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All Journal Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Seminar Nasional Teknologi Informasi Komunikasi dan Industri JURNAL INSTEK (Informatika Sains dan Teknologi) KOMPUTIKA - Jurnal Sistem Komputer TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research
Fitri Insani
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences

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Journal : Bulletin of Computer Science Research

 1 
Analisis Perbandingan Metode DBSCAN dan Meanshift dalam Klasterisasi Data Gempa Bumi di Indonesia MHD Ade Setiawan; Fitri Insani; Yelfi Vitriani; Yusra
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.605

Abstract

Indonesia is one of the countries with a high vulnerability to earthquakes due to its location at the convergence of three major tectonic plates: the Indo-Australian, Eurasian, and Pacific plates. As a result of this interaction, seismic activity is highly frequent across various regions. Understanding the distribution patterns of earthquakes is essential for disaster risk mitigation. One approach used to analyze these patterns is clustering, particularly using the DBSCAN  and Meanshift algorithms, which can group spatial data without predefining the number of clusters. This study aims to compare the effectiveness of both algorithms in clustering earthquake data based on spatial parameters, namely latitude and longitude. Evaluation was conducted using cluster visualization and the Silhouette Score as the clustering validity metric. The results show that DBSCAN  produces more optimal clustering with a Silhouette Score of 0.930028, higher than Meanshift's score of 0.90103. DBSCAN  is also capable of detecting relevant outliers in earthquake analysis, while Meanshift generates more clusters but with less separation. Using spatial parameters such as latitude and longitude, DBSCAN  is considered more effective in identifying the spatial distribution patterns of seismic activity in Indonesia based on earthquake data. This research supports the development of decision support systems for earthquake disaster mitigation and serves as a reference for selecting appropriate clustering methods for spatial data analysis.
Pengelompokan Wilayah Bencana Banjir di Indonesia Menggunakan Algoritma K-Means Wenny Tarisa Oktaviany; Fitri Insani; Alwis Nazir; Pizaini
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.608

Abstract

Floods are one of the natural disasters that often occur in Indonesia, especially during the rainy season. This disaster is caused by various factors, both natural and caused by human activities, such as high rainfall, poor drainage systems, land conversion, and suboptimal spatial planning. The impact of floods is very detrimental, both physically and psychologically, including loss of life and damage to property. Therefore, a method is needed to group areas based on their level of vulnerability to flooding. This study aims to group flood disaster areas in Indonesia using the K-Means algorithm. The data used comes from the BNPB Geoportal covering flood events from January 2020 to December 2024, with a total of 7,487 events from 498 areas. Based on the test results obtained using the Silhouette Coefficient, it shows that 2 clusters were selected as the best number of clusters with a Silhouette Coefficient value of 0.8461 which is included in the strong clustering structure. Of the 2 clusters obtained, cluster 1 is a high-risk category consisting of 35 areas, while cluster 2 is a low-risk category consisting of 463 areas. The results of this study can provide information for related parties to improve the efficiency of flood disaster management.
Co-Authors Alwis Nazir Damayanti, Elok Eggy P Elin Haerani Elvia Budianita Frica A Ambarwati Iis Afrianty Iwan Iskandar Jasril Jasril M Fadhli Ihsan MHD Ade Setiawan Muhammad Habib Nazlis Muhammad Hasbi Assidiqqi Novi Yanti Nuradha Liza Utami Okfalisa Okfalisa Okfalisa saktioto Pizaini Pizaini Purnama, Nilam Rahmad Abdillah SURYA ADITYA GD Suwanto Sanjaya Syarifatun Nissa Wenny Tarisa Oktaviany Yelfi Vitriani Yelvi Fitriani Yusra