Abdul Rachman Manga, Assyahrin Nanda, Anik Nur Handayani, Heru Wahyu Herwanto, Rosa Andrie Asmara, Dirgahayu Lantara
Data classification plays a crucial role in artificial intelligence, particularly in enhancing model accuracy. This study focuses on classifying Toraja buffalo, a livestock breed with significant cultural importance in South Sulawesi, Indonesia. While the Single Input Approach is commonly used for classification, it often fails to capture all the necessary attributes to effectively distinguish between racial traits. Therefore, this research aims to evaluate the effectiveness of a multi-input approach, which integrates multiple data inputs to improve classification performance compared to the Single Input method. We employed four classification techniques: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naïve Bayes, and Decision Tree, using both Single Input and Multi Input configurations. Model performance was assessed through Precision, Recall, F1 Score, and Accuracy metrics. The findings indicate that the Multi Input approach consistently outperformed the Single Input method. Notably, KNN achieved its best performance with Multi Input, recording an F1 Score of 0.7263 and an Accuracy of 0.7333, significantly surpassing the results obtained from Single Input. Similarly, SVM also demonstrated substantial performance enhancements with Multi Input. Overall, the study highlights the importance of incorporating a wider array of informative data to enhance the model's capability in accurately classifying specific categories, with KNN showing the most pronounced improvements. © 2025 IEEE.
Department of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia; Department of Electrical Engineering, Universitas Negeri Malang, Malang, Indonesia; Information Technology Department, State Polytechnic of Malang, Malang, Indonesia; Faculty of Industrial Engineering, Universitas Muslim Indonesia, Makassar, Indonesia
