International Journal of Electrical and Computer Engineering (IJECE) Vol. 13, No. 3, June 2023, pp. 3266~3280 ISSN: 2088-8708, DOI: 10.11591/ijece.v13i3.pp3266-3280  3266 Journal homepage: http://ijece.iaescore.com Initial location selection of electric vehicles charging infrastructure in urban city through clustering algorithm Handrea Bernando Tambunan, Ruly Bayu Sitanggang, Muhammad Muslih Mafruddin, Oksa Prasetyawan, Kensianesi, Istiqomah, Nur Cahyo, Fefria Tanbar PT PLN (Persero) Research Institute, Jakarta, Indonesia Article Info ABSTRACT Article history: Received May 23, 2022 Revised Sep 16, 2022 Accepted Oct 1, 2022 Transportation is one of the critical sectors worldwide, mainly based on fossil fuels, especially internal combustion engines. In a developing country, heightened dependence on fossil fuels affected energy sustainability issues, greenhouse gas emissions, and increasing state budget allocation towards fuel subsidies. Moreover, shifting to electric vehicles (EVs) with alternative energy, primely renewable energy sources, is considered a promising alternative to decreasing dependence on fossil fuel consumption. The availability of a sufficient EV charging station infrastructure is determined as an appropriate strategy and rudimentary requirement to optimize the growth of EV users, especially in urban cities. This study aims to utilize the k-mean algorithm’s clustering method to group and select a potential EV charging station location in Jakarta an urban city in Indonesia. This study proposed a method for advancing the layout location’s comprehensive suitability. An iterative procedure determines the most suitable value for K as centroids. The K value is evaluated by cluster silhouette coefficient scores to acquire the optimized numeral of clusters. The results show that 95 potential locations are divided into 19 different groups. The suggested initial EV charging station location was selected and validated by silhouette coefficient scores. This research also presents the maps of the initially selected locations and clustering. Keywords: Charging station infrastructure Clustering Electric vehicles K-means Location selection Silhouette scores Urban city This is an open access article under the CC BY-SA license. Corresponding Author: Handrea Bernando Tambunan Transmission and Distribution Research Division, PT. PLN (Persero) Research Institute Jakarta, Indonesia Email: handrea.bernando.t@gmail.com 1. INTRODUCTION In several sectors, primary energy sources, e.g., transportation, household, industry, commercial, power plant, and others (construction, agriculture, and mining), are mainly based on fossil fuels [1]. In a developing country, heightened dependence on fossil fuels affected energy sustainability issues, greenhouse gas emissions, and increasing state budget allocation toward fuel subsidies [2]. Especially the transportation sector has consumed elevated fossil fuels and contributes to a significant consequence to the environment. Moving to an alternative energy source in transportation could decrease carbon emissions. The energy transition to renewable energy sources (RES) has become a global issue in response to managing the threat of greenhouse gas emissions [3]. The electric vehicle (EV) is considered a promising option to minimize environmental impact and decrease addiction to fossil fuel consumption at once. Nowadays, EV penetration and adoption in Indonesia as a developing country is very early. On the other hand, EV in Indonesia has great potential to mitigate greenhouse gas emissions and improve energy