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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Medika Teknika : Jurnal Teknik Elektromedik Indonesia
Apza, Rechi Yudha
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Pengembangan Sistem Pengukuran Tingkat Stres Menggunakan Sensor GSR dengan Perbandingan Metode PSS Apza, Rechi Yudha; Surakusumah, Rino Ferdian
Medika Teknika : Jurnal Teknik Elektromedik Indonesia Vol. 6 No. 2 (2025): April
Publisher : Universitas Muhammadiyah Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/mt.v6i2.23710

Abstract

Stres merupakan faktor yang dapat mempengaruhi kesejahteraan dan kinerja akademik mahasiswa, sehingga diperlukan metode pengukuran yang akurat dan praktis. Penelitian ini bertujuan untuk mengembangkan sistem pengukuran tingkat stres berbasis sensor Galvanic Skin Response (GSR) yang terintegrasi dengan Internet of Things (IoT) dan membandingkan hasilnya dengan metode Perceived Stress Scale (PSS). Sistem yang dikembangkan memungkinkan pemantauan stres secara real-time melalui aplikasi smartphone. Metode penelitian menggunakan pendekatan kuantitatif dengan tahap perancangan, pengembangan, serta evaluasi validitas dan reliabilitas sistem. Pengujian dilakukan terhadap 20 mahasiswa semester akhir, dengan hasil menunjukkan bahwa 35% responden dalam kondisi normal, 35% mengalami stres ringan, 10% stres sedang, 5% stres berat, dan 15% mengalami error dalam pengukuran. Perbandingan antara sensor GSR dan metode PSS menunjukkan tingkat kesesuaian sebesar 83,33%, dengan rata-rata selisih nilai sebesar 16,67%, di mana metode PSS cenderung memberikan skor stres yang lebih tinggi dibandingkan sensor GSR. Selain itu, evaluasi usability menunjukkan bahwa sistem memiliki tingkat kepuasan pengguna yang tinggi, dengan skor rata-rata usability 4,48, simplicity 4,35, dan interactivity 4,31 dari skala 5. Kesimpulannya, sistem berbasis sensor GSR yang dikembangkan telah terbukti dapat mengukur tingkat stres dengan tingkat akurasi yang cukup baik dan memiliki potensi sebagai alat pemantauan stres yang objektif, praktis, serta mudah digunakan.
Adaptive Stress Prediction with GSR, SMOTE Balancing, and Random Forest Models Surakusumah, Rino Ferdian; Apza, Rechi Yudha
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6588

Abstract

Stress is a pervasive condition that affects mental health, productivity, and quality of life across populations. Traditional methods for stress assessment, such as the Perceived Stress Scale (PSS), rely on retrospective self-reporting and are limited by subjectivity and delayed feedback. To address this gap, this study developed an integrated real-time stress monitoring system combining Galvanic Skin Response (GSR) sensors, Internet of Things (IoT) technology, and machine learning algorithms. Primary GSR data were collected from 30 participants under varied conditions, supplemented by secondary data from the WESAD dataset. A Random Forest classifier was employed to categorize stress into four levels: normal, mild, moderate, and severe. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied, leading to improved model robustness. The system achieved a cross-validated classification accuracy of 69%, with substantial improvements in the detection of moderate and severe stress cases compared to traditional threshold-based methods. A strong agreement (Cohen’s Kappa κ = 0.82) was observed between system predictions and PSS-based stress assessments. Feature importance analysis identified mean GSR value and Skin Conductance Response (SCR) amplitude as the most influential indicators of stress. The system was evaluated for usability, receiving high user ratings in terms of accessibility, simplicity, and interactivity. A simple Python-based command-line interface (CLI) was also developed for real-time stress prediction based on input features. This research demonstrates the feasibility and effectiveness of combining physiological sensing, predictive analytics, and user-friendly interfaces to enable scalable and adaptive stress monitoring. Future developments will focus on integrating additional physiological modalities and deep learning techniques to enhance predictive performance and personalization in clinical and everyday contexts.
Co-Authors Rino Ferdian Surakusumah