<![CDATA[Abstract: The development of electric vehicles in Indonesia continues to grow in line with global efforts to reduce carbon emissions. However, the limited and uneven distribution of public electric vehicle charging stations (SPKLU) remains a major obstacle. This study aims to analyze effective and efficient electric vehicle charging patterns using the K-Means Clustering method. The dataset was obtained from the Kaggle platform, containing attributes such as time, duration, charging level, and location. The research process includes data collection, cleaning, normalization, model training, and testing. The results show that the K-Means method successfully groups charging patterns with good cluster clarity. The developed web-based system also simplifies the analysis of SPKLU usage patterns automatically and efficiently. This research is expected to serve as a reference for planning electric vehicle charging infrastructure in Indonesia and to expand the application of machine learning in the field of Information Technology Engineering. Keywords: K-Means Clustering, Electric Vehicle, Charging Pattern, SPKLU, Machine Learning. Abstrak: Perkembangan kendaraan listrik di Indonesia terus meningkat seiring upaya global mengurangi emisi karbon. Namun keterbatasan dan ketidakteraturan stasiun pengisian kendaraan listrik umum (SPKLU) masih menjadi kendala utama. Penelitian ini bertujuan menganalisis pola pengisian kendaraan listrik yang efektif dan efisien menggunakan metode K-Means Clustering. Data diperoleh dari platform Kaggle dengan atribut waktu, durasi, tingkat, dan lokasi pengisian. Proses penelitian meliputi pengumpulan, pembersihan, normalisasi, pelatihan, serta pengujian model. Hasil menunjukkan bahwa metode K-Means mampu mengelompokkan pola pengisian dengan tingkat kejelasan cluster yang baik. Sistem berbasis web yang dikembangkan juga mempermudah analisis pola penggunaan SPKLU secara otomatis dan efisien. Penelitian ini diharapkan menjadi acuan dalam perencanaan infrastruktur pengisian kendaraan listrik di Indonesia serta memperluas penerapan machine learning di bidang Teknik Informatika. Keyword: K-Means Clustering, Kendaraan Listrik, Pola Pengisian, SPKLU, Machine Learning. ]]>
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