K-MEANS CLUSTER COUNT OPTIMIZATION WITH SILHOUETTE INDEX VALIDATION AND DAVIES BOULDIN INDEX (CASE STUDY: COVERAGE OF PREGNANT WOMEN, CHILDBIRTH, AND POSTPARTUM HEALTH SERVICES IN INDONESIA IN 2020) - Utami, Iut Tri; Suryaningrum, Fahlevi; Ispriyanti, Dwi (BAREKENG: Jurnal Ilmu Matematika dan Terapan, 2023)
One of the causes of the increasing maternal mortality rate in Indonesia is the declining performance of maternal health services in each Indonesian province. To overcome the decline in performance, namely by determining in advance the provinces that need to be prioritized for services by grouping 34 provinces in Indonesia. This study aims to obtain the best provincial grouping results so that it can prioritize the right provinces. One of the methods that are suitable for grouping provinces is K-Means because it is simple and easy to implement. The disadvantage of K-Means is that it is sensitive to determining the right number of initial clusters, so Silhouette Index and Davies Bouldin Index validation is used to obtain the optimal number of clusters with stable and consistent results. This study used healthcare data for pregnant women, childbirth, and postpartum with K=2, 3, and 4 as the initial cluster number. K-Means objects are grouped in similarities using Euclidean and Manhattan distances. The result obtained was the optimal number of clusters with K=2 using Manhattan, where the highest Silhouette Index value was 0,658685 and the lowest Davies Bouldin Index was 0,3561214 which met the criteria for determining the optimal cluster.