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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang
Muhammad Yafi
Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Mulawarman University, Samarinda
Decision Sciences, Operations Research & Management

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K-MEDOIDS ALGORITHM CLUSTERING WITH PRINCIPAL COMPONENT ANALYSIS (PCA) (CASE STUDY: DISTRICTS/CITIES ON THE BORNEO ISLAND BASED ON POVERTY INDICATORS IN 2021) Muhammad Yafi; Rito Goejantoro; Andrea Tri Rian Dani
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 11, No 2 (2023): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.11.2.2023.31-43

Abstract

Cluster analysis is a technique in data mining that aims to group data (object) based on the information in the data. This research is used a non-hierarchical grouping named K-Medoids algorithm to group districts/cities in Borneo island based on poverty indicators and Principal Component Analysis (PCA) method to reduce research variable. This research is also do a cluster validity test to see how many cluster there are has the best grouping result using Silhouette Coefficient (SC) method. Based on the results of the analysis there is 3 optimal Principal Component (PC) were obtained with eigen value criteria of greater than or equal to 1. Furthermore, districts/cities on Borneo island were grouped based on the PC that formed and obtained 2 optimal clusters with an SC value of 0.61. The K-Medoids algorithm obtain 2 cluster, cluster 1 consisting of 49 districts/cities and cluster 2 consisting of 7 cities.
Co-Authors Andrea Tri Rian Dani Rito Goejantoro, Rito