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All Journal Jurnal Pendidikan Teknologi dan Kejuruan AdMathEdu : Jurnal Ilmiah Pendidikan Matematika, Ilmu Matematika dan Matematika Terapan Riau Journal of Computer Science Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pendidikan IPA (JPPIPA) INTECOMS: Journal of Information Technology and Computer Science JURNAL TEKNOLOGI DAN OPEN SOURCE HIKMAH : JURNAL PENDIDIKAN AGAMA ISLAM Journal of Robotics and Control (JRC) Jurnal Ilmu Komputer Journal of Applied Data Sciences JAIA - Journal of Artificial Intelligence and Applications Pengabdian Jurnal Abdimas SATIN - Sains dan Teknologi Informasi AMMA : Jurnal Pengabdian Masyarakat
Melyanti, Rika
Universitas Hang Tuah Pekanbaru
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Social Sciences Other

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Optimization of a New Adaptive Stacking Ensemble Model Integrated with IoT for Stress Level Detection Based on Physiological Signals Muhardi; Mohd Rinaldi Amartha; Rika Melyanti; Yuda Irawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.6770

Abstract

Mental health issues among college students are receiving increasing attention, particularly because of academic and social pressures and the impact of technology use. This study aims to develop a real-time stress level prediction model using a New Adaptive Stacking Ensemble approach based on physiological data and IoT devices. The data included heart rate, SpO₂, body temperature, and systolic and diastolic blood pressure. Five machine learning algorithms are used as base models: SVM, C4.5, Decision Tree, KNN, and Random Forest. The MLP serves as the meta-model, which is then optimized using Optuna. The model training process begins with pre-processing, feature standardization using StandardScaler, and data balancing using SMOTE. The results showed that the stacking model with the MLP meta-model achieved an accuracy of 90.00% under the individual Random Forest and KNN models, and increased to 97.00% after hyperparameter optimization. This model was then integrated with IoT devices using MAX30102, MLX90614, and digital tensiometer sensors, as well as a Streamlit interface to display real-time stress classification results. The system built not only excels in accuracy but can also be implemented to directly detect stress levels, thereby potentially supporting early intervention and mental health promotion in campus environments.
Co-Authors -, Herianto Ali Syahbana Ambiyar, Ambiyar Anita Febriani Daulay, Suandi Dedy Irfan Devi Yuliana Eka, Eka Sabna Fitri Ayu Fitri Ayu Fonda, Hendry Gunawan Henda, Zupri Herianto Herianto Herianto Herianto Hidayati Hidayati Ikhwanul Ikhsan Jenni Oinike Br Sitorus Mardainis Mardeni, Mardeni Mohd Rinaldi Amartha Muhaimin, Abdi Muhammad Iqbal Muhardi Muhardi . Muhardi Muhardi Muhardi Muhardi NUR YUSUF Perkasa, Reza Ponda, Hendry Rahmalisa, Uci Rahmiati Refni Wahyuni Reza Perkasa Riska Khairana Rizky Wahyu Syahputra Rofiqoh, Ummi Rohana Rohana Rohana, Rohana Sabna, Eka Saputra , Haris Tri Suhardi Suhardi Syahbana, Ali Ulia Ulfa Yose Andreas Yuda Irawan Yulanda, Yulanda