Analisis Faktor Penentu Profit Penjualan Mobil Menggunakan Algoritma Random Forest - Muhamad Fahrul Rozi; Mukhammad Fakhir Rizal (Bulletin of Computer Science Research, 2026)
The automotive industry has significant changes in recent years that have directly affected vehicle sales profitability. The objective of this study is to analyze the factors influencing car sales profit using the USA Car Sales dataset for the 2018–2024 period. The approach employed is a quantitative method based on machine learning using the random forest algorithm, which was selected for its ability to handle complex data and identify important variables contributing to profit. The analysis was conducted through several stages, including data preprocessing, model training, performance evaluation, and result interpretation using feature importance techniques. These stages aim to obtain an accurate model while providing a comprehensive understanding of the influence of each variable on car sales profit. The results indicate that several factors have a significant impact on car sales profit, including car brand, year of sale, and the number of units purchased in a single transaction. Car brand reflects market preferences and consumer segmentation, while the year of sale represents market trends and changing conditions over time. In addition, the number of units sold per transaction plays an important role in increasing total profit. These findings provide strategic insights for automotive companies in formulating more effective, adaptive, and data-driven sales strategies.