Application of Bisecting K-Means Method in Grouping Earthquake Data (Case Study: Earthquakes in Indonesia 2023)

Zulkifli Rais; Hardianti Hafid; Shopia Risqi

doi:10.12962/j27213862.v8i3.23335

Application of Bisecting K-Means Method in Grouping Earthquake Data (Case Study: Earthquakes in Indonesia 2023)

Zulkifli Rais, Hardianti Hafid, Shopia Risqi

Abstract

Earthquakes are natural disasters that frequently occur in Indonesia, threatening the safety and resilience of its communities. This study aims to analyze the descriptive and clustering results of earthquake data in Indonesia. The data used in this study include various variables such as latitude, longitude, magnitude, and depth as the main features. The method used in this study is Bisecting K-means, and the Davies Bouldin Index test is used to determine the number of clusters. The study results indicate the formation of 3 groups, where cluster 1 falls into the deep earthquake category, cluster 3 falls into the intermediate earthquake category, and cluster 2 falls into the shallow earthquake category, with an average Davies-Bouldin Index value of 0.4758.

Keywords

Clustering; Bisecting K-means; Earthquakes

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References

Anindya, F., & Oktaviana, P. P. (2022). Pemodelan Magnitude Gempa Bumi di Indonesia Menggunakan Generalized Extreme Value

( GEV ). Jurnal Sains Dan Seni Its, 11(6).

Apriyani, P., Dikananda, A. R., & Ali, I. (2023). Penerapan Algoritma K-Means dalam Klasterisasi Kasus Stunting Balita Desa Tegalwangi. Hello World Jurnal Ilmu Komputer, 2(1), 20–33. https://doi.org/10.56211/helloworld.v2i1.230

Arbaeti, E. E., Pardede, A. M. H., & Kadim, L. A. N. (2023). Application of K-Means Clustering Algorithm To Analyze Insurance Company Business (Case Study: Pt. Jasindo Insurance). Journal of Mathematics and Technology (MATECH), 2(2), 173–192.

Arifin, A. (2023). Pengelompokan Titik Kejadian Gempabumi di Wilayah Asia Tenggara Menggunakan Agglomerative Hierarchical Clustering Clustering Earthquake Event Points in the Southeast Asia Region using Agglomerative Hierarchical Clustering. SISTEMASI: Jurnal Sistem Informasi, 12(September), 900–914.

Ayub, M. (2007). Proses Data Mining dalam Sistem Pembelajaran Berbantuan Komputer. Jurnal Sistem Informasi, 21(1), 21–30.

Dwididanti, S., & Anggoro, D. A. (2022). Analisis Perbandingan Algoritma Bisecting K-Means dan Fuzzy C-Means pada Data Pengguna Kartu Kredit. Emitor: Jurnal Teknik Elektro, 22(2), 110–117. https://doi.org/10.23917/emitor.v22i2.15677

Febriyanti, L., & Zakaria, H. (2023). Implementasi Data Mining Untuk Memprediksi Produktivitas Pada Tanaman Kacang Tanah Menggunakan Metode Naive Bayes (Studi Kasus : Perkebunan Kacang Tanah Di Kota Bogor). LOGIC : Jurnal Ilmu Komputer Dan Pendidikan, 1(2), 105–118. https://journal.mediapublikasi.id/index.php/logic

Firmansyah, T., Poningsih, & Andani, S. R. (2022). Analisis Clustering Algoritma K-Means Sebagai Rekomendasi Penambahan Koleksi Buku Di Perpustakaan Madrasah Tsanawiyah Negeri 2 Simalungun. Zahra: Buletin Big Data, Data Science and Artificial Intelligence, 1(1), 44–48.

Hastari, D., Nurunnisa, F., Winanda, S., & Dwi Aprillia, D. (2023). Penerapan Algoritma K-Means dan K-Medoids untuk MengelompokkanData Negara Berdasarkan Faktor Sosial-Ekonomi dan Kesehatan. SENTIMAS: Seminar Nasional Penelitian Dan Pengabdian Masyarakat, 274–281. https://journal.irpi.or.id/index.php/sentimas

Imro’ah, N., Ayuningtias, I., & Debataraja, N, N. (2019). Analisis Cluster Non-Hirarki Dengan Metode K-Modes. Bimaster : Buletin Ilmiah Matematika, Statistika Dan Terapannya, 8(4), 909–916. https://doi.org/10.26418/bbimst.v8i4.36633

Isyfa Rhamdani, A., & Jamaludin, H. (2023). Pengelompokan Wilayah Menurut Kekuatan Gempa Bumi Menggunakan Clustering. Jurnal Media Pratama, 17(2), 149–158.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2017). An Introduction to Statistical Learning. Springer.

K3, Tim Karakter. (2019). Pedoman K3 Gempuran: Gempa Bumi, Erupsi Gunung Merapi & Kebakaran. Universitas Negeri Yogyakarta.

Lompoliuw, N. O., & Purnomo, H. D. (2023). Implementasi Algoritma K-Means untuk Pengelompokkan Lama Sembuh Pasien Covid-19. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 7(2), 186–193. https://doi.org/10.35870/jtik.v7i2.706

Prasetio, A., Effendi, M. M., & Dwi M, M. N. (2023). Analisis Gempa Bumi Di Indonesia Dengan Metode Clustering. Bulletin of Information Technology (BIT), 4(3), 338–343. https://doi.org/10.47065/bit.v4i3.820

Prataopu, R. D. (2013). Analisis Tingkat Kekerasan Tanah di Bawah Stasiun-Stasiun Seismik di Jawa Tengah Menggunakan Software Seisgram2K. Jurnal Inovasi Fisika Indonesia, 2(3), 27–36.

Royal, S. (2024). Perancangan Aplikasi Data Mining Untuk Menentukan Tingkat Kelarisan Produk Menggunakan Metode Clustering Dengan Algoritma K-Means. Journal of Science and Social Research, 4307(1), 116–123. http://jurnal.goretanpena.com/index.php/JSSR

Santosa, B. (2007). Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis. Graha Ilmu.

Tania, A., Handhayani, T., & Hendryli, J. (2023). Perbandingan Antara Algoritma K-Means Dan Algoritma Bisecting K-Means Dalam Menganalisis Gempa Bumi Di Indonesia. Simtek : Jurnal Sistem Informasi Dan Teknik Komputer, 8(2), 265–270. https://doi.org/10.51876/simtek.v8i2.205

Virantika, E., Kusnawi, K., & Ipmawati, J. (2022). Evaluasi Hasil Pengujian Tingkat Clusterisasi Penerapan Metode K-Means Dalam Menentukan Tingkat Penyebaran Covid-19 di Indonesia. Jurnal Media Informatika Budidarma, 6(3), 1657. https://doi.org/10.30865/mib.v6i3.4325

Wahono, R. S. (2023). Data Mining Data mining. In Mining of Massive Datasets (Vol. 2, Issue January 2013). https://www.cambridge.org/core/product/identifier/CBO9781139058452A007/type/book_part

Yoliadi, D. N. (2023). Data Mining Dalam Analisis Tingkat Penjualan Barang Elektronik Menggunakan Algoritma K-Means. Insearch: Information System Research Journal, 3(01). https://doi.org/10.15548/isrj.v3i01.5829

Zhou, Z., Ran, A., Chen, S., Zhang, X., Wei, G., Li, B., Kang, F., Zhou, X., & Sun, H. (2020). A Fast Screening Framework dor Second-Life Batteries Based on an Improved Bisecting K-Means Algorithm Combined with Fast Pulse Test. Journal of Energy Storage, 31, 1–32

DOI: http://dx.doi.org/10.12962%2Fj27213862.v8i3.23335

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