Implementasi Algoritma K-Means dan K-Medoids Dalam Klasterisasi Kasus Kekerasan Terhadap Perempuan

Nur Azizah Kamilah; Tatang Rohana; Rahmat Rahmat; Ahmad Fauzi

doi:10.30865/mib.v8i2.7558

Implementasi Algoritma K-Means dan K-Medoids Dalam Klasterisasi Kasus Kekerasan Terhadap Perempuan

^(*)Nur Azizah Kamilah

(Universitas Buana Perjuangan Karawang, Karawang, Indonesia)

Tatang Rohana (Universitas Buana Perjuangan Karawang, Karawang, Indonesia)

Rahmat Rahmat (Universitas Buana Perjuangan Karawang, Karawang, Indonesia)

Ahmad Fauzi (Universitas Buana Perjuangan Karawang, Karawang, Indonesia)

^{(*) Corresponding Author}

Submitted: March 14, 2024; Published: April 23, 2024

DOI: http://dx.doi.org/10.30865/mib.v8i2.7558

Abstract

The number of women's violence in Indonesia is increasing. In West Java alone, 58,395 cases of violence against women were recorded. Violence against women that occurs in West Java is among the most common compared to other provinces. This high number shows that violence against women is still not being handled seriously. Therefore, clustering is carried out to achieve a more structured solution so that it can assist the government in providing appropriate and appropriate responses to the conditions of each region, so that case handling can be more focused. The aim of this research is to group districts or cities in West Java in cases of violence against women using the K-Means and K-Medoids algorithms into two clusters, namely, high and low. In this research, data grouping was carried out using 2 methods, namely the K-Means and K-Medoids algorithms to find out which comparison between the two algorithms is more optimal. It is hoped that this research will produce the best cluster, the results of this cluster can help the government and related agencies to determine which districts or cities should be prioritized in handling cases of violence against women in West Java. The results of this research produced 2 clusters. Cluster 0 (high) and cluster 1 (low). The number of cluster 0 (high) is 14 districts and cities, while cluster 1 (low) is 13 districts and cities. Comparing the clustering evaluation between K-Means and K-Medoids, the best cluster evaluation value was obtained using the K-Medoids Algorithm with a Silhoutte Coefficient evaluation of 0.43, while the Davies Bouldin Index evaluation results showed the best cluster results using the K-Means Algorithm with a DBI value of 0.95.

Keywords

Clustering; K-Means; K-Medoids; Silhouette Coefficient; Davies Bouldin Index

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