ANALISIS KONVERGENSI DAN EFEKTIVITAS ALGORITMA K-MEANS PADA KLASTERISASI DATA KRIMINALITAS DAN PARTISIPASI KEAMANAN MASYARAKAT DI INDONESIA
DOI:
https://doi.org/10.54314/jssr.v9i3.6664Keywords:
K-Means Clustering, Crime, Community Participation, Elbow Method, Silhouette ScoreAbstract
Abstract: This study aims to cluster provinces in Indonesia based on crime rates and community participation in security in 2024. The objective is to determine how effective the K-Means Clustering method is in analyzing this data. This study uses six crime indicators and five indicators of community participation at the provincial level. Before analysis, the data were cleaned and standardized using the Z-Score method. The clustering process employed the K-Means method with Euclidean distance. Cluster quality was evaluated using SSE and the Silhouette Score. The results show that the K-Means algorithm achieved convergence with a stable SSE value. However, determining the optimal number of clusters yielded different results between the Elbow method and the Silhouette Score. After considering stability and interpretability, three clusters were determined to be the optimal result. The clustering results indicate variations in characteristics across provinces regarding crime and community participation. The study also shows that cluster separation is better for crime data than for participation data. Thus, the K-Means Clustering method can be used as an initial mapping of regional security conditions in Indonesia. However, these results are also influenced by data characteristics and the sensitivity of centroid initialization.
Keywords: K-Means Clustering, Crime, Community Participation, Elbow Method, Silhouette Score.
Abstrak: Penelitian ini bertujuan untuk mengelompokkan provinsi di Indonesia berdasarkan tingkat kriminalitas dan partisipasi masyarakat dalam keamanan pada tahun 2024. Tujuannya adalah untuk mengetahui seberapa efektif metode K-Means Clustering dalam menganalisis data ini. Penelitian ini menggunakan enam indikator kriminalitas dan lima indikator partisipasi masyarakat di tingkat provinsi. Sebelum dianalisis, data tersebut dibersihkan dan distandardisasi menggunakan metode Z-Score. Proses pengelompokan menggunakan metode K-Means dengan jarak Euclidean. Kualitas cluster dievaluasi menggunakan SSE dan Silhouette Score. Dari hasil penelitian, algoritma K-Means dapat mencapai konvergensi dengan nilai SSE yang stabil. Namun, penentuan jumlah cluster optimal menunjukkan hasil yang berbeda antara metode Elbow dan Silhouette Score. Setelah mempertimbangkan stabilitas dan interpretasi, ditetapkan bahwa tiga cluster adalah hasil yang optimal. Hasil klasterisasi menunjukkan adanya variasi karakteristik antarprovinsi pada aspek kriminalitas dan partisipasi masyarakat. Hasil penelitian juga menunjukkan bahwa pemisahan cluster pada data kriminalitas lebih baik dibandingkan data partisipasi. Dengan demikian, metode K-Means Clustering dapat digunakan sebagai pemetaan awal kondisi keamanan wilayah di Indonesia. Namun, hasil ini juga dipengaruhi oleh karakteristik data dan sensitivitas inisialisasi centroid.
Kata kunci: K-Means Clustering, Kriminalitas, Partisipasi Masyarakat, Elbow Method, Silhouette Score.
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