Evaluasi Kinerja Naïve Bayes, Decision Tree, Dan Random Forest Serta Voting Ensemble Pada Klasifikasi Multi-Kelas Penyakit Sapi Berbasis Gejala

Abstract

Cattle are an important livestock commodity; however, farmers often face difficulties in disease diagnosis due to the similarity of clinical symptoms and limited access to veterinary experts. This study aims to compare the performance of three machine learning classification algorithms, namely Naïve Bayes, Decision Tree, and Random Forest, and to evaluate the effectiveness of an ensemble approach using a Voting Ensemble method for cattle disease diagnosis. The study adopts the CRISP-DM methodology, consisting of data preprocessing, modeling, and evaluation stages. Model performance is assessed using accuracy, precision, recall, and F1-score metrics. The experimental results show that Naïve Bayes achieves the best performance with an accuracy of 0.951 and an F1-score of 0.920. Random Forest obtains an accuracy of 0.799, while Decision Tree performs the lowest with an accuracy of 0.265. Ensemble methods, including Voting NB+RF, Voting Weighted, Voting Soft, and Voting Hard, achieve accuracies of 0.912, 0.900, 0.853, and 0.792, respectively. These findings indicate that Naïve Bayes is more suitable for high-dimensional and sparse symptom-based data, providing the most stable performance among the evaluated models. The developed system is implemented as a web-based expert system. Usability evaluation using the System Usability Scale (SUS) yields a score of 77, categorized as “Good.” This study demonstrates that machine learning can support decision-making in cattle disease diagnosis.