Analysis of The Level of Satisfaction of Electric Bus Passengers in Medan Using C5.0 Algorithm

Abstract

The C5.0 method was successfully implemented in the analysis of passenger satisfaction level of Medan City Electric Bus using 500 passenger data divided into 70% for training data (350 data) and 30% for test data (150 data). The steps include creating a decision tree based on training data, where the model learns to identify patterns that affect passenger satisfaction. After the decision tree is formed, the model is tested using testing data to measure the prediction accuracy. The evaluation results show that the C5.0 model is able to classify the testing data effectively, providing an accurate picture of passenger satisfaction levels in the tested context. Based on manual calculations with 10 testing data against training data, the following prediction results are obtained: out of 10 testing data, the model predicts 7 data as Satisfied (satisfied) and 3 data as Dissatisfied (not satisfied). This result shows that the model managed to classify most of the testing data correctly, giving a positive indication of the accuracy and reliability of the model in identifying passenger satisfaction levels. Based on the evaluation results of the C5.0 model using R, the training data showed accuracy, precision, and recall each reached 100%. This indicates that the C5.0 model was perfectly successful in classifying all training data, with no errors in prediction or classification. This result confirms that the model is very effective and reliable in analyzing the satisfaction level of Medan City Electric Bus passengers, demonstrating its ability to provide accurate and consistent predictions.