Decision Tree Modeling Using the ID3 Algorithm in a Data Mining Approach

Abstract

The rapid development of information technology has encouraged the use of data mining as a foundation for data-driven decision-making across various sectors, including the karaoke entertainment industry. This study aims to evaluate the performance of the ID3 algorithm in supporting decision support systems through the construction of a decision tree–based classification model. The research method employs the Knowledge Discovery in Databases (KDD) approach, which involves data selection, data transformation, modeling using the ID3 algorithm, and evaluation of decision outcomes. The performance of the method was evaluated based on five key aspects: decision-making capability, classification processing speed, classification result stability, model interpretability, and suitability to user needs. The results indicate that the ID3 algorithm achieved an average success rate of 92%, with the highest performance observed in processing speed and classification stability. These findings demonstrate that the ID3 algorithm is effective, efficient, and highly interpretable, making it suitable for implementation as a classification method in data mining–based decision support systems.