<![CDATA[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.]]>
