Exploring Energy Data through Clustering: A Hyperparameter Approach to Mapping Indonesia's Primary Energy Supply

Agus Perdana Windarto, Yerika Puspa Rosanti, Mesran Mesran

Abstract


The rapid economic growth and population development in Indonesia have significantly increased the demand for energy, presenting complex challenges in managing the primary energy supply due to geographical variability and dispersed natural resources. This study addresses these challenges by applying clustering techniques with a hyperparameter approach to explore and map Indonesia's primary energy supply. The research contributes to the field by offering an effective method for analyzing energy data patterns and optimizing energy management. Secondary data on energy production, consumption, and distribution from reliable sources such as the Ministry of Energy and Mineral Resources were collected and analyzed. Various clustering algorithms, including K-Means, Fast K-Means, X-Means, and K-Medoids, were applied to identify energy supply patterns across different regions. The Davies-Bouldin Index was used to evaluate the effectiveness of the clustering algorithms. The results indicate that distance measures such as Euclidean Distance and Chebychev Distance consistently show excellent clustering performance. The study found that the choice of distance measure significantly impacts the clustering quality. The insights gained from this analysis provide valuable information for stakeholders involved in energy planning and policy-making, enhancing the efficiency and sustainability of energy management in Indonesia. This research establishes a foundation for further detailed and holistic energy data analysis, supporting better decision-making in energy planning and development.

Keywords


Energy Data; Clustering; Hyperparameter Tuning; K-Means; Indonesia Energy Supply; Davies-Bouldin Index; Data Analysis

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DOI: http://dx.doi.org/10.26555/jiteki.v10i2.29032

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