The application of unsupervised learning techniques to the clustering method in use of cell phones in Indonesia

Abstract

The research objective was to apply unsupervised learning techniques by comparing several clustering methods in data mining to determine the best method for cell phone use cases in Indonesia. The data source comes from the Central Bureau of Statistics with the website url https://www.bps.go.id/. The data category used is the percentage of the population using cellular phones by province from 2012&#x2013;2018 (34 records). The attributes used are the name of the province, the percentage (&#x0025;) of the population using cellular telephones in urban areas and the percentage (&#x0025;) of the population using cellular phones in rural areas. The unsupervised learning methods compared were k-medoids and k-means using the T - Test validity test based on the Davies Bouldin Index (DBI) to evaluate the cluster model. The analysis process uses Rapid Miner software. The mapping label is in the form of the cluster used, namely the high cluster (K1) for cellular phone use and the low cluster (K2) for cellular phone use. The best mapping result for the use of cell phones in urban areas is the k-medoids method with the T - Test result = 0.526 (DBI with <tex>$\mathbf{k}=2$</tex>) where 26 provinces <tex>$(\mathbf{K}1=\mathbf{cluster}_{-}1)$</tex> and 8 provinces <tex>$(\mathbf{K}2\ =\mathbf{cluster}_{-}0)$</tex> &#x2022; Meanwhile, the best mapping result for the use of cellular phones in rural areas is the k-medoids method with the results of T - Test <tex>$=0.431$</tex> (DBI with k <tex>$=2)$</tex> where 31 provinces <tex>$(\mathbf{K}1=\mathbf{cluster}_{-}0)$</tex> and 2 provinces (K2 <tex>$= \mathbf{cluster}_{-}1)$</tex>. Based on the mapping results, the mastery of cellular telephones in rural areas (2 &#x0025;) and in urban areas (24 &#x0025;) means that the understanding of the use of cellular telephones in Indonesia is very good. Overall, the k-medoid method is better at mapping cases of cell phone use in Indonesia. Based on the results, it is expected to be information for the government considering that Indonesia has the opportunity to grow very fast and large with the support of the government so that Indonesia&#x0027;s digital industry can overcome the lag behind other countries.