Exploration of Violence Case Patterns in Jayapura Police Site using Random Forest and K-Means

Widya Kurniawan; Eko Prasetio Widhi; Dewi Ikhlas Sari

doi:10.59934/jaiea.v5i1.1280

Authors

Widya Kurniawan Universitas Darussalam Gontor
Eko Prasetio Widhi Universitas Darussalam Gontor
Dewi Ikhlas Sari Universitas Darussalam Gontor

DOI:

https://doi.org/10.59934/jaiea.v5i1.1280

Keywords:

Crime, Violence, Data Mining, Random Forest, K-Means, Forecasting, Jayapura City

Abstract

Crime is an act that endangers individuals and society, with significant social consequences, including psychological trauma and economic losses. In Jayapura City, violence cases have shown a notable increase from 133 cases in 2020 to 233 cases in 2024, with "common assault" and "mob violence" being the most dominant types. The primary issue in crime data management at the Jayapura Police Department lies in its manual and non-integrated system, hindering effective analysis and decision-making. This study applies data mining techniques using the Random Forest algorithm to classify case resolution status, K-Means Clustering to group cases by characteristics, and Holt-Winters Exponential Smoothing to forecast trends through 2025. The Random Forest model achieved 91% accuracy in classification, while clustering successfully identified three priority clusters. Forecasting results indicate a continued upward trend in violence cases in the upcoming year. These findings provide a strong foundation for data-driven policy formulation and enhance the efficiency of violence case resolution. With this comprehensive approach, it is expected that decision-making by law enforcement and stakeholders will become more responsive, targeted, and effective.

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