Deteksi Dini Stres Remaja Putri menggunakan Teknologi Sensor dan Aplikasi Web: Tinjauan Literatur Sistematis

Kharissa Rinandyawati, Ari Suwondo, Suryati Kumorowulan

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ABSTRACT

 

Stress among adolescent girls is a significant mental health issue, as it can affect their psychological, social, and academic well-being. Early detection of stress is a crucial step in preventive efforts, especially in school environments. With technological advancements, stress detection can now be conducted through non-invasive approaches using physiological sensors such as Galvanic Skin Response (GSR), heart rate, body temperature, and blood pressure, integrated into a web-based application system powered by the Internet of Things (IoT). This system enables real-time stress monitoring and provides users with light intervention recommendations. A systematic literature review was conducted on research articles published between 2019 and 2024, sourced from databases such as Google Scholar, IEEE Xplore, and PubMed. Articles were selected based on inclusion criteria, which involved the use of physiological sensors for stress detection, integration with digital applications, and a focus on adolescent populations. Based on the review, stress detection systems using physiological sensors and web-based applications have proven effective in identifying stress levels ranging from normal to moderate, with an average accuracy above 85%. Systems that utilize sensor combinations demonstrated higher performance compared to those relying on a single sensor type. Web applications also support data visualization and early intervention. The evaluation of early stress detection systems based on physiological sensors and web applications shows significant potential for monitoring stress in adolescent girls. Factors supporting the effectiveness of these systems include sensor accuracy, ease of integration into applications, and the ability to deliver personalized, data-driven recommendations. Further development is recommended to improve classification algorithms and broaden the system's applicability across a wider adolescent population.

 

Keywords: Early Detection, Adolescent Stress, Physiological Sensors, Web Application, Internet of Things.

 

 

ABSTRAK

 

Stres pada remaja putri merupakan isu kesehatan mental yang signifikan, karena dapat memengaruhi kesejahteraan psikologis, sosial, dan akademik. Deteksi dini tingkat stres menjadi langkah penting dalam upaya preventif, terutama di lingkungan sekolah. Dengan kemajuan teknologi, deteksi stres kini dapat dilakukan menggunakan pendekatan non-invasif berbasis sensor fisiologis seperti Galvanic Skin Response (GSR), detak jantung, suhu tubuh, dan tekanan darah, yang terintegrasi dalam sistem aplikasi web berbasis Internet of Things (IoT). Sistem ini memungkinkan pemantauan kondisi stres secara real-time dan memberikan rekomendasi intervensi ringan bagi pengguna. Tinjauan pustaka sistematis dilakukan artikel penelitian yang diterbitkan dalam rentang tahun 2019–2024, diperoleh dari database Google Scholar, IEEE Xplore, dan PubMed. Artikel dipilih berdasarkan kriteria inklusi yang meliputi penggunaan sensor fisiologis untuk deteksi stres, integrasi dengan aplikasi digital, serta fokus pada populasi remaja. Berdasarkan hasil kajian, sistem deteksi stres berbasis sensor dan aplikasi web terbukti efektif dalam mengidentifikasi stres kategori normal hingga sedang, dengan akurasi rata-rata di atas 85%. Sistem yang menggunakan kombinasi sensor menunjukkan performa lebih tinggi dibandingkan yang hanya mengandalkan satu jenis sensor. Aplikasi web juga terbukti mendukung visualisasi data dan intervensi awal. Evaluasi terhadap sistem deteksi dini tingkat stres berbasis sensor dan aplikasi web menunjukkan potensi besar dalam pemantauan stres remaja putri. Faktor-faktor yang mendukung efektivitas sistem ini meliputi: akurasi sensor, kemudahan integrasi ke aplikasi, serta kemampuan sistem dalam memberikan rekomendasi berbasis data secara personal. Pengembangan lebih lanjut disarankan pada peningkatan algoritma klasifikasi dan perluasan penggunaan pada populasi remaja yang lebih luas.

 

Kata Kunci: Deteksi Dini, Stres Remaja, Sensor Fisiologis, Aplikasi Web, Internet of Things.


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Abdulmalek, S., Nasir, A., Jabbar, W. A., Almuhaya, M. A. M., Bairagi, A. K., Khan, M. A. M., & Kee, S. H. (2022). IoT-Based Healthcare-Monitoring System towards Improving Quality of Life: A Review. In Healthcare (Switzerland) (Vol. 10, Issue 10). MDPI. https://doi.org/10.3390/healthcare10101993

Al-Atawi, A. A., Alyahyan, S., Alatawi, M. N., Sadad, T., Manzoor, T., Farooq-I-Azam, M., & Khan, Z. H. (2023). Stress Monitoring Using Machine Learning, IoT and Wearable Sensors. Sensors (Basel, Switzerland), 23(21). https://doi.org/10.3390/s23218875

Aqajari, S. A. H., Labbaf, S., Tran, P. H., Nguyen, B., Mehrabadi, M. A., Levorato, M., Dutt, N., & Rahmani, A. M. (2023). Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settings. http://arxiv.org/abs/2401.05367

Aziz Syhabudin, N., & Darmawan, B. (2024). Rancang Bangun Sistem Alat Ukur Heart Rate Dan Suhu Tubuh Menggunakan Esp 8266. Journal of Information System Research, 6(1), 459–468. https://doi.org/10.47065/josh.v6i1.6096

Basjaruddin, N. C., Syahbarudin, F., & Sutjiredjeki, E. (2021). Measurement device for stress level and vital sign based on sensor fusion. Healthcare Informatics Research, 27(1), 11–18. https://doi.org/10.4258/hir.2021.27.1.11

Chyan, P., & Kasmara, Y. (2021). Sistem Monitoring dan Deteksi Stres Pada Anak Berbasis Wearable Device. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(5), 943–949. https://doi.org/10.29207/resti.v5i5.3503

Deza, F., Madona, P., & Rahmardy, N. (2020). Alat Pendeteksi Tingkat Stress Manusia Berdasarkan Suhu Tubuh, Kelembaban Kulit, Tekanan Darah dan Detak Jantung (Vol. 3, Issue 2). http://jurnal.pcr.ac.id

Eisen, A. M., Bratman, G. N., & Olvera-Alvarez, H. A. (2024). Susceptibility to stress and nature exposure: Unveiling differential susceptibility to physical environments; a randomized controlled trial. PLoS ONE, 19(4 April). https://doi.org/10.1371/journal.pone.0301473

Ghosh, S., Kim, S. K., Ijaz, M. F., Singh, P. K., & Mahmud, M. (2022). Classification of Mental Stress from Wearable Physiological Sensors Using Image-Encoding-Based Deep Neural Network. Biosensors, 12(12). https://doi.org/10.3390/bios12121153

Giray, L., Nemeño, J., Braganaza, J., Lucero, S. M., & Bacarra, R. (2024a). A survey on digital device engagement, digital stress, and coping strategies among college students in the Philippines. International Journal of Adolescence and Youth, 29(1). https://doi.org/10.1080/02673843.2024.2371413

Giray, L., Nemeño, J., Braganaza, J., Lucero, S. M., & Bacarra, R. (2024b). A survey on digital device engagement, digital stress, and coping strategies among college students in the Philippines. International Journal of Adolescence and Youth, 29(1). https://doi.org/10.1080/02673843.2024.2371413

Gulo, N. R., & Sinaga, B. (2022). Journal of Intelligent Decision Support System (IDSS) Expert System for Diagnosis of Sexual Diseases (Paraphilia) Using Method Dempster Shafer. In Journal of Intelligent Decision Support System (IDSS) (Vol. 5, Issue 1).

Iqbal, T., Simpkin, A. J., Roshan, D., Glynn, N., Killilea, J., Walsh, J., Molloy, G., Ganly, S., Ryman, H., Coen, E., Elahi, A., Wijns, W., & Shahzad, A. (2022a). Stress Monitoring Using Wearable Sensors: A Pilot Study and Stress-Predict Dataset. Sensors, 22(21). https://doi.org/10.3390/s22218135

Iqbal, T., Simpkin, A. J., Roshan, D., Glynn, N., Killilea, J., Walsh, J., Molloy, G., Ganly, S., Ryman, H., Coen, E., Elahi, A., Wijns, W., & Shahzad, A. (2022b). Stress Monitoring Using Wearable Sensors: A Pilot Study and Stress-Predict Dataset. Sensors, 22(21). https://doi.org/10.3390/s22218135

Iqbal, T., Simpkin, A. J., Roshan, D., Glynn, N., Killilea, J., Walsh, J., Molloy, G., Ganly, S., Ryman, H., Coen, E., Elahi, A., Wijns, W., & Shahzad, A. (2022c). Stress Monitoring Using Wearable Sensors: A Pilot Study and Stress-Predict Dataset. Sensors, 22(21). https://doi.org/10.3390/s22218135

Istiadi, I., Emma Budi Sulistiarini, Rudy Joegijantoro, & Affi Nizar Suksmawati. (2021). Perbandingan Metode CBR dan Dempster-Shafer pada Sistem Pakar Terintegrasi Layanan Kesehatan. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(6), 1143–1152. https://doi.org/10.29207/resti.v5i6.3612

Luthfiya, L. ’, Rochmah, I. A., Nabawiyah, H., Damayanti, A. Y., & Pibriyanti, K. (2021). Hubungan tingkat stres, aktivitas fisik, dan pengetahuan gizi seimbang dengan pola makan remaja Relationship beetwen stress level, ohysical activity, and balance nutrition knowledge with dietary pattern in adolescents. https://doi.org/10.30867/sago.v5i2.1391

Mandal, A., Paradkar, M., Panindre, P., & Kumar, S. (2023). AI-based Detection of Stress using Heart Rate Data obtained from Wearable Devices. Procedia Computer Science, 230, 749–759. https://doi.org/10.1016/j.procs.2023.12.129

Meier, J. K., & Schwabe, L. (2024). Consistently increased dorsolateral prefrontal cortex activity during the exposure to acute stressors. Cerebral Cortex, 34(4). https://doi.org/10.1093/cercor/bhae159

Mukhopadhyay, M., Banerjee, S., & Das Mukhopadhyay, C. (2024). Internet of Medical Things and the Evolution of Healthcare 4.0: Exploring Recent Trends. Journal of Electronics, Electromedical Engineering, and Medical Informatics, 6(2), 182–194. https://doi.org/10.35882/jeeemi.v6i2.402

Naegelin, M., Weibel, R. P., Kerr, J. I., Schinazi, V. R., La Marca, R., von Wangenheim, F., Hoelscher, C., & Ferrario, A. (2023). An interpretable machine learning approach to multimodal stress detection in a simulated office environment. Journal of Biomedical Informatics, 139. https://doi.org/10.1016/j.jbi.2023.104299

Nopita, S., Simanjuntak, M., Lubis, I., Studi Sistem Informasi, P., & Kaputama, S. (2023). The Use Of Dempster Shafer’s Method For Diagnosing Acute Renal Failure In Children. Journal of Mathematics and Technology (MATECH), 2(2).

Pratama, I. P. A. E. (2021). Self-Diagnosis of Web-Based Pregnancy and Childbirth Disorders Using Forward Chaining Methods. International Journal of Advances in Data and Information Systems, 2(1). https://doi.org/10.25008/ijadis.v2i1.1198

Rahmadhani, A., Fauziah, F., & Aningsih, A. (2020). Sistem Pakar Deteksi Dini Kesehatan Mental Menggunakan Metode Dempster-Shafer. SISFOTENIKA, 10(1), 37. https://doi.org/10.30700/jst.v10i1.747

Rahmawati, D., Fahrudin, A., & Abdillah, R. (2022). Hubungan Kontrol Diri Dengan Stres Akademik Akibat Pembelajaran Hybrid dalam Masa Pandemi Covid-19 di SMK X Kota Bekasi. Journal of Social Work and Social Services, 2(2), 135–153.

Suryana, E., Wulandari, S., Sagita, E., & Harto, K. (2022). Perkembangan Masa Remaja Akhir (Tugas, Fisik, Intelektual, Emosi, Sosial dan Agama) dan Implikasinya pada Pendidikan (Vol. 5, Issue 6). http://Jiip.stkipyapisdompu.ac.id

Syahputra, H., Naibaho, S. I., Maulana, M. A., Zulfahmi, I., & Sinaga, E. P. (2023). Perbandingan Algoritma Support Vector Machine (SVM) dan Decision Tree Untuk Deteksi Tingkat Depresi Mahasiswa. Bina Insani Ict Journal, 10(1), 52–61.

Talaat, F. M., & El-Balka, R. M. (2023). Stress monitoring using wearable sensors: IoT techniques in medical field. Neural Computing and Applications, 35(25), 18571–18584. https://doi.org/10.1007/s00521-023-08681-z

Thalor, M., Pathak, M., Zingade, D., & Kale, V. (2024). A Web Application to Predict Stress via Keyboard Data and Sensor Data. In Original Research Paper International Journal of Intelligent Systems and Applications in Engineering IJISAE (Vol. 2024, Issue 4). www.ijisae.org

Tjønnås, M. S., Muller, S., Våpenstad, C., Tjønnås, J., Ose, S. O., Das, A., & Sandsund, M. (2024). Stress responses in surgical trainees during simulation-based training courses in laparoscopy. BMC Medical Education, 24(1). https://doi.org/10.1186/s12909-024-05393-3

Tomitani, N., Kanegae, H., Suzuki, Y., Kuwabara, M., & Kario, K. (2021). Stress-Induced Blood Pressure Elevation Self-Measured by a Wearable Watch-Type Device. American Journal of Hypertension, 34(4), 377–382. https://doi.org/10.1093/ajh/hpaa139




DOI: https://doi.org/10.33024/mahesa.v6i2.20257

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