Prediction of the Number of Patient Visits in a Psychiatric Hospital Prof. Dr. M. Ildrem Using Naive Bayesian Algorithm - Syahputra, Zidhane; Kurniawan, Rakhmat (Journal of Computer Networks, Architecture and High Performance Computing, 2025)
This study was conducted to predict the number of patient visits at Prof. Dr. M. Ildrem Mental Hospital using the Naive Bayes algorithm, which is relevant given the increasing need for global mental health care. The main problem of this study is the difficulty in managing hospital resources efficiently due to unpredictable fluctuations in the number of patient visits. The research aims to apply the Naive Bayes algorithm to predict the number of patient visits and evaluate their performance. The method used is a naïve Bayes algorithm with systematic steps including historical data collection, data preprocessing using LabelEncoder, and dividing the dataset into training data and test data (80:20) where the training data totals 1331 data and the test data has 333 data. The Naive Bayes model is built and tested with metrics such as accuracy, precision, recall, and F1-score. The results of the study based on confusion matrix analysis, the model achieved an accuracy of 0.8108108108108109 or 81%, a precision of 0.8206686930091185 or 82.07%, a recall value of 0.9926470588235294 or 99.26%, and an F1-score of 0.90 or 90%, which shows that this model is quite effective in predicting service units with the dominance of adolescent category patient data where it is concluded that this prediction model is able to provide accurate estimates of patient visits,  supporting the management of hospital resources, and improving the operational efficiency of mental health services. This research is expected to help hospitals in planning facilities and workforce more effectively.