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All Journal Bit (Fakultas Teknologi Informasi Universitas Budi Luhur)
Piliang, Arnita
Universitas Negeri Medan
Computer Science & IT Electrical & Electronics Engineering

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Clustering Crime-Prone Areas in Indonesia Using the K-Means Method Chairani, T.Sofia; Listia, Hijka; Wardaniah, Sabina; Wulandari, Siti; Tampubolon, Putri Tasya Agustina; Piliang, Arnita
Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Vol 21, No 2 (2024): SEPTEMBER 2024 (IN PRESS)
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/bit.v21i2.3414

Abstract

Crime in Indonesia is a significant problem that affects various aspects such as security, social, and economic. However, mitigation efforts are often hampered by a lack of structured information about crime-prone areas. This study aims to overcome this problem by grouping provinces in Indonesia based on crime patterns using the K-Means Clustering algorithm. Crime statistics data for 2014-2023 from the Central Statistics Agency (BPS) were analyzed by determining the optimal number of clusters that resulted in five clusters with evaluation using Python and the Scikit-learn library. The results showed a Silhouette Score of 0.593, which reflects the formation of a fairly good cluster. This clustering provides data-driven guidance for the government in developing more targeted security policies to reduce crime rates in Indonesia.
Co-Authors Chairani, T.Sofia Listia, Hijka Tampubolon, Putri Tasya Agustina Wardaniah, Sabina Wulandari, Siti