Penerapan Model Regresi Linier untuk Estimasi Distribusi Kendaraan Listrik Terhadap Kendaraan Konvensional - Denny Rosadi; Irfan Sulthoni; Valgy Alfiando; Dzamar Fawwaz; Dedi Martdiansyah; Ayyub Syahifulloh; Nur Aini Setiyawati (Jurnal Nasional Komputasi dan Teknologi Informasi, 2026)
Abstrak - Transisi energi global mendorong pergeseran signifikan dari kendaraan berbahan bakar fosil menuju kendaraan listrik, sehingga estimasi distribusi pasar antara kedua jenis kendaraan menjadi penting bagi pemangku kepentingan industri otomotif. Penelitian ini bertujuan menerapkan model regresi linier untuk mengestimasi distribusi kendaraan listrik (Battery Electric Vehicle) terhadap kendaraan konvensional (Internal Combustion Engine) berdasarkan data wholesales Gabungan Industri Kendaraan Bermotor Indonesia (Gaikindo) periode Januari-Desember 2025. Variabel independen (X) merupakan total distribusi kendaraan konvensional, sedangkan variabel dependen (Y) merupakan total distribusi kendaraan listrik. Perhitungan koefisien regresi dilakukan secara manual menggunakan Microsoft Excel dan diuji menggunakan perangkat lunak RapidMiner melalui operator Linear Regression. Hasil penelitian menunjukkan persamaan regresi Y = 84,50 + 0,059614364X yang mengindikasikan hubungan positif antara distribusi kendaraan konvensional dan kendaraan listrik selama periode pengamatan. Estimasi distribusi kendaraan listrik untuk tahun 2026 diperoleh melalui proyeksi variabel X menggunakan metode regresi time series, yang kemudian disubstitusikan ke dalam persamaan regresi linier. Hasil perhitungan manual dan Rapidminer menunjukkan nilai estimasi yang relatif konsisten, sehingga model ini dapat dijadikan salah satu metode pendukung pengambilan keputusan bagi produsen, pembuat kebijakan, dan investor dalam merancang strategi produksi dan distribusi kendaraan di tengah transisi energi sektor transportasi. Kata kunci : Estimasi; Gaikindo; Kendaraan Konvensional; Kendaraan Listrik; Microsoft Excel; Rapidminer; Regresi Linier;   Abstract - The global energy transition has driven a significant shift from fossil-fuel vehicles toward electric vehicles, making the estimation of market distribution between the two vehicle types important for stakeholders in the automotive industry. This study aims to apply a linear regression model to estimate the distribution of electric vehicles (Battery Electric Vehicles) relative to conventional vehicles (Internal Combustion Engine) based on wholesale data from the Association of Indonesian Automotive Industries (Gaikindo) for the period of January-December 2025. The independent variable (X) is the total distribution of conventional vehicles, while the dependent variable (Y) is the total distribution of electric vehicles. The regression coefficients were calculated manually using Microsoft Excel and validated using RapidMiner software through the Linear Regression operator. The results show the regression equation Y = 84.50 + 0.059614364X, indicating a positive relationship between the distribution of conventional and electric vehicles during the observation period. The estimated electric vehicle distribution for 2026 was obtained by projecting variable X using the time-series regression method, then substituting it into the linear regression equation. The manual calculation and RapidMiner results show relatively consistent estimation values, indicating that this model can serve as a decision-support method for manufacturers, policymakers, and investors in designing vehicle production and distribution strategies amid the energy transition in the transportation sector. Keywords: Conventional Vehicle; Electric Vehicle; Estimation; Gaikindo; Linear Regression; Microsoft Excel; Rapidminer;