Use of Hierarchical Clustering Method with Complexity Invariant Distance on Provincial Rice Prices in Indonesia (Journal of Applied Statistics and Data Science, 2025)
Cluster analysis of time series data used to group objects based on their characteristics or data patterns. Rice is one of the main food commodities for the people of Indonesia, so it is necessary to monitor and control rice prices by the government by considering the characteristics of each province. This study aims to determine the characteristics of each province based on data patterns in clusters formed based on rice prices using time series data cluster analysis. The data used is monthly data on rice prices in 34 provinces in Indonesia from January 2020 - December 2022. Clustering of time series data is done by hierarchical cluster analysis with agglomerative methods consisting of single linkage, complete linkage, average linkage, ward's method, and centroid method. The distance used for cluster analysis of time series data is Complexity Invariant Distance (CID). Determination of many clusters using the silhouette coefficient and measurement of accuracy using the cophenetic correlation coefficient. The results of clustering provinces in Indonesia based on rice prices resulted in 3 clusters. The first cluster consists of 1 province categorized as a cluster with high rice prices and the highest complexity value, the second cluster consists of 12 provinces categorized as a cluster with high rice prices and the lowest complexity value, and the third cluster consists of 21 provinces categorized as a cluster with low rice prices and low complexity value.