Analisis Kinerja Algoritma Naive Bayes dalam Klasifikasi Data pada Pasien Tuberkulosis Berbasis Data Mining - Ulumuddin, Ulumuddin; Widodo, Pudji (Forum Kerjasama Pendidikan Tinggi (FKPT), 2025)
Tuberculosis (TB) is one of the infectious diseases that remains a major public health problem in Indonesia, particularly at the primary healthcare level such as public health centers. The increasing amount of patient data stored in health information systems requires effective analytical methods to support accurate and efficient decision-making. This study aims to analyze the performance of the Naive Bayes algorithm in classifying tuberculosis patient data. The dataset used in this research was obtained from medical records of TB patients and non-TB patients, which were processed through several preprocessing stages, including data cleaning, data integration, data transformation, and normalization to ensure data quality. The data were then divided into training and testing datasets for classification purposes. The Naive Bayes algorithm was implemented to classify patient status based on selected clinical and demographic attributes. Model performance was evaluated using a confusion matrix and several evaluation metrics, including accuracy, precision, recall, and F1-score. The experimental results show that the Naive Bayes algorithm achieves satisfactory performance in classifying tuberculosis patient data and demonstrates good efficiency when applied to real-world healthcare data. However, the algorithm still has limitations related to the assumption of independence among attributes, which may affect classification accuracy. The findings of this study are expected to contribute to the development of a decision support system that can assist healthcare professionals at public health centers in performing early classification and analysis of tuberculosis patient data more effectively and efficiently.