Clustering of Clean Water Needs in Indonesia for the 2012-2017 Period Using the K-Means Algorithm

Daniati Uki Eka Saputri, Taopik Hidayat, Siti Masturoh

Abstract


The need for clean water is important to support all activities of human survival. Data from the Central Statistics Agency (BPS) in 2017 showed the highest number of clean water distribution in each province was only 72.04%. These data indicate that access to clean water to meet daily needs is still far from sufficient. This study aims to classify the need for clean water for the period 2012-2017 using the K-Means algorithm. The data source was obtained from the official BPS website, namely data on the volume of clean water distributed to each province in Indonesia in 2012-2017. The process of replacing missing values was carried out on the missing data, then the data were grouped into three clusters, namely low (C0) in 25 provinces, high (C1) in 4 provinces, and moderate (C2) in 5 provinces using the K-Means algorithm. The centroid value for the C0 cluster is 150588.24, the centroid data for the C1 cluster is 1939461, the centroid data for the C2 cluster is 857876.6. The results of the K-Means clustering were tested using the Davies Bouldin Index (DBI) Validation as many as 3 clusters with a value of 0.534, the cluster results were optimal because the DBI value was close to 0.

Keywords


Clustering; Clean Water; K-Means Method; Rapidminer; Davies Bouldin Index;

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DOI: http://dx.doi.org/10.30700/jst.v12i2.1241

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