Time Series Clustering Analysis for Increases Food Commodity Prices in Indonesia Based on K-Means Method

The global food crisis is perceived to have a significant impact on the national food sector. Time series clustering, a potent data mining technique, is employed to decipher and interpret intricate temporal patterns. Dynamic Time Warping (DTW), a measure that currently appears to be the most relevant, is predicated on the distance between sequences of elements. This paper explores the application of DTW in data mining algorithms to cluster commodity prices in Indonesia, aiming for enhanced accuracy based on time series movement. The clustering algorithm employs the K-Means method, necessitating a comprehensive description of the groups it forms. The analysis results reveal time series clustering for commodity prices using K-Means. Optimal results are achieved with five clusters, based on the commodity price trend. Influencing factors include seasonal variations and government policies related to consumer demand. It is imperative for the government to establish a robust market monitoring system to track commodity price fluctuations in real-time, thereby facilitating the design of effective price stabilization policies. The insights gleaned from this study can guide decision-makers in implementing targeted interventions to stabilize prices, bolster food security, and ensure sustainable economic growth.