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Abstract
Open Unemployment Rate (OUR) data is a time series dataset that shows changes over time and often has similar patterns across regions. This study aims to cluster Indonesian provinces based on OUR change patterns using a whole-time-series clustering approach. Each province is treated as a single complete time series, and clustering is performed based on pattern similarities rather than only comparing values at specific time points. Two clustering methods are used: K-Medoids and Fuzzy K-Medoids. These methods are with two distance measures, Euclidean distance and Dynamic Time Warping (DTW), resulting in four clustering methods. DTW is applied to capture flexible pattern similarities in time series data, while Euclidean distance measures direct value differences. The quality of clustering results is evaluated using the silhouette coefficient. The results show that the Fuzzy K-Medoids – DTW combination with 2 clusters produces the highest silhouette score = 0.627, indicating the best clustering structure among all tested methods. These findings suggest that combining fuzzy clustering with DTW distance is more suitable for OUR data, which tend to have smooth changes and similar patterns across provinces.
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