K-Mean Algorithm Analysis for Election Cluster Prediction

Sri Ngudi ST. M.Kom - Universitas Amikom Yogyakarta
Nazmun Khanom - University of Professionals Bangladesh
Yuli Astuti - Universitas Amikom Yogyakarta

Citation Format:

DOI: http://dx.doi.org/10.30630/joiv.7.1.1107


The election is one of the democratic parties held in every country. The K-Means method is an algorithm that can use to predict nonvoters' areas in election activities. The prediction of the election pattern of the nonvoters area is important to do for early mitigation in the drafting of election budgeting. The last election party in the Margokaton Seyegan, Yogyakarta, Indonesia which have more than people who do join the election party in 2021.  Many budgeting is a waste because of it. Based on it, important to predict the clusters in this area using the K-mean algorithm. Based on the several studies where using K-mean is no one which implements for election prediction. This study will identify the pattern of voters of the election using the K-means algorithm. The parameters of data in this study are the list of voters, Unused voter's letters, and the sum of nonvoters. The cluster evaluation uses the Davies-Bouldin Index. The data was taken from the Indonesia Ministry of Internal Affairs in 2021 and processed using the RapidMiner tool. The study result is there are 96 nonvoters and the number of unused voter letters is 391. This means that several areas which in Cluster 3 need attention. In the next election party, this case does not happen, and the budget is not wasted. The evaluation of clusters value is 2.04. This value indicates that the clustering using K-means is recommended for this case, this is indicated by the DBI value which is close to 0. 


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