Density Based Clustering Untuk Pemetaan Daerah Rawan Gempa Bumi Di Wilayah Sumatera Barat Menggunakan Metode DBSCAN
Keywords:
Clustering, Earthquake, K-Nearest Neighbors, DBSCAN, Silhouette Coefficient
Abstract
Earthquakes are natural disasters that cannot be prevented or avoided. One of the areas affected is the West Sumatra region, where West Sumatra is one of the regions in Indonesia which is in the Sumatra basin which is vulnerable to earthquakes. Therefore, density-based clustering analysis can be carried out which aims to produce a point map of earthquake-prone areas in the West Sumatra region using the Density Based Spatial Clustering of Application with Noise (DBSCAN) method. In implementing the DBSCAN algorithm, epsilon and minpts parameters are required using the K-Nearest Neighbors method with evaluation of results using the Silhouette Coefficient. The results of DBSCAN clustering using KNN input parameters obtained a total of 3 clusters and 1 noise with a silhouette coefficient value of 0.310 from the 2010-2023 data period. However, from the testing stage without using KNN, we got a high silhouette score, namely 0.890 with 2 clusters and 1 noise.
Downloads
Download data is not yet available.
References
[1] R. Baihaqi and D. Pujiastuti, “Analisis Risiko Gempa Bumi di Kota Pariaman Provinsi Sumatera Barat,” J. Fis. Unand, vol. 12, no. 2, pp. 206–212, 2023, doi: 10.25077/jfu.12.2.206-212.2023.
[2] F. D. Cahyo, F. Ihsan, R. Roulita, N. Wijayanti, and R. Mirwanti, “Kesiapsiagaan Bencana Gempa Bumi Dalam Keperawatan: Tinjauan Penelitian,” JPP (Jurnal Kesehat. Poltekkes Palembang), vol. 18, no. 1, pp. 87–94, 2023, doi: 10.36086/jpp.v18i1.1525.
[3] J. M. Flenniken, S. Stuglik, and B. V. Iannone, “Quantum GIS (QGIS): An introduction to a free alternative to more costly GIS platforms,” Edis, vol. 2020, no. 2, p. 7, 2020, doi: 10.32473/edis-fr428-2020.
[4] P. C. Latue, “Analisis Spasial Temporal Perubahan Tutupan Lahan di Pulau Ternate Provinsi Maluku Utara Citra Satelit Resolusi Tinggi,” Buana J. Geogr. Ekol. dan Kebencanaan, vol. 1, no. 1, pp. 31–38, 2023, doi: 10.56211/buana.v1i1.339.
[5] I. N. Setiawan, D. Krismawati, S. Pramana, and E. Tanur, “Klasterisasi Wilayah Rentan Bencana Alam Berupa Gerakan Tanah Dan Gempa Bumi Di Indonesia,” Semin. Nas. Off. Stat., vol. 2022, no. 1, pp. 669–676, 2022, doi: 10.34123/semnasoffstat.v2022i1.1538.
[6] I. M. Faiza, G. Gunawan, and W. Andriani, “Tinjauan Pustaka Sistematis: Penerapan Metode Machine Learning untuk Deteksi Bencana Banjir,” J. Minfo Polgan, vol. 11, no. 2, pp. 59–63, 2022, doi: 10.33395/jmp.v11i2.11657.
[7] G. E. Karniadakis, I. G. Kevrekidis, L. Lu, P. Perdikaris, S. Wang, and L. Yang, “Physics-informed machine learning,” Nat. Rev. Phys., vol. 3, no. 6, pp. 422–440, 2021, doi: 10.1038/s42254-021-00314-5.
[8] T. A. Alghamdi and N. Javaid, “A Survey of Preprocessing Methods Used for Analysis of Big Data Originated from Smart Grids,” IEEE Access, vol. 10, pp. 29149–29171, 2022, doi: 10.1109/ACCESS.2022.3157941.
[9] I. B. F. Arafat, M. A. Hariyadi, I. B. Santoso, and C. Crysdian, “Clustering Gempabumi di Wilayah Regional VII Menggunakan Pendekatan DBSCAN,” J. Teknol. Inf. dan Ilmu Komput., vol. 10, no. 4, pp. 823–830, 2023, doi: 10.25126/jtiik.20241046918.
[10] G. Djati, C. Series, and D. Mining, “Klasterisasi Data Pemain Premier League 2020 dengan Algoritma DBSCAN,” Gunung Djati Conf. Ser., vol. 3, p. 8, 2021.
[11] L. Qadrini, “Metode K-Means dan DBSCAN pada Pengelompokan Data Dasar Kompetensi Laboratorium ITS Tahun 2017,” J Stat. J. Ilm. Teor. dan Apl. Stat., vol. 13, no. 2, pp. 5–11, 2020, doi: 10.36456/jstat.vol13.no2.a2886.
[12] I. R. C. Pata, Statiswaty, and N. Ransi, “Segmentasi Pemetaan Pelanggan Potensial Menggunakan Algoritma Dbscan Dengan Rfm Model Berbasis Web,” Animator, vol. 1, no. 2, pp. 63–71, 2023, [Online]. Available: http://animator.uho.ac.id/index.php/journal/article/view/25%0Ahttp://animator.uho.ac.id/index.php/journal/article/download/25/16
[13] K. Annisa, B. S. Ginting, and M. A. Syar, “Penerapan Data Mining Pengelompokan Data Pengguna Air Bersih Berdasarkan Keluhannya Menggunakan Metode Clustering Pada Pdam Langkat,” J. Sist. Inf. Kaputama, vol. 6, no. 2, pp. 165–179, 2022, doi: 10.59697/jsik.v6i2.167.
[14] B. N. Sari and A. Primajaya, “Penerapan Clustering Dbscan Untuk Pertanian Padi Di Kabupaten Karawang,” J. Inform. dan Komput., vol. 4, no. 1, pp. 28–34, 2019, [Online]. Available: www.mapcoordinates.net/en.
[15] S. ‘Aina Salsabila, T. Widiharih, and S. Sudarno, “METODE K-HARMONIC MEANS CLUSTERING DENGAN VALIDASI SILHOUETTE COEFFICIENT (Studi Kasus : Empat Faktor Utama Penyebab Stunting 34 Provinsi di Indonesia Tahun 2018),” J. Gaussian, vol. 11, no. 1, pp. 11–20, 2022, doi: 10.14710/j.gauss.v11i1.34003.
[16] D. Hartama and M. Anjelita, “Analysis of Silhouette Coefficient Evaluation with Euclidean Distance in the Clustering Method (Case Study: Number of Public Schools in Indonesia),” J. Mantik, vol. 6, no. 3, pp. 3667–3677, 2022, [Online]. Available: https://iocscience.org/ejournal/index.php/mantik/article/view/3318
[17] T. S. Gunawan et al., “Development of video-based emotion recognition using deep learning with Google Colab,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 18, no. 5, pp. 2463–2471, 2020, doi: 10.12928/TELKOMNIKA.v18i5.16717.
[18] Y. Galahartlambang, T. Khotiah, and J. Jumain, “Visualisasi Data Dari Dataset COVID-19 Menggunakan Pemrograman Python,” J. Ilm. Intech Inf. Technol. J. UMUS, vol. 3, no. 01, pp. 58–64, 2021, [Online]. Available: http://jurnal.umus.ac.id/index.php/intech/article/view/417
[19] Angelina M. T. I. Sambi Ua et al., “Penggunaan Bahasa Pemrograman Python Dalam Analisis Faktor Penyebab Kanker Paru-Paru,” J. Publ. Tek. Inform., vol. 2, no. 2, pp. 88–99, 2023, doi: 10.55606/jupti.v2i2.1742.
[2] F. D. Cahyo, F. Ihsan, R. Roulita, N. Wijayanti, and R. Mirwanti, “Kesiapsiagaan Bencana Gempa Bumi Dalam Keperawatan: Tinjauan Penelitian,” JPP (Jurnal Kesehat. Poltekkes Palembang), vol. 18, no. 1, pp. 87–94, 2023, doi: 10.36086/jpp.v18i1.1525.
[3] J. M. Flenniken, S. Stuglik, and B. V. Iannone, “Quantum GIS (QGIS): An introduction to a free alternative to more costly GIS platforms,” Edis, vol. 2020, no. 2, p. 7, 2020, doi: 10.32473/edis-fr428-2020.
[4] P. C. Latue, “Analisis Spasial Temporal Perubahan Tutupan Lahan di Pulau Ternate Provinsi Maluku Utara Citra Satelit Resolusi Tinggi,” Buana J. Geogr. Ekol. dan Kebencanaan, vol. 1, no. 1, pp. 31–38, 2023, doi: 10.56211/buana.v1i1.339.
[5] I. N. Setiawan, D. Krismawati, S. Pramana, and E. Tanur, “Klasterisasi Wilayah Rentan Bencana Alam Berupa Gerakan Tanah Dan Gempa Bumi Di Indonesia,” Semin. Nas. Off. Stat., vol. 2022, no. 1, pp. 669–676, 2022, doi: 10.34123/semnasoffstat.v2022i1.1538.
[6] I. M. Faiza, G. Gunawan, and W. Andriani, “Tinjauan Pustaka Sistematis: Penerapan Metode Machine Learning untuk Deteksi Bencana Banjir,” J. Minfo Polgan, vol. 11, no. 2, pp. 59–63, 2022, doi: 10.33395/jmp.v11i2.11657.
[7] G. E. Karniadakis, I. G. Kevrekidis, L. Lu, P. Perdikaris, S. Wang, and L. Yang, “Physics-informed machine learning,” Nat. Rev. Phys., vol. 3, no. 6, pp. 422–440, 2021, doi: 10.1038/s42254-021-00314-5.
[8] T. A. Alghamdi and N. Javaid, “A Survey of Preprocessing Methods Used for Analysis of Big Data Originated from Smart Grids,” IEEE Access, vol. 10, pp. 29149–29171, 2022, doi: 10.1109/ACCESS.2022.3157941.
[9] I. B. F. Arafat, M. A. Hariyadi, I. B. Santoso, and C. Crysdian, “Clustering Gempabumi di Wilayah Regional VII Menggunakan Pendekatan DBSCAN,” J. Teknol. Inf. dan Ilmu Komput., vol. 10, no. 4, pp. 823–830, 2023, doi: 10.25126/jtiik.20241046918.
[10] G. Djati, C. Series, and D. Mining, “Klasterisasi Data Pemain Premier League 2020 dengan Algoritma DBSCAN,” Gunung Djati Conf. Ser., vol. 3, p. 8, 2021.
[11] L. Qadrini, “Metode K-Means dan DBSCAN pada Pengelompokan Data Dasar Kompetensi Laboratorium ITS Tahun 2017,” J Stat. J. Ilm. Teor. dan Apl. Stat., vol. 13, no. 2, pp. 5–11, 2020, doi: 10.36456/jstat.vol13.no2.a2886.
[12] I. R. C. Pata, Statiswaty, and N. Ransi, “Segmentasi Pemetaan Pelanggan Potensial Menggunakan Algoritma Dbscan Dengan Rfm Model Berbasis Web,” Animator, vol. 1, no. 2, pp. 63–71, 2023, [Online]. Available: http://animator.uho.ac.id/index.php/journal/article/view/25%0Ahttp://animator.uho.ac.id/index.php/journal/article/download/25/16
[13] K. Annisa, B. S. Ginting, and M. A. Syar, “Penerapan Data Mining Pengelompokan Data Pengguna Air Bersih Berdasarkan Keluhannya Menggunakan Metode Clustering Pada Pdam Langkat,” J. Sist. Inf. Kaputama, vol. 6, no. 2, pp. 165–179, 2022, doi: 10.59697/jsik.v6i2.167.
[14] B. N. Sari and A. Primajaya, “Penerapan Clustering Dbscan Untuk Pertanian Padi Di Kabupaten Karawang,” J. Inform. dan Komput., vol. 4, no. 1, pp. 28–34, 2019, [Online]. Available: www.mapcoordinates.net/en.
[15] S. ‘Aina Salsabila, T. Widiharih, and S. Sudarno, “METODE K-HARMONIC MEANS CLUSTERING DENGAN VALIDASI SILHOUETTE COEFFICIENT (Studi Kasus : Empat Faktor Utama Penyebab Stunting 34 Provinsi di Indonesia Tahun 2018),” J. Gaussian, vol. 11, no. 1, pp. 11–20, 2022, doi: 10.14710/j.gauss.v11i1.34003.
[16] D. Hartama and M. Anjelita, “Analysis of Silhouette Coefficient Evaluation with Euclidean Distance in the Clustering Method (Case Study: Number of Public Schools in Indonesia),” J. Mantik, vol. 6, no. 3, pp. 3667–3677, 2022, [Online]. Available: https://iocscience.org/ejournal/index.php/mantik/article/view/3318
[17] T. S. Gunawan et al., “Development of video-based emotion recognition using deep learning with Google Colab,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 18, no. 5, pp. 2463–2471, 2020, doi: 10.12928/TELKOMNIKA.v18i5.16717.
[18] Y. Galahartlambang, T. Khotiah, and J. Jumain, “Visualisasi Data Dari Dataset COVID-19 Menggunakan Pemrograman Python,” J. Ilm. Intech Inf. Technol. J. UMUS, vol. 3, no. 01, pp. 58–64, 2021, [Online]. Available: http://jurnal.umus.ac.id/index.php/intech/article/view/417
[19] Angelina M. T. I. Sambi Ua et al., “Penggunaan Bahasa Pemrograman Python Dalam Analisis Faktor Penyebab Kanker Paru-Paru,” J. Publ. Tek. Inform., vol. 2, no. 2, pp. 88–99, 2023, doi: 10.55606/jupti.v2i2.1742.
Published
2025-02-15
Section
Articles
Copyright Notice
An author who publishes in the Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) agrees to the following terms:
- Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-ShareAlike 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal
- Author is able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgement of its initial publication in this journal.
- Author is permitted and encouraged to post his/her work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).
Read more about the Creative Commons Attribution-ShareAlike 4.0 Licence here: https://creativecommons.org/licenses/by-sa/4.0/.
Abstract views: 116 , 
pdf downloads: 144
_(1).png)
