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All Journal Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi
Passa, Rahma Satila
Universitas Sriwijaya
Computer Science & IT

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Machine Learning Classification of Insomnia Using Multidimensional Features and SMOTE Novanza, Trional; Ramadhan, Mgs. M. Luthfi; Caryn, Femilia Hardina; Passa, Rahma Satila; Iqrom, Redho Aidil; Seftianto, Ferlian; Hardoni, Andre; Baturohmah, Habi
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3372

Abstract

Sleep disorders, especially insomnia, are common among adolescents and negatively affect health. Early detection is crucial for appropriate treatment. This study aims to classify insomnia severity in adolescents using a Machine Learning (ML) model and multidimensional features derived from 19 questionnaire instruments. The dataset consists of 95 adolescents aged 16–19 years, categorized into Insomnia, Subclinical Insomnia, and Control classes. The modeling process includes reducing multicollinearity, class balancing with SMOTE, and hyperparameter optimization using GridSearchCV and StratifiedKFold. Feature importance analysis was conducted using decision tree-based methods and permutation importance. The results show that SMOTE improves SVM performance from 0.690 to 0.793 and positively affects Random Forest. Logistic Regression performs best without SMOTE (accuracy 0.759), while XGBoost shows the lowest accuracy (0.614) even with SMOTE. A total of 11 features consistently contribute to all models. In conclusion, ML models, particularly SVM, are effective for classifying insomnia severity in adolescents. 
Co-Authors Baturohmah, Habi Caryn, Femilia Hardina Hardoni, Andre Iqrom, Redho Aidil Novanza, Trional Ramadhan, Mgs. M. Luthfi Seftianto, Ferlian