ISSN: 2785-2997 Available online at www.HEFJournal.org Journal of Human, Earth, and Future Vol. 5, No. 3, September, 2024 319 Time Series Clustering Analysis for Increases Food Commodity Prices in Indonesia Based on K-Means Method M. Fariz Fadillah Mardianto 1* , N. Ramadhan Al Akhwal Siregar 1 , Steven Soewignjo 1 , F. Friska Rahmana Putri 1 , Hadi Prayogi 1 , Citra Imama 1 , Dita Amelia 1 , Sediono 1 , Deshinta Arrova Dewi 2 1 Department of Mathematics, Faculty of Science and Technology, Airlangga University, Surabaya, Indonesia. 2 Faculty of Information Technology, International University (INTI), Nilai, Malaysia. Received 05 March 2024; Revised 23 July 2024; Accepted 04 August 2024; Published 01 September 2024 Abstract 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. Keywords: Food Commodity Prices; Dynamic Time Wraping; K-Means; Sustainable Economy Growth; Time Series Clustering. 1. Introduction The issue of the 2023 global economic recession will make Indonesia faced with problems in achieving prosperity for its citizens. This issue was reinforced by the World Bank in its report, which predicts the possibility of a global economic recession in 2023 [1]. In this study, a time series clustering approach is used to predict food commodity prices in Indonesia, while the DTW approach is used to group provinces in Indonesia based on their economic growth as an effort to prevent and address economic recessions, especially those related to food commodity prices in the country. The high food and energy commodity prices influenced the supply chain to trigger inflation in several sectors; at the same time, Indonesia's inflation rate reached 5.71% year on year (YOY) [2]. Efforts to control inflation by several central banks around the world have generally increased the benchmark interest rate [3]. According to the Central Bureau of Statistics (BPS), there are three global phenomena that have triggered a spike in food commodity prices. First, climate change is affecting global food production. Second, the conditions after the * Corresponding author: m.fariz.fadillah.m@fst.unair.ac.id http://dx.doi.org/10. 28991/HEF-2024-05-03-02 ➢ This is an open access article under the CC-BY license (https://creativecommons.org/licenses/by/4.0/). © Authors retain all copyrights.