Prediksi Tingkat Kepuasan Pasien Fisioterapi Menggunakan Algoritma Naive Bayes - Kaila Nazuwa; Indra Bagoes Mu’afa; Muhamad Firly; Ahmad Taher; Refo Altalario Bintang Anugrah; Maulana Fansyuri (Journal of Information Technology and Informatics Engineering, 2025)
This study aims to predict patient satisfaction levels in physiotherapy services using the Naive Bayes algorithm. Patient satisfaction is a key indicator of healthcare service quality, and this prediction is based on attributes such as age, gender, session duration, and therapist expertise. The dataset, consisting of 31 entries, was analyzed using RapidMiner software. The classification process applied the Naive Bayes model, known for its simplicity, computational efficiency, and strong performance even with limited data. Evaluation results showed an accuracy rate of 90%, with balanced precision and recall between the "satisfied" and "dissatisfied" categories. These find-ings demonstrate that data mining techniques can serve as valuable tools to support continuous improvement in physiotherapy service quality.