KLASIFIKASI TIPE MOBIL LISTRIK MENGGUNAKAN ALGORITMA DECISION TREE: STUDI PADA DATA SET PENJUALAN

This study explores the use of the Decision Tree algorithm (using the CART approach) to classify electric vehicle body types based on sales data. The model is trained using structured data with 23 features and evaluated using accuracy, precision, recall, and F1-score metrics. It aims to classify electric vehicle body types based on the technical specifications available in the sales dataset. Leveraging machine learning techniques, the model was developed and tested using Google Colab and popular Python libraries, including scikit-learn and pandas. The dataset comprises over 60 observations and more than 20 features related to vehicle performance and dimensions. The test results show an accuracy rate of up to 92%, with stable classification performance across several classes, such as SUV and Hatchback. The model was built using a supervised learning approach, with an 80% training and 20% testing data split, and a decision tree parameter setting of max_depth = 5.