Penerapan Data Mining Model Algoritma C4.5 Dan Naïve Bayes Pada Diagnosis Awal Penyakit Hipertensi - Novhirtamely Kahar; Gustina Gustina; Widia Widia (JURNAL AKADEMIKA, 2026)
Hypertension is a global health problem whose prevalence continues to increase, including in Jambi Province. Early detection of hypertension is crucial to prevent serious complications. This study aims to compare the performance of the C4.5 and Naïve Bayes algorithms in the early diagnosis of hypertension using patient data from Simpang Kawat Community Health Center, Jambi. The data used are hypertension patient data consisting of 200 training data and 50 test data with variables such as age, gender, smoking, BMI, cholesterol levels and blood pressure. The research methods include data collection, data cleaning, algorithm implementation using RapidMiner, and performance evaluation based on accuracy, precision, recall, and F1-score. The results show that the Naïve Bayes algorithm achieved the highest accuracy of 90%, precision of 93.48% and recall of 95.56%. Meanwhile, the C4.5 algorithm achieved 86% accuracy, precision of 91.30% and recall of 93.33%. The Naïve Bayes algorithm demonstrated superior performance in predicting hypertension based on the tested data, both on data with independent attributes, and tended to provide stable accuracy results. Meanwhile, the C4.5 algorithm produced an easily understood model due to its systematic and easily explained decision tree structure. The results of this study concluded that the Naïve Bayes algorithm is more effective for the early diagnosis of hypertension.