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All Journal JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Ilmiah Research Student
Baromim Triwijaya
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Religion Computer Science & IT Economics, Econometrics & Finance Mathematics Medicine & Pharmacology

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Sistem Informasi Geografis Persebaran Sepeda Motor Dan Tingkat Kecelakaan Di Jawa Tengah Berbasis Web Baromim Triwijaya; Bambang Agus Herlambang; Ahmad Khoirul Anam
JURNAL ILMIAH RESEARCH STUDENT Vol. 1 No. 3 (2024): Januari
Publisher : CV. KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jirs.v1i3.736

Abstract

Population growth in Central Java is increasing motorbike ownership, causing traffic problems, air pollution and accidents. The lack of an integrated information system regarding the relationship between the number of motorbikes and accident rates is a major obstacle. This research applies a Geographic Information System (GIS) with the Waterfall system development method. The result is a web-based GIS implementation that maps the number of motorbikes and accident rates in one interactive map. This GIS is expected to overcome limitations in access to accurate data and provide an in-depth view of the impact of the number of motorbikes on traffic accidents in the region. This research is an important basis for decision making and policy design to improve traffic safety in Central Java
Performance Comparison of K-Means Algorithm and BIRCH Algorithm in Clustering Earthquake Data in Indonesia with Web-Based Map Visualization Baromim Triwijaya; Setyoningsih Wibowo; Nur Latifah Dwi Mutiara Sari
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 1 (2025): Jurnal Teknologi dan Open Source, June 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i1.4400

Abstract

This study applies the K-Means and BIRCH algorithms to cluster earthquake data in Indonesia based on geographic coordinates (latitude and longitude), depth, and magnitude from 2008 to 2023. Due to its position at the intersection of three major tectonic plates, Indonesia is highly prone to earthquakes, making the mapping of vulnerable regions essential for disaster risk reduction. K-Means is selected for its simplicity and clustering effectiveness, while BIRCH is known for its scalability and efficiency in processing large datasets. The clustering process involves data preprocessing and normalization, followed by determining the optimal number of clusters using the Elbow method. Initial findings indicate that K-Means produces more distinct and well-separated clusters than BIRCH, with Silhouette Scores of 0.3501 and 0.2247, respectively. However, after expanding the dataset to 121,123 records and incorporating additional attributes such as mag_type, phasecount, and azimuth_gap, BIRCH demonstrated a significant improvement in performance, achieving a Silhouette Score of 0.3489—surpassing K-Means, which dropped to 0.1293. These results suggest that BIRCH is more effective for clustering large and complex datasets. The final clustering results are visualized on a web-based map to support spatial analysis and the identification of earthquake-prone zones.
Co-Authors Ahmad Khoirul Anam Bambang Agus Herlambang, Bambang Nur Latifah Dwi Mutiara Sari Setyoningsih Wibowo