Earthquake Clustering in Sumatra Using K-Medoids Based on Magnitude and Depth
Keywords:
Earthquake, K-Medoids, MagnitudeAbstract
Abstract. Earthquake activity in Sumatra exhibits high variability in terms of magnitude and focal depth due to its complex tectonic setting, making it difficult to identify general patterns using conventional descriptive analysis. This study aims to explore mainshock earthquake grouping patterns in Sumatra based on magnitude and depth variables as an initial effort to understand the characteristics of seismic events in the region. Earthquake data from January 2000 to October 2025 were obtained from the International Seismological Centre (ISC) and the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG), consisting of 1037 mainshock earthquake events in Sumatra with a minimum magnitude of 5 moment magnitude. Data preprocessing included outlier detection using boxplots, multicollinearity testing, and standardization using Z-score. The clustering process was conducted using the K-Medoids method with Euclidean distance, and the optimal number of clusters was determined based on the Silhouette Coefficient. The results show that the optimal clustering configuration consists of three clusters with a Silhouette Coefficient value of 0.405, indicating a moderate level of cluster separation. Each cluster represents distinct mainshock earthquake characteristics based on magnitude
and depth, and their spatial distributions exhibit different patterns across the Sumatra region. These findings indicate that the K-Medoids method is suitable for exploratory clustering of mainshock earthquake data with large variability and the presence of outliers. The results provide an initial overview of mainshock earthquake characteristics in Sumatra and serve as a basis for further research incorporating additional variables or spatio temporal approaches to improve the understanding of regional seismicity.
References
S. Dey Rahmawati, “Spatial Modeling of Earthquake Risk in Sulawesi and Maluku Based on Geological Factors,” ESTIMASI: Journal of Statistics and Its Application, vol. 6, no. 2, pp. 2721–379, 2025, doi: 10.20956/ejsa.v6i2.45160.
K. Choirunnisa, M. N. Fahmi, and A. Realita, “SEISMISITAS PULAU SUMATERA DAN WILAYAH SEKITARNYA AKIBAT AKTIVITAS SEISMO-TEKTONIK ZONA SUBDUKSI,” Jurnal Inovasi Fisika Indonesia (IFI), vol. 14, no. 2, pp. 253–267, 2025.
D. A. N. Sirodj, M. N. Aidi, B. Sartono, U. D. Syafitri, and B. Pranata, “Identification of Earthquake Prone Zones in Sumatra using Density Based Spatial Clustering of Applications with Noise,” CAUCHY: Jurnal Matematika Murni dan Aplikasi, vol. 10, no. 2, pp. 916–928, Sep. 2025, doi: 10.18860/cauchy.v10i2.36120.
J. T. Simamora and E. L. Namigo, “Pemetaan Magnitude of Completeness (Mc) untuk Gempa Sumatera,” Jurnal Fisika Unand, vol. 5, no. 2, pp. 179–186, Apr. 2016, doi: 10.25077/jfu.5.2.179-186.2016.
A. A. Eluyemi, S. Baruah, and S. Baruah, “Empirical relationships of earthquake magnitude scales and estimation of Guttenberg–Richter parameters in gulf of Guinea region,” Sci. Afr., vol. 6, pp. 1–8, Nov. 2019, doi: 10.1016/j.sciaf.2019.e00161.
Q. Salsabillah and T. Prastowo, “ANALISIS RELASI MOMEN SEISMIK DAN MAGNITUDO MOMEN UNTUK VARIASI KEDALAMAN SUMBER GEMPA TEKTONIK (SHALLOW, INTERMEDIATE, AND DEEP SOURCES),” Inovasi Fisika Indonesia, vol. 11, no. 1, pp. 8–16, Feb. 2022, doi: 10.26740/ifi.v11n1.p8-16.
W. Anggraeni, “Analisis Pengelompokan Gempa Bumi di Indonesia Berdasarkan Ruang-Waktu-Kekuatan Kedalaman,” NUCLEUS, vol. 4, no. 2, pp. 136–160, Feb. 2024, doi: 10.37010/nuc.v4i2.1480.
U. Rafflesia, D. Rosadi, D. P. Sari, and P. Novianti, “Analysis of Seismic Data in Sumatra using Robust K-Means Clustering,” Journal of Applied Data Sciences, vol. 6, no. 1, pp. 391–404, Jan. 2025, doi: 10.47738/jads.v6i1.523.
A. P. Putra, J. Tshivana, and E. Rilvani, “PERBANDINGAN TEORITIS DAN EKSPERIMEN ALGORITMA K-MEANS DAN K-MEDOIDS DALAM KLASTERISASI DATA,” Kohesi: Jurnal Multidisiplin Saintek, vol. 10, no. 2, pp. 10–24, 2025, doi: 10.8734/Kohesi.v1i2.365.
M. Orisa and A. Faisol, “Analisis Algoritma Partitioning Around Medoid untuk Penentuan Klasterisasi,” Jurnal Teknologi Informasi dan Terapan, vol. 8, no. 2, pp. 86–90, Dec. 2021, doi: 10.25047/jtit.v8i2.258.
A. Mazarei, R. Sousa, J. Mendes-Moreira, S. Molchanov, and H. M. Ferreira, “Online boxplot derived outlier detection,” Int. J. Data Sci. Anal., vol. 19, no. 1, pp. 83–97, Jan. 2025, doi: 10.1007/s41060-024-00559-0.
Effiyaldi et al., “Penerapan Uji Multikolinieritas Dalam Penelitian Manajemen Sumber Daya Manusia,” Jurnal Ilmiah Manajemen dan Kewirausahaan, vol. 1, no. 2, pp. 94–102, 2022, [Online]. Available: https://ejournal.unama.ac.id/index.php/jumanage
A. K. Jain, “Data clustering: 50 years beyond K-means,” Pattern Recognit. Lett., vol. 31, no. 8, pp. 651–666, Jun. 2010, doi: 10.1016/j.patrec.2009.09.011.
M. A. Mercioni and S. Holban, “A Survey of Distance Metrics in Clustering Data Mining Techniques,” in Proceedings of the 2019 3rd International Conference on Graphics and Signal Processing, New York, NY, USA: ACM, Jun. 2019, pp. 44–47. doi: 10.1145/3338472.3338490.
D. Marlina, N. Fauzer Putri, A. Fernando, and A. Ramadhan, “Implementasi Algoritma K-Medoids dan K-Means untuk Pengelompokkan Wilayah Sebaran Cacat pada Anak,” Jurnal CoreIT, vol. 4, no. 2, 2018.
V. Fauziah and Suliadi, “Pengelompokkan Desa di Jawa Barat Berdasarkan Variabel Penyusun Indeks Desa Membangun,” Bandung Conference Series: Statistics, vol. 4, no. 1, pp. 221–229, Feb. 2024, doi: 10.29313/bcss.v4i1.11589.
C. Nisa and L. Anggi Kurniawati, “Perbandingan Algoritma K-Means dan K-Medoids dalam Klasterisasi Migrasi Penduduk Kabupaten/Kota di Jawa Tengah,” Jurnal Riset Statistika, pp. 131–140, Dec. 2025, doi: 10.29313/jrs.v5i2.8701.
A. F. Nurfauzan and S. Darwis, “Clustering Probabilistic Seismic Hazard Analysis Temporal Epidemic-Type Aftershock Sequence untuk Premi Asuransi,” Jurnal Riset Statistika, pp. 41–48, Jul. 2024, doi: 10.29313/jrs.v4i1.3864.
