ANALISIS PERBANDINGAN ALGORITMA K-MEANS DAN FUZZY C-MEANS DALAM PENGELOMPOKAN TIPE INFLASI KOTA DI INDONESIA
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Abstract
This study analyzes the comparison between the K-Means and Fuzzy C-Means algorithms in clustering cities in Indonesia based on types of inflation. The dataset used was obtained from the Central Bureau of Statistics (BPS), covering 10 types of inflation from January to October 2024, with a total of 5,371 data rows. A heatmap visualization was used for feature selection, resulting in six columns trained in the model. Both algorithms were tested in two scenarios: with and without outlier handling. The evaluation was conducted using the Silhouette Score and Davies-Bouldin Index to assess clustering quality. The results show that K-Means without outlier handling at n-cluster 6 achieved the best performance with a Silhouette score of 0.2493 and a DBI of 1.2061, indicating good cluster separation. Meanwhile, for the Fuzzy C-Means model, the best scenario without outlier handling was found at n-cluster 4. This study indicates that the scenario without outlier handling produces more stable clustering in K-Means compared to Fuzzy C-Means. These findings are expected to serve as a reference for the application of clustering methods for inflation analysis in Indonesia, supporting more precise economic policy-making.
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