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

Abstract

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

Klasterisasi Penyakit Berdasarkan Wilayah dengan Metode K-Means

“…Penelitian Firdaus, dkk mengelompokan penyakit di posyandu lansia [7]. Syamfithriani, dkk yang melakukan penelitian lain mengelompokkan lokasi penanganan diare pada balita [8]. Poningsih dkk melakukan penelitian tentang penularan penyakit ISPA di Riau [9], dan Agoestina dkk di Pematangsiantar [10].…”

Analisis Segmentasi Pelanggan Berbasis Recency Frequency Monetary (Rfm) Menggunakan Algoritma K-Means

“…[7] Data mining adalah aktivitas yang menggambarkan sebuah proses analisis yang terjadi secara iteratif pada database yang besar. [8] Istilah data mining disebut juga sebagai knowledge discovery in database (KDD) sering digunakan untuk menggambarkan proses penggalian informasi tersembunyi dari database besar. Oleh karena itu data mining digunakan untuk mengklasifikasikan data pelanggan dalam menentukan strategi penjualan.…”