Analisis Pola Spatio-Temporal Gempa Bumi di Zona Busur Belakang Utara Lombok–Flores Menggunakan Agglomerative Clustering

Cipta Ramadhani; Made Sutha Yadnya; Paniran Paniran; Bulkis Kanata; Teti Zubaidah

doi:10.29303/jstl.v11i4.957

Authors

Cipta Ramadhani Jurusan Teknik Elektro Fakultas Teknik, Universitas Mataram
Made Sutha Yadnya Jurusan Teknik Elektro Fakultas Teknik, Universitas Mataram
Paniran Paniran Jurusan Teknik Elektro Fakultas Teknik, Universitas Mataram
Bulkis Kanata Jurusan Teknik Elektro Fakultas Teknik, Universitas Mataram
Teti Zubaidah Jurusan Teknik Elektro Fakultas Teknik, Universitas Mataram

DOI:

https://doi.org/10.29303/jstl.v11i4.957

Abstract

This study aims to identify earthquake clustering patterns in the Northern Lombok–Flores Back-Arc Zone using the Agglomerative Clustering method. Seismic data were obtained from the United States Geological Survey (USGS) covering the period 1970–2021, using latitude, longitude, depth, and magnitude as clustering variables. A sliding window approach was applied to capture the temporal evolution of seismic activity. The results indicate the formation of three stable clusters that consistently represent different earthquake depth levels across all analyzed time windows. Although fluctuations in the occurrence of large-magnitude earthquakes were observed in several windows, these variations did not significantly affect the clustering structure. This study demonstrates that the combination of Agglomerative Clustering and a sliding window approach is effective for identifying depth-based earthquake clustering patterns and describing their temporal dynamics

References

Arifin, A. (2023). Clustering earthquake event points in the Southeast Asia region using agglomerative hierarchical clustering. Sistemasi: Jurnal Sistem Informasi, 12(3), 900–914. https://doi.org/10.32520/stmsi.v12i3.3150

Bittencourt, J.C.N., Costa, D.G., Portugal, P. & Peixoto, M.L.M. (2025). On the spatiotemporal knowledge-driven vulnerability assessment of urban areas: A clustering-based approach. International Journal of Disaster Risk Reduction, 127, 105681. https://doi.org/10.1016/j.ijdrr.2025.105681

Dwi Mauluda, A., Kusumadewi Kuncoro, A. & Aningsih, S. (2025). Analisis pengelompokan gempa bumi di Sumatra Indonesia menggunakan metode K-Means, DBSCAN, Agglomerative Clustering, dan Mean Shift. Journal Online of Physics, 10(3), 75–86. https://doi.org/10.22437/jop.v10i3.44090

Felix, R.P., Hubbard, J.A., Bradley, K.E., Lythgoe, K.H., Li, L. & Switzer, A.D. (2022). Tsunami hazard in Lombok and Bali, Indonesia, due to the Flores back-arc thrust. Natural Hazards and Earth System Sciences, 22(5), pp.1665–1682. https://doi.org/10.5194/nhess-22-1665-2022

Handayani, W., Pratama, I. & Wibowo, N.B. (2025). Subduction and local fault earthquake analysis using ST-DBSCAN clustering algorithm in the Special Region of Yogyakarta (DIY). Kaunia: Integration and Interconnection Islam and Science Journal, 21(1), pp.27–39. https://doi.org/10.14421/kaunia.5347

Putri, N.A., Utami, T.W. & Wasono, R. (2023). Spatial Temporal Density-Based Spatial Clustering Applications With Noise (ST-DBSCAN) untuk pengelompokan titik kebakaran hutan dan lahan di Provinsi Riau pada tahun 2021. Prosiding Seminar Nasional Universitas Muhammadiyah Semarang (UNIMUS), 6, pp.436–445, 18 Oktober 2023.

Rahim, I., Yunus, A.R. & Hidayah, S. (2023). Seismic hazard analysis and zoning using hybrid machine learning approaches: Case study in Indonesia. Journal of Artificial Intelligence, Electrical and Computer Engineering (JAIEA), 4(3), pp.265–275. https://doi.org/10.59934/jaiea.v4i3.1072

Rahman, R.R.A. & Wijayanto, A.W. (2021). Pengelompokan data gempa bumi menggunakan algoritma DBSCAN. Jurnal Meteorologi dan Geofisika, 22(1), pp.31–38. https://doi.org/10.31172/jmg.v22i1.738

Selvina, Y. (2025). Pemetaan dan klasifikasi wilayah rawan gempa di Indonesia dengan metode K-Means dan LightGBM. Jurnal Informatika dan Teknik Elektro Terapan, 13(3S1). https://doi.org/10.23960/jitet.v13i3S1.8152

Seydoux, L., Balestriero, R., Poli, P., de Hoop, M., Campillo, M. & Baraniuk, R. (2020). Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning. Nature Communications, 11, 3972. https://doi.org/10.1038/s41467-020-17841-x

Supendi, P., Widiyantoro, S., Rawlinson, N., Yatimantoro, T., Muhari, A., Hanifa, N.R., Gunawan, E., Ash Shiddiqi, H., Imran, I., Anugrah, S.D., Daryono, D., Prayitno, B.S., Adi, S.P., Karnawati, D., Faizal, L. & Damanik, R. (2023). On the potential for megathrust earthquakes and tsunamis off the southern coast of West Java and southeast Sumatra, Indonesia. Natural Hazards, 116(1), pp.1315–1328. https://doi.org/10.1007/s11069-022-05696-y

Syaputra, E.W., Refrizon, R. & Zakariya, H. (2023). Identifikasi Sesar Segmen Ketahun Berdasarkan Metode First Horizontal Derivative (FHD) dan Second Vertical Derivative (SVD) Data Anomali Gaya Berat GGMplus. Jurnal Fisika Unand, 12(4), pp.598–607. https://doi.org/10.25077/jfu.12.4.598-607.2023

United States Geological Survey (USGS) (2024). Earthquake Catalog. Available at: https://earthquake.usgs.gov/earthquakes/search/ (Accessed: 24 May 2025)

Vijay, R.K. & Nanda, S.J. (2023). Earthquake pattern analysis using subsequence time series clustering. Pattern Analysis and Applications, 26(1), 19–37. https://doi.org/10.1007/s10044-022-01092-1

Vijay, R.K., Nanda, S.J. & Sharma, A. (2025). A review on clustering algorithms for spatiotemporal seismicity analysis. Artificial Intelligence Review, 58(8), pp.1–61. https://doi.org/10.1007/s10462-025-11229-3

Widiyantoro, S., Supendi, P., Rawlinson, N., Daryono, M.R. & Rosalia, S. (2024). A note on the seismicity of Sumatra, western Sunda Arc, Indonesia, in relation to the potential for back-arc thrusting. Scientific Reports, 14, 13115. https://doi.org/10.1038/s41598-024-64076-7