A Data Mining Approach to Wage Inequality Analysis in Indonesia: A Clustering Study Using Fuzzy C-Means - Harwanti, Nur Achmey Selgi; Hendikawati, Putriaji; Sanusi, Ratna Nur Mustika; Pratama, Alfian Adi (Unnes Journal of Mathematics, 2024)
This study aims to cluster Indonesian provinces based on the average wage structure of workers across 17 economic sectors using the Fuzzy C-Means (FCM) method. The wage data underwent preprocessing steps including missing value imputation using the median, logarithmic transformation to reduce skewness, and Z-Score standardization to ensure uniform data scaling. The evaluation of the number of clusters and fuzziness values was conducted using the Silhouette coefficient and Fuzzy Partition Coefficient (FPC), with the best results achieved at three clusters and a fuzziness value of 1.3. Further analysis using Principal Component Analysis (PCA) provided visualization of the clusters, while radar charts illustrated wage characteristics by sector within each cluster. The clustering results reveal significant economic disparities among provinces: Cluster 1 consists of provinces with the highest wages dominated by high-value-added sectors such as mining and finance; Cluster 0 shows a balanced wage distribution reflecting a transitional economy; and Cluster 2 includes provinces with the lowest wages facing structural challenges. These findings offer a comprehensive overview of regional economic diversity in Indonesia and can serve as a basis for policy-making aimed at more equitable economic development.