Improving the Performance of K-Means Algorithm using the Particle Swarm Optimization for Clustering Forest Fire

Putri Utami

doi:10.52436/1.jutif.2026.7.2.4949

Authors

Putri Utami Muhammadiyah Pontianak

DOI:

https://doi.org/10.52436/1.jutif.2026.7.2.4949

Keywords:

Clustering, Forest Fire, K-Means, Particle Swarm Optimization, Spatial Analysis, Wildfire Risk

Abstract

Forest and land fires are a recurring ecological disaster in Indonesia, particularly in West Kalimantan, where peatlands and tropical climates contribute to high vulnerability. Effective identification of fire-prone areas is critical for mitigation efforts, yet conventional clustering methods such as K-Means suffer from limitations, especially in determining optimal cluster numbers and centroid initialization. This study proposes an enhanced clustering approach by integrating the Particle Swarm Optimization (PSO) algorithm with K-Means to improve the accuracy of hotspot clustering in Mempawah Regency. The research utilizes hotspot and weather datasets from January 2023 to March 2024, incorporating variables such as temperature, humidity, rainfall, and wind speed. Data preprocessing includes normalization using Min-Max Scaling. PSO is applied to determine the optimal number of clusters (K) within a range of 2 to 10 by evaluating Davies-Bouldin Index (DBI), Silhouette Coefficient (SC), and inertia. Experimental results show that the optimal configuration—20 iterations and 20 particles—yields a DBI of 0.897 and an SC of 0.464, indicating standard cluster quality. Visual validation using PCA demonstrates clear cluster separation, supporting the evaluation results. Compared to visual methods like Elbow, which suggest K=3–4, the PSO-KMeans approach identifies K=10 as optimal, providing better clustering performance. This research highlights the effectiveness of swarm intelligence in enhancing spatial data modeling and supports strategic decision-making for local wildfire mitigation efforts.

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