Penerapan Algoritma K-Means Untuk Menganalisis Berdasarkan Jenis Dan Penyebab Kematian Di Jawa Barat
DOI:
https://doi.org/10.55606/jitek.v5i2.7148Keywords:
West Java Province, clustering, K-Means, Metode Elbow, Data miningAbstract
West Java Province is one of the regions in Indonesia with fluctuating mortality rates each year. The high number of deaths in various areas, with diverse characteristics of death types and causes, encourages the need for in-depth analysis to map the pattern of mortality cases. This study aims to analyze mortality data in West Java Province based on the type of death, cause of death, and number of cases using the K-Means Clustering method. The data used in this study is secondary data obtained from the official Open Data Jawa Barat portal for the period 2019 to 2023, with a total of 506 mortality records that have undergone pre-processing and data transformation stages. The analysis process was carried out through clustering using the K-Means algorithm, with the optimal number of clusters determined by the Elbow Method, and the clustering quality evaluated using the Silhouette Score. The results showed that the mortality data could be grouped into three main clusters, each with different characteristics in terms of the number of deaths, types, and causes. Cluster 0 consists of data with a high number of deaths, mostly occurring in urban areas. Cluster 1 contains data with moderate numbers and a variety of causes, while Cluster 2 represents areas with low mortality rates spread across several districts. The obtained Silhouette Score value of 0.52 indicates that the quality of cluster separation is quite good. In addition to clustering results, data visualization showed that Dengue Fever has been the dominant cause of death in the last three years, with Cianjur Regency recorded as the region with the highest mortality cases. This study is expected to serve as a reference for formulating public health policies and mortality prevention strategies in West Java Province.
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