TEMPORAL SPATIAL PROPERTY PROFILING AND IDENTIFICATION OF EARTHQUAKE PRONE AREAS USING ST-DBSCAN AND K-MEANS CLUSTERING

Angga Radlisa Samsudin; Dhomas Hatta Fudholi; Lizda Iswari

doi:10.52436/1.jutif.2024.5.3.1293

Angga Radlisa Samsudin Magister Informatics, University Islam Indonesia Yogyakarta, Indonesia
Dhomas Hatta Fudholi Magister Informatics, University Islam Indonesia Yogyakarta, Indonesia
Lizda Iswari Magister Informatics, University Islam Indonesia Yogyakarta, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.3.1293

Keywords: Earthquake, K-Means, Prone area, Spasial Temporal, ST-DBSCAN

Abstract

Indonesia is a country located at the confluence of three major tectonic plates, namely Indo-Australia, Eurasia, and the Pacific so that earthquakes often occur, one of which is in West Nusa Tenggara Province. One way to accelerate the disaster mitigation process is to analyze earthquake occurrence based on spatial temporal aspects. This study uses data from BMKG NTB Province during 2018 with a total of 3,699 earthquake events which are then analyzed using ST-DBSCAN and K-Means. ST-DBSCAN analysis was used to determine earthquake prone areas based on the date and location of the event, while k-means used the depth and magnitude of the earthquake. The results show that the distribution pattern of earthquakes in the NTB region has a stationary pattern and there are similar prone areas based on the location and time of occurrence as well as the strength and depth of the earthquake. The ST-DBSCAN method using latitude and longitude attributes produces one cluster that covers 96.33% of the total data. Meanwhile, K-Means using the depth and magnitude attributes produced four clusters. The four clusters were obtained from the cluster density using the silhouette score value between -1 and 1. The K-means analysis used a silhouette score result of 18.527 which was found in cluster 1. Earthquake prone areas in the distribution of earthquakes or types of earthquakes are located in Gangga and Bayan sub-districts of North Lombok and in Sambelia and Sembalun sub-districts of East Lombok. The sub-district with the most frequent earthquakes is Sambelia sub-district with 112 earthquakes. Then the strength of the largest earthquakes on average occurred in Gangga sub-district with magnitudes of 4 to 6.2 SR with shallow earthquake types. The prone area is located at the foot of the mountain and directly adjacent to the ocean.ith shallow earthquake types. The Prone area is at the foot of a mountain and directly adjacent to the ocean.

Downloads

Download data is not yet available.

References

N. H. Qothrunnada, R. Y. Utami, and R. S. Amanda, “Menganalisis Bencana Alam Gempa Bumi Dalam Perspektif Al-Quran. Konferensi Integrasi Interkneksi Islam dan Sains”, Yogyakarta: FST UIN SUKA Yogyakarta, pp. 255-260, 2022.

A. Ricky, D. Ridho, Marizka, dkk, “Implementasi Business Intelligence Menentukan Daerah Rawan Gempa Bumi di Indonesia dengan Fitur Geolokasi”, Jurnal Edukasi dan Penelitian Informatika, Vol. 4, No. 1, 2018.

BPBD Kota Banda Aceh,BPBD Kota Banda Aceh. Retrieved from BPBD Kota Banda Aceh: https://bpbd.bandaacehkota.go.id/2018/08/05/pengertian-gempa-bumi-jenis-jenis-penyebab-akibat-dan-cara-menghadapi-gempa-bumi/ . 2018, Agustus 5.

BMKG, “Tentang Gempa Bumi”. BMKG, 2023. http://inatews2.bmkg.go.id/new/tentang_eq.php

BPS, “Provinsi Nusa Tenggara dalam Angka”. Badan Pusat Statistik Provinsi NTB. 2019.

S. Rudi. “Gempa Bumi M &.0 SR di Lombok Utara, NTB”. Magma Indonesia, 2022. https://magma.esdm.go.id/v1/press-release/162/gempa-bumi-m-70-sr-di-lombok-utara-ntb

Afandi, Ridwan, “Mekanisme Gempa Pulau Lombok”, ESDM. 2018. https://www.esdm.go.id/en/media-center/news-archives/mengapa-terjadi-gempa-beruntun-di-lombok-ini-penjelasannya

JDIH Prov NTB, “Rencana Pembangunan Jangka Menengah Provinsi NTB tahun 2013 – 2018 BAB II”. JDIH Prov NTB. 2018. https://jdih.ntbprov.go.id/sites/default/files/produk_hukum/BAB%20II.pdf

L. Iswari, “Profiling the and Temporal Properties of earthquake Occurances Using ST-DBSCAN Algorithm”, 2022 IEEE 7th International Conference on Information Technology and Digital Applications (ICITDA), pp. 1-8, 2022.

D. J. Manalu, R. Rahmawati, and T. Widiharih, “Pengelompokkan Titik Gempa di Pulau Sulawesi Menggunakan Algoritma ST-DBSCAN (Spatio Temporal-Density Based Spatial Clustering Application with Noise)”, Jurnal Gaussian, vol. 10, no. 4, pp. 554-561, 2021.

K. B. Chimwayi and J. Anuradha, “Clustering West Nile Virus Spatio-temporal data using ST-DBSCAN”, Procedia Computer Science, vol. 132, pp. 1218–1227, 2018. doi: 10.1016/j.procs.2018.05.037.

N. Hartanti, “Metode Elbow dan k-Means Guna Mengukur Kesiapan Siswa SMK Dalam Ujian Nasional”, Jurnal Nasional Teknologi & Sistem Informasi, vol. 6, no. 2. 2020.

R. Rahman, R. Amalia, and A. W. Wijayanto, “Pengelompokkan Data Gempa Bumi Menggunakan Algoritma DBSCAN”, Jurnal Meteorologi dan Geofisika, vol. 22, no. 1. 2021.

H. Huu-Trung, P. Quoc-Viet, H. Won-Joo, “Spatial-Temporal-DBSCAN-Based User Clustering and Power Allocation for Sum Rate Maximization in Millimeter-Wave NOMA Systems”, MDPI, 2020.

STPN, “Modul IV Jenis Data dan Struktur Data”. Sekolah Tinggi Pertanahan Nasional. 2016. https://prodi4.stpn.ac.id/wp-content/uploads/2020/2020/Modul/Semester%203/Modul%20SIG%20Teori%20&%20Praktik/Modul-SIG%202019%20Nuraini/Kuliah_SIG/Modul-4%20SIG.pdf .

Pertiwi, I. Indah, M. Arsyad, and P. Palloan, “Analisis Distribusi Spasial dan Temporal Seismotektonik Wilayah Sulawesi Bagian Selatan dan Barat Berdasarkan Nilai-b Dengan Metode Maksimum Likelihood”, Jurnal Fisika Universitas Negeri Makasar, vol. 8, no. 3, 2012.

Linda, N. Ihsan, and P. Palloan, “Analisis Analisis Distribusi Spasial dan Temporal Seismotektonik Berdasarkan Nilai-b Dengan Metode Maksimum Likelihood di Pulau Jawa”, Jurnal Sains dan Pendidikan Fisika, vol. 15, no. 1, 2019.

M. Ariandi and E. A. Agustini, “Data Spasial dan Non Spasial Penyebaran Penduduk di Kecamatan Rambutan”, SEMNASTIKOM. Mataram: 28 – 29 Oktober 2016. https://journal.universitasbumigora.ac.id/index.php/semnastikom2016/article/view/146

U. Y. Purwanto, “Penggerombolan Spasial Hotspot Kebakaran Hutan dan Lahan Menggunakan DBSCAN dan ST-DBSCAN”, Thesis, Sekolah Pacasarjana Institut Pertanian Bogor, 2012. https://123dok.com/document/9yn20j1y-spatial-hotspots-clustering-forest-land-fires-dbscan-dbscan.html

F. Reviantika, C. N. Harahap, and Y. Azhar, “Analsis Gempa Bumi Pada Pulau Jawa Menggunakan Clustering Algoritma K-Means”. Jurnal Dinamika Informatika, vol. 9, no. 1, 2020.

R. T. Vulandari, Data Mining Teori dan Aplikasi Rapid Maner, Yogyakarta: Gava Media. 2017.

R. Indraputra and R. Fitriana, “K-Means Clustering Data COVID-19”, Jurnal Teknik Industri, vol. 10, no. 3, pp. 283-297, 2020.

Y. Heryadi and T. Wahyono, Machine Learning Konsep dan Implementasi, Yogyakarta: Gava Media. 2020.

A. Murdianty and C. Sylvia, “Pengelompokkan Data Bencana Alam Berdasarkan Wilayah, Waktu, Jumlah Korban dan Kerusakan Fasilitas Dengan Algoritma K-Means”, Jurnal Media Informatika Budidarma, vol. 4, no. 3. 2020.

D. Suyanto, Data Mining Untuk Klasifikasi dan Klasterisasi Data, Bandung: Penerbit Informatika, 2019.