Deteksi Gangguan Tidur Menggunakan Support Vector Machine pada Aplikasi Web Streamlit - Satria Dava Riansa; Aria Hendrawan (JOINS (Journal of Information System), 2026)
Sleep disorders are health problems that may affect an individual’s physical condition, mental well-being, and daily productivity. These conditions can be influenced by lifestyle and physiological factors, such as sleep duration, sleep quality, stress level, physical activity, heart rate, and blood pressure. This study aims to apply the Support Vector Machine (SVM) method to classify sleep disorders into three categories, namely normal, insomnia, and sleep apnea, as well as to develop a Streamlit-based web application to support interactive prediction. The dataset used in this study is the Sleep Health and Lifestyle dataset obtained from Kaggle. The research stages include data preprocessing, normalization using StandardScaler, model training using SVM and five comparison algorithms, and hyperparameter tuning to obtain the best performance. The evaluation results show that the SVM model with a poly kernel achieves an accuracy of 97.33% and a macro F1-score of 0.9569. The best model is then implemented into a web application that displays classification results along with the probability of each class, making it useful as an accessible early screening tool for sleep disorders.