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Nusantara Jurnal of Computer Application
Articles
DOI: 10.47679/njca.v1i1.4
Submitted: 2022-01-24
Published: 2022-01-24

Comparisonal Analysis of K-Means and Fuzzy . Algorithm C-Means in Clasterization of Poor Population Data at Rawa Makmur

Information Study Program, Faculty Of Computer Science, Dehasen University, Bengkulu
Information Study Program, Faculty Of Computer Science, Dehasen University, Bengkulu
Information Study Program, Faculty Of Computer Science, Dehasen University, Bengkulu
Analysis Comparison K-Means Algorithm Fuzzy C-Means Algorithm Poor Population Data Kelurahan Rawa Makmur

Abstract

Rawa Makmur Village is one of the villages located in Muara Bangkahulu District, Bengkulu City. So far, the process of processing data on the poor at the Rawa Makmur Village Office is still done manually, namely by conducting a survey of each resident, and providing an assessment of whether the population is categorized as poor or not. However, because the data collection process is still manual, it is difficult for the Rawa Makmur Village to manage the data because it takes a long time. Therefore, a system development was carried out by creating applications that could simplify the process of managing data for the poor in Rawa Makmur Village. The application for clustering data for the poor in Rawa Makmur Village was made using the Visual Basic .Net programming language and SQL Server 2008 database by applying two data mining methods, namely the K-Means Algorithm and the Fuzzy C-Means Algorithm. The grouping is done based on the income and the number of the insured from the data obtained at the Rawa Makmur Village Office. Based on the processing time, the Fuzzy C-Means Algorithm is faster than the K-Means Algorithm in the data clustering process because the K-Means Algorithm has repeated iterations until the final cluster result is found while the Fuzzy C-Means does not have iterative iterations. Based on the results of clusters on the data of the poor as many as 440 people, there are differences in cluster results where the K-Means Algorithm has Cluster 1 (65 people), Cluster 2 (334 people), Cluster 3 (41 people), while the Fuzzy C-Means Algorithm has Clusters. 1 (147 people), Cluster 2 (136 people), Cluster 3 (157 people). From the results of the comparative analysis between the K-Means Algorithm and the Fuzzy C-Means Algorithm, it is found that the K-Means Algorithm is more recommended for classifying poor population data than the Fuzzy C-Means Algorithm because the average value of each cluster does not differ too much, and is still significant according to with data on the poor

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How to Cite

Ilham, M., Suranti, D., & Fredricka, J. (2022). Comparisonal Analysis of K-Means and Fuzzy . Algorithm C-Means in Clasterization of Poor Population Data at Rawa Makmur. Nusantara Jurnal of Computer Application, 1(1), 11–16. https://doi.org/10.47679/njca.v1i1.4