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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Indonesian Journal of Mathematics and Applications
Ardiyani, Faradilla
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Transportation Other

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Clustering of Indonesian Provinces by Environmental Pollution in 2024 Using the K-Means Algorithm Ardiyani, Faradilla; Akmalia, Rizka
Indonesian Journal of Mathematics and Applications Vol. 3 No. 2 (2025): Indonesian Journal of Mathematics and Applications (IJMA)
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.ijma.2025.003.02.1

Abstract

The environment encompasses all living things and their surroundings, which interact to influence and sustain each other. In the era of globalization, advanced technologies have emerged that enhance human life, but these developments also have negative effects, such as environmental pollution. This research aims to categorize environmental pollution data in Indonesia, enabling further analysis of the clustering results. The study employs the K-Means algorithm to analyze data from 2024. This algorithm groups a set of objects into K clusters, with each object assigned to the nearest cluster based on average distance. The research findings indicate that the K-Means algorithm achieved a Silhouette Coefficient of 0.68 and identified two distinct clusters: Cluster 1 consists of 32 members characterized by lower numbers of environmental pollution while Cluster 2 includes 6 members that represent areas with greater environmental pollution. This study aims to provide the government with insights to address the rising issue of environmental pollution effectively.
COMPARISON OF CLUSTERING EARTHQUAKE PRONE AREA IN SUMATRA ISLAND USING K-MEANS AND SELF-ORGANIZING MAPS Ardiyani, Faradilla; Sulandari, Winita; Susanti, Yuliana
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0017-0030

Abstract

An earthquake is a sudden vibration on the earth's surface caused by the shifting of tectonic plates. One region in Indonesia that is particularly prone to earthquakes is Sumatra Island, due to its geographical location at the convergence of two tectonic plates, namely the Indo-Australian plate, which is actively subducting beneath the Eurasian plate. While earthquakes cannot be prevented or avoided, effective disaster mitigation strategies can help minimize the impact. The purpose of this research is to classify earthquake-prone areas on Sumatra Island based on depth and magnitude, allowing for further analysis to determine the characteristics of the clustering results. The study employs two clustering methods to analyze earthquake data from 1973 to 2024: the K-means and Self-Organizing Maps (SOM) algorithm. K-means algorithm is preferred for its simplicity and efficiency in handling large datasets, and suitability for numerical earthquake data analysis. Conversely, the SOM algorithm offers more stable clustering results and preserves the topological structure of the data, making it advantageous for exploring spatial patterns. The research findings indicate that the K-means algorithm provides better grouping, achieving a Silhouette Coefficient of 0.53, compared to 0.47 for the SOM algorithm. The K-means clustering resulted in two clusters: Cluster 1 contains 1,213 members and is characterized by shallow depths (3.9 km-41 km) and larger magnitudes (5 - 8.92 ), indicating a higher risk level. In contrast, Cluster 2 includes 412 members and represents areas with greater depths (40.8 km-70 km) and smaller magnitudes (5 - 6.85 ), corresponding to a lower risk level. This research aims to support the government in its earthquake disaster mitigation efforts, especially on Sumatra Island.
Co-Authors Akmalia, Rizka Winita Sulandari Yuliana Susanti, Yuliana