Penerapan Data Mining untuk Prediksi Evaluasi Mobil dengan Metode Decision Tree

Abstract

The evaluation of motor vehicle acceptability is a multicriteria classification problem involving a complex set of qualitative attributes. This research aims to apply Data Mining techniques using the C4.5 Decision Tree Algorithm to predict the car evaluation outcome (Car Evaluation Dataset), categorized into unacceptable, acceptable, good, and very good classes. The classification model was constructed based on six categorical input attributes and implemented using RapidMiner Studio software with a 10-Fold Cross Validation scheme. The main objectives of this study were to measure the model's accuracy and to identify the priority sequence of attributes most influential in determining the car evaluation class. The test results show that the C4.5 model achieved an accuracy level of 92.94%. Furthermore, the model identified the Safety attribute as the most dominant factor affecting the evaluation outcome, followed by Persont These findings validate the effectiveness of the Decision Tree Algorithm in providing a predictive and interpretive solution for complex multicriteria scoring systems.