Penerapan Random Forest untuk Klasifikasi Gangguan Tidur Berdasarkan Data Kesehatan dan Aktivitas Fisik
Keywords:
Gangguan Tidur, Pembelajaran Mesin, Random ForestAbstract
Gangguan tidur dapat menurunkan kualitas hidup dan berkaitan dengan berbagai masalah kesehatan serta gangguan fungsi sehari-hari. Penelitian ini bertujuan menerapkan algoritma Random Forest untuk mengklasifikasikan gangguan tidur berdasarkan data kesehatan dan aktivitas fisik serta mengimplementasikan model ke dalam aplikasi berbasis Android. Penelitian menggunakan data sekunder dari Sleep Health and Lifestyle Dataset. Dataset awal terdiri atas 374 rekam data; setelah tahap prapemrosesan dan penghapusan rekam data tanpa label gangguan tidur, diperoleh 155 rekam data yang memenuhi kriteria pemodelan, terdiri atas 78 Sleep Apnea dan 77 Insomnia. Data dibagi menjadi 124 data latih dan 31 data uji. Model Random Forest menggunakan 30 pohon keputusan dengan kedalaman maksimum 20. Kinerja model dievaluasi menggunakan akurasi, presisi, recall, F1-score, dan confusion matrix. Model mencapai akurasi 90,32%. Kelas Insomnia menghasilkan presisi 87,50%, recall 93,33%, dan F1-score 90,32%, sedangkan kelas Sleep Apnea menghasilkan presisi 93,33%, recall 87,50%, dan F1-score 90,32%. Analisis feature importance menunjukkan Physical Activity Level sebagai fitur paling berpengaruh, diikuti Daily Steps, Age, Blood Pressure, dan Heart Rate. Hasil penelitian menunjukkan bahwa Random Forest mampu mengklasifikasikan Insomnia dan Sleep Apnea dengan kinerja yang seimbang serta dapat diintegrasikan ke dalam aplikasi Android sebagai alat bantu analisis awal.
References
Adefemi Ayodele. (2023). A comparative study of ensemble learning techniques for imbalanced classification problems. World Journal of Advanced Research and Reviews, 19(2), 1633–1643. https://doi.org/10.30574/wjarr.2023.19.1.1202
An, X., Zhou, J., Xu, Q., Zhao, Z., & Li, W. (2025). Artificial intelligence in obstructive sleep apnea: A bibliometric analysis. In Digital Health (Vol. 11). SAGE Publications Inc. https://doi.org/10.1177/20552076251324446
Bhongade, A., & Gandhi, T. K. (2024). Predict-OSA: Integrative Multimodal-based Early Prediction of Sleep Apnea using Single-lead ECG Signal. https://doi.org/10.22541/au.173452558.89977628/v1
Dimitriadis, S. I., Salis, C. I., & Liparas, D. (2021). An automatic sleep disorder detection based on EEG cross-frequency coupling and random forest model. Journal of Neural Engineering, 18(4). https://doi.org/10.1088/1741-2552/abf773
Farrahi, V., & Rostami, M. (2024). Machine learning in physical activity, sedentary, and sleep behavior research. In Journal of Activity, Sedentary and Sleep Behaviors (Vol. 3, Number 1). BioMed Central Ltd. https://doi.org/10.1186/s44167-024-00045-9
Gao, L., & Ding, Y. (2020). Disease prediction via Bayesian hyperparameter optimization and ensemble learning. BMC Research Notes, 13(1). https://doi.org/10.1186/s13104-020-05050-0
Hamza, & Kebiri. (2023). Deep learning methods for diffusion MRI in early development of the human brain: resolution enhancement and model estimation Kebiri Hamza Kebiri Hamza, 2023, Deep learning methods for diffusion MRI in early development of the human brain: resolution enhancement and model estimation. http://serval.unil.chhttp://serval.unil.ch
Hidayat, I. A. (2023). Classification of Sleep Disorders Using Random Forest on Sleep Health and Lifestyle Dataset. Journal of Dinda Data Science, Information Technology, and Data Analytics, 3(2), 71–76. http://journal.ittelkom-pwt.ac.id/index.php/dinda
Khasanah, N., Uki Eka Saputri, D., Aziz, F., Hidayat, T., Nusa Mandiri Jl, U., & Author, C. (2025). Studi Perbandingan Algoritma Random Forest dan K-Nearest Neighbors (KNN) dalam Klasifikasi Gangguan Tidur. In Cipinang Melayu, Kec. Makasar (Vol. 5, Number 1). http://jurnal.bsi.ac.id/index.php/co-science
Kilic, O., Saylam, B., & Durmaz Incel, O. (2023). Sleep Quality Prediction from Wearables using Convolution Neural Networks and Ensemble Learning. ACM International Conference Proceeding Series, 116–120. https://doi.org/10.1145/3589883.3589900
Morin, C. M., Chen, S. J., Ivers, H., Beaulieu-Bonneau, S., Krystal, A. D., Guay, B., Bélanger, L., Cartwright, A., Simmons, B., Lamy, M., Busby, M., & Edinger, J. D. (2023). Effect of Psychological and Medication Therapies for Insomnia on Daytime Functions: A Randomized Clinical Trial. JAMA Network Open, 6(12). https://doi.org/10.1001/jamanetworkopen.2023.49638
ŞEVGİN, H. (2023). A comparative study of ensemble methods in the field of education: Bagging and Boosting algorithms. International Journal of Assessment Tools in Education, 10(3), 544–562. https://doi.org/10.21449/ijate.1167705
Sharaf, A. I. (2023). Sleep Apnea Detection Using Wavelet Scattering Transformation and Random Forest Classifier. Entropy, 25(3). https://doi.org/10.3390/e25030399
Shin, Y. W., Byun, J.-I., Kim, H.-J., & Jung, K.-Y. (2024). FORECAST-RBD: Forecasting Phenoconversion Risks and Its Clinical Phenotype in Patients with Isolated RBD Using Machine Learning and Explainable AI. https://doi.org/10.1101/2024.06.02.24308240
Skaramagkas, V., Kyprakis, I., Karanasiou, G. S., Fotiadis, D. I., & Tsiknakis, M. (2025). A Review on Deep Learning for Quality of Life Assessment Through the Use of Wearable Data. In IEEE Open Journal of Engineering in Medicine and Biology (Vol. 6, pp. 261–268). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/OJEMB.2025.3526457
Tanuku, S. R., & Tummala, V. (2023). Integration of Feature Selection Techniques using a Sleep Quality Dataset for Comparing Regression Algorithms. http://arxiv.org/abs/2303.02467
Widyastuty, W., & Azis, M. A. (2024). Classification and Evaluation of Sleep Disorders Using Random Forest Algorithm in Health and Lifestyle Dataset. Compiler, 13(1), 11. https://doi.org/10.28989/compiler.v13i1.2184
Zhang, C., Yu, L., Li, L., Zeng, P., & Zhang, X. (2024). Screening for moderate to severe obstructive sleep apnea by using heart rate variability features based on random forest algorithm. Sleep and Breathing, 28(6), 2521–2530. https://doi.org/10.1007/s11325-024-03151-9
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Kevin Jordan Moc, Huliman Huliman, Devi Devi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
