EARTHQUAKE CLUSTER MAPPING IN ACEH PROVINCE: AN ORDERING POINTS TO IDENTIFY THE CLUSTERING STRUCTURE (OPTICS) CLUSTERING APPROACH
DOI:
https://doi.org/10.11113/aej.v16.25198Keywords:
Disaster Mitigation, Earthquake, Spatial Clustering, OPTICSAbstract
Indonesia is known as a disaster-prone region. Located on the Pacific Ring of Fire, it is one of the countries most susceptible to various types of natural disasters. The province of Aceh is one of the regions in Indonesia that frequently experiences earthquakes due to its geographical location in a highly seismically active area. Therefore, an analysis of earthquake data clustering in the province of Aceh is necessary. The aim of this research is to determine earthquake clusters, which can be used for disaster mitigation and prevention measures. This research begins with examining the distribution pattern of data through a scatterplot. If the data distribution pattern shows varying density levels, then a clustering analysis using Ordering Points to Identify the Clustering Structure (OPTICS) is conducted. In the OPTICS algorithm, two parameters are required before clustering: minimum points (MinPts) and xi. The clustering results are evaluated using the silhouette coefficient (SC). Further data exploration is carried out by: (1) reducing the MinPts value, (2) clustering based on the supremum of the SC, and (3) identifying earthquake events with potential tsunami risks. The purpose of this data exploration is to increase the number of clusters formed while still considering the SC value limit, thus expanding the regions identified as earthquake-prone. The best clustering results are determined by comparing the obtained clusters. The best clustering result was found at MinPts = 20 and xi = 0.05, forming 4 clusters with a silhouette coefficient value of 0.72, indicating a strong cluster structure. This information is expected to benefit the public by raising awareness of earthquake-prone areas.
References
Djalante, R. 2018. Review article: A Systematic Literature Review of Research Trends and Authorships on Natural Hazards, Disasters, Risk Reduction and Climate Change in Indonesia. Natural Hazards and Earth System Sciences. 18(6): 1785–1810. DOI: https://doi.org/10.5194/nhess-18-1785-2018.
[2] Ismail-Zadeh, A.T. 2013. Earthquake Prediction and Forecasting. In: Bobrowsky, P.T. (eds) Encyclopedia of Natural Hazards. Encyclopedia of Earth Sciences Series. Springer, Dordrecht. DOI: https://doi.org/10.1007/978-1-4020-4399-4_106
[3] Cheng, S., and H. Liu. 2023. General Review of the Worldwide Tsunami Research. Journal of Marine Science and Application. 22 (1): 14–24. DOI: https://doi.org/10.1007/s11804-023-00315-z.
[4] Putriana, S., E. Ernawati, and D. Andreswari. 2021. Clustering Data Titik Gempa dengan Metode Fuzzy Possibilistic C-Means (Studi Kasus: Titik Gempa Pulau Sumatera Tahun 2013- 2018). Rekursif: Jurnal Informatika. 9(1): 99-119. DOI: https://doi.org/10.33369/rekursif.v9i1.15898.
[5] Benazir, and R. S. Oktari. 2024. Assessing Tsunami Risk Along the Aceh Coast, Indonesia: A Quantitative Analysis of Fault Rupture Potential and Early Warning System Efficacy for Predicting Arrival Time and Flood Extent. Natural Hazards. 120(5): 4875–4900. DOI: https://doi.org/10.1007/s11069-024-06401-x.
[6] Badan Penanggulangan Bencana Aceh. 2021. Dokumen Kajian Risiko Bencana Nasional Provinsi Aceh 2021-2025. Indonesia: Kartsaceh.
[7] King, R.S.,. 2015. Cluster Analysis and Data Mining: an Introduction. United States of America: Mercury Learning and Information.
[8] Aggarwal, C.C. 2018. An Introduction to Cluster Analysis. in Data Clustering: Algorithm and Application. New York: Chapman and Hall/CRC.
[9] Saavedra-Nieves, P., and M. Fernández-Pérez. 2024 Directional Density-Based Clustering. Advances in Data Analysis and Classification. 20(1): 289-314. DOI: https://doi.org/10.1007/s11634-024-00616-3.
[10] Menardi, M., and D. De Stefano. 2022. Density-based clustering of Social Networks. Journal of the Royal Statistical Society Series A: Statistics in Society. 185(3): 1004–1029. DOI: https://doi.org/10.48550/arXiv.2101.08334.
[11] Liu. L., Z. Hu, C. Zhou, and G. Xu. 2018. Research on the Clustering Algorithm of the Bicycle Stations Based on OPTICS. Concurrency and Computation: Practice and Experience. 31(10): 1-7. DOI: https://doi.org/10.1002/cpe.4876.
[12] Wagner, T., R. Feger, and A. Stelzer. 2018. Modifications of the OPTICS Clustering Algorithm for Short-Range Radar Tracking Applications. 15th European Radar Conference (EuRAD). DOI: https://doi.org/10.23919/EuRAD.2018.8546579.
[13] Ahmed, M.A., H. Baharin, and P. N. E. Nohuddin. 2020. Analysis of K-means, DBSCAN and OPTICS Cluster Algorithms on Al-Quran Verses. International Journal of Advanced Computer Science and Applications. 11(8): 248-254. DOI https://doi.org/10.14569/IJACSA.2020.0110832.
[14] Ankerst M., M. M. Breunig, H. P. Kriegel, and J. Sander. 1999. OPTICS: Ordering Points to Identify the Clustering Structure. SIGMOD Record (ACM Special Interest Group on Management of Data). 28(2): 49–60. DOI: https://doi.org/10.1145/304182.304187.
[15] Zhu, X., S. Nie, C. Wang, X. Xi, J. Wang, D.Li, and H. Zhou. 2021. A Noise Removal Algorithm Based on OPTICS for Photon-Counting LiDAR Data. IEEE Geoscience and Remote Sensing Letters. 18(8): 1471–1475. DOI: https://doi.org/10.1109/LGRS.2020.3003191.
[16] Febriana, N.L., and I. S. Sitanggang . 2017. Outlier Detection on Hotspot Data in Riau Province using OPTICS Algorithm. IOP Conf. Series: Earth and Environmental Science. 58(2017). DOI: https://doi.org/10.1109/LGRS.2020.3003191.
[17] Fan Y., and M. Wang,. 2024. Specification Mining Based on the Ordering Points to Identify the Clustering Structure Clustering Algorithm and Model Checking. Algorithms. 17(1): 28. DOI: https://doi.org/10.3390/a17010028.
[18] Rodriguez, M.Z., C.H. Comin, D. Casanova, O.M. Bruno, D.R. Amancio, L.F. Costa, and F.A. Rodrigues. 2019. Clustering Algorithms: A comparative approach. PLoS One. 14 (1): e0210236. DOI: https://doi.org/10.1371/journal.pone.0210236.
[19] Yin, L., X.Li, W.Zheng, Z.Lin, L.Song, L.Ge, and Q.Zeng. 2019. Fractal Dimension Analysis For Seismicity Spatial And Temporal Distribution In The Circum-Pacific Seismic Belt. Journal of Earth System Science. 128(1). DOI: https://doi.org/10.1007/s12040-018-1040-2.
[20] Moustafa, S.S.R., M.H.Yassien, M.Metwaly, A.M.Faried, and B.Elsaka. 2024. Applying Geostatistics to Understand Seismic Activity Patterns in the Northern Red Sea Boundary Zone. Applied Sciences. 14(4):1455. DOI: https://doi.org/10.3390/app14041455.
[21] Datasans. 2023. Machine Learning Cheatsheet : Model Klasifikasi. Datasans.
[22] Campello. R. J. G. B., P. Kröger, J. Sander, and A. Zimek. 2020. Density‐based Clustering. WIREs Data Mining and Knowledge Discovery. 10(2): 1-15. DOI: https://doi.org/10.1002/widm.1343.
[23] Rajabi, F., and J. J. McArthur. 2024. Applying OPTICS with and without PCA for Fault Detection of Fan Coil Units Using Building Automation System Data. Energy and Buildings. 317: 114368. DOI: https://doi.org/10.1016/j.enbuild.2024.114368
[24] Esri. (n.d.). How density-based clustering works. ArcGIS Pro documentation. Retrieved August, 2025, from https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/how-density-based-clustering-works.htm
[25] Sowan, B., T.-P. Hong, A. Al-Qerem, M. Alauthman, and N. Matar. 2023. Ensembling validation indices to estimate the optimal number of clusters. Applied Intelligence. 53(9): 9933–9957. DOI: https://doi.org/10.1007/s10489-022-03939-w.
[26] Pavlopoulos, J., G. Vardakas, and A. Likas. 2025. Revisiting Silhouette Aggregation Discovery Science. DOI: https://doi.org/10.1007/978-3-031-78977-9_23.
[27] Chicco, D., A. Campagner, A. Spagnolo, D. Ciucci, and G. Jurman. 2025. The Silhouette coefficient and the Davies-Bouldin index are more informative than Dunn index, Calinski-Harabasz index, Shannon entropy, and Gap statistic for unsupervised clustering internal evaluation of two convex clusters. PeerJ Computer Science. 11. DOI: https://peerj.com/articles/cs-3309/.
[28] Struyf, a., M. Hubert, and P. Rousseeuw. 1997. Clustering in an Object-Oriented Environment. Journal of Statistical Software. 1(4). DOI: https://doi.org/10.18637/jss.v001.i04.
[29] Ester, M., H.-P. Kriegel, J. Sander, and X. Xu,. 1996. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD'96). 226–231.
[30] Schubert, E., J. Sander, M. Ester, H. P. Kriegel, and X. Xu. 2017. DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN. ACM Transactions on Database Systems. 42(3). DOI: https://doi.org/10.1145/3068335.
[31] Kagan, Y. Y. 2010. Earthquake Size Distribution: Power-Law with Exponent β≡1/2?. Tectonophysics. 490(1-2): 103–114. DOI: https://doi.org/10.1016/j.tecto.2010.04.034.
[32] Chattamvelli, R., and R. Shanmugam. 2023. Descriptive Statistics for Scientists and Engineers: Applications in R. Springer Nature Switzerland. DOI: https://doi.org/10.1007/978-3-031-32330-0.
[33] Madlazim. 2012. Toward Tsunami Early Warning System in Indonesia by Using Rapid Rupture Durations Estimation. AIP Conference Proceedings. 1454(1):142–145. DOI: https://doi.org/10.1063/1.4730707.
[34] Desfandi, M. 2014. Urgensi Kurikulum Pendidikan Kebencanaan Berbasis Kearifan Lokal di Indonesia. Sosio-Didaktika: Social Science Education Journal. 1(2): 191–198. DOI: https://doi.org/10.15408/sd.v1i2.1261
