Perbandingan Algoritma K-Means dan K-Medoids Untuk Pemetaan Daerah Penanganan Diare Pada Balita di Kabupaten Kuningan

Diarrhea is an endemic disease that contributes to the high mortality rate in Indonesia, especially among children under five. The Kuningan District Health Office had difficulties monitoring and supervising spread of diarrheal diseases. This study aims produce a mapping scheme priority areas handling prevention control five Regency. method used Data Mining Clustering by comparing two algorithms, namely K-Means algorithm K-Medoids algorithm. Determination optimum number clusters using Elbow Silhouette Coefficient methods. With this method, result 3 while 2 clusters. best cluster evaluation uses Davies-Bouldin Index (DBI) results show DBI value always smaller than either or clusters, shows better Based on these results, it recommended map for diseases with medium consisting 9 regions, regions low 25 regions. can be as input develop strategies preventing