Statistika, Fakultas Farmasi dan Sains, Universitas Islam Bandung, Indonesia

DOI:

https://doi.org/10.29313/bcss.v6i2.26220

Keywords:

ARIMA, harga saham, peramalan, time series, BBRI

Abstract

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 harga penutupan harian saham BBRI periode 1 Januari 2025 sampai dengan 15 Agustus 2025 sebanyak 143 observasi. Tahapan analisis meliputi uji stasioneritas menggunakan transformasi Box-Cox dan uji Augmented Dickey-Fuller (ADF), identifikasi model melalui plot Autocorrelation Function (ACF) dan Partial Autocorrelation Function (PACF), estimasi parameter menggunakan Maximum Likelihood Estimation (MLE), evaluasi model berdasarkan Residual Mean Square (MS) dan uji diagnostik Ljung-Box, serta pengukuran akurasi menggunakan Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa model ARIMA(0,1,2) merupakan model terbaik dengan nilai Residual Mean Square (MS) sebesar 10.132,8 serta memenuhi asumsi residual white noise. Hasil peramalan menunjukkan harga saham PT Bank Rakyat Indonesia (Persero) Tbk cenderung stabil pada tujuh periode mendatang dengan tingkat akurasi peramalan yang sangat baik berdasarkan nilai MAPE.

Kata Kunci: ARIMA, harga saham, peramalan, time series, BBRI.

References

Yahoo Finance. (2025). Historical data saham PT Bank Rakyat Indonesia (Persero) Tbk.
Diakses dari https://finance.yahoo.com pada tanggal 20 Agustus 2025.
Alfian, M., Sadewa, B., Gubu, L., Pimpi, L., Matematika, J., Matematika, F., & Pengetahuan, I. (2024). Peramalan Harga Saham PT. Bank Central Asia, Tbk Menggunakan Metode ARIMA. Jurnal Derivat, 11(1). https://finance.yahoo.com.
Gazali, M. M., & Setiawan, H. (2025). Penerapan Model ARIMA untuk Meramalkan Harga Pembukaan Harian Saham PT. Bank Central Asia Tbk. Digital Transformation Technology, 5(1), 278–289. https://doi.org/10.47709/digitech.v5i1.6129
Guo, Z. (2023). Research on the Augmented Dickey-Fuller Test for Predicting Stock Prices and Returns. Advances in Economics, Management and Political Sciences, 44(1), 101–106. https://doi.org/10.54254/2754-1169/44/20232198
Hassani, H., Mashhad, L. M., Royer-Carenzi, M., Yeganegi, M. R., & Komendantova, N. (2025). White Noise and Its Misapplications: Impacts on Time Series Model Adequacy and Forecasting. Forecasting, 7(1). https://doi.org/10.3390/forecast7010008
Hyndman, R. J., & Koehler, A. B. (2005). Another look at measures of forecast accuracy. http://www.forecasters.org/data/m3comp/m3comp.htm
Maulana, C., & Hajarisman, N. (2023). Penerapan Transformasi Box Cox untuk Mengatasi Masalah Ketidakstasioneran dan Pola Periodik dalam Data Deret Waktu pada Ekspor Bidang Pertanian di Indonesia. Bandung Conference Series: Statistics, 3(2), 763–772. https://doi.org/10.29313/bcss.v3i2.9371
Maulidiyah, W., & Fauzy, A. (2023). Perbandingan Metode Peramalan Double Exponential Smoothing with Damped Parameter dan Autoregressive Integrated Moving Average (Studi Kasus: Data Volume Penjualan Bunga Krisan di Pasar Bunga Rawa Belong DKI Jakarta Tahun 2018-2022). In Emerging Statistics and Data Science Journal (Vol. 1, Issue 3).
Montaño Moreno, J. J., Palmer Pol, A., Sesé Abad, A., & Cajal Blasco, B. (2013). El índice R-MAPE como medida resistente del ajuste en la previsiońn. Psicothema, 25(4), 500–506. https://doi.org/10.7334/psicothema2013.23
Ospina, R., Gondim, J. A. M., Leiva, V., & Castro, C. (2023). An Overview of Forecast Analysis with ARIMA Models during the COVID-19 Pandemic: Methodology and Case Study in Brazil. In Mathematics (Vol. 11, Issue 14). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/math11143069
Romanuke, V. (2022). Arima Model Optimal Selection for Time Series Forecasting. Maritime Technical Journal, 224(1), 28–40. https://doi.org/10.2478/sjpna-2022-0003
Saputra, J. E., & Febrianti, W. (2025). Application of Autoregressive Integrated Moving Average (ARIMA) for Forecasting Inflation Rate in Indonesia. Jurnal Matematika, Statistika Dan Komputasi, 21(2), 382–396. https://doi.org/10.20956/j.v21i2.36609
Sheryl Noven, M., Respatiwulan, R., & Sulandari, W. (2025). IS THE BOX-COX TRANSFORMATION NEEDED IN MODELING TELKOM’S STOCK PRICE USING NNAR AND DESH METHODS? MEDIA STATISTIKA, 17(2), 185–196. https://doi.org/10.14710/medstat.17.2.185-196
SJ, D., DV, P., MS, S., & SR, R. (2023). Time series forecasting of price for oilseed crops by combining ARIMA and ANN. International Journal of Statistics and Applied Mathematics, 8(4), 40–54. https://doi.org/10.22271/maths.2023.v8.i4a.1098
Uadiale, K. K., & Guobadia, E. K. (2024). Effect of Box-Cox Transformation on a k-th Exponential Weighted Moving Average Processes for Time Series. African Multidisciplinary Journal of Sciences and Artificial Intelligence, 1(2), 867–881. https://doi.org/10.58578/amjsai.v1i2.4167
Voulgaraki, M. K. (2019). Box Cox transformation in forecasting sales. Evidence of the Greek Market.

Published

2026-08-05