ANALISIS ALGORITMA K-MEANS DAN FUZZY C-MEANS UNTUK PERSENTASE RATA‑RATA PENGELUARAN UNTUK MAKANAN DAN BUKAN MAKANAN

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Published: Apr 20, 2024
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Spending Percentage, K-Means, Fuzzy C-Means
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Fabian Darrell Widyadhana Reswara

Abstract

Data clustering is one of the important methods in data analysis, especially for understanding patterns and relationships in socio-economic data. This study compares the performance of K-Means and Fuzzy C-Means algorithms in clustering the percentage of average monthly per capita expenditure on food and non-food in urban areas. The data set consists of several urban areas with food and non-food expenditure variables as the main features. The performance of both algorithms was evaluated using metrics such as average silhouette value, computation time, and interpretation of cluster results. The results show that the K-Means algorithm overall produces better results than the Fuzzy C-Means algorithm. The largest average Silhouette Score is found in the K-Means and Fuzzy C-Means algorithm of 0.873 with cluster = 2. But in other clusters, the K-Means algorithm has better results.

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Vol. 19 No. 1 (2024): Jurnal Komputer dan Informatika
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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