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All Journal Progresif: Jurnal Ilmiah Komputer
Khairi Budayawan
Univesitas Negeri Padang
Computer Science & IT Control & Systems Engineering

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RANCANG BANGUN APLIKASI WEB PREVENTIF COMPUTER VISION SYNDROME BERBASIS MONITORING POSTUR LEHER REAL-TIME DENGAN METODE PERSONALIZED BASELINE M Rizal Saputra; Khairi Budayawan; Syafrijon Syafrijon; Delvi Asmara
Progresif: Jurnal Ilmiah Komputer Vol. 22 No. 3 (2026): Juli
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i3.4055

Abstract

Prolonged use of digital devices triggers the risk of Computer Vision Syndrome (CVS) and neck posture abnormalities such as Forward Head Posture. Conventional detection systems with static thresholds often trigger false alarms due to differences in individuals' natural postures. This study aims to develop a real-time neck posture monitoring web application to prevent CVS adaptively. The system was developed using the Prototyping method based on a Progressive Web App (PWA). Visual processing is executed entirely on the client-side using MediaPipe Pose Landmarker and WebRTC, while dynamic threshold calibration utilizes the Personalized Baseline method. Monitoring data and gamification history are managed locally through Local Storage. The test results indicate that the system is capable of extracting neck coordinates, monitoring posture inclination, and providing ergonomic notifications accurately without data transmission to a server. The Personalized Baseline method proved effective in reducing the false alarm rate, making this system an adaptive, lightweight CVS preventive solution capable of ensuring user privacy. Keywords: Computer Vision Syndrome; Forward Head Posture; Local Storage; MediaPipe; Personalized Baseline.   Abstrak Penggunaan perangkat digital yang berkepanjangan memicu risiko Computer Vision Syndrome (CVS) dan kelainan postur leher seperti Forward Head Posture. Sistem deteksi konvensional dengan ambang batas statis sering memicu peringatan palsu akibat perbedaan postur alami individu. Penelitian ini bertujuan mengembangkan aplikasi web pemantau postur leher real-time untuk mencegah CVS secara adaptif. Sistem dikembangkan menggunakan metode Prototyping berbasis Progressive Web App (PWA). Pemrosesan visual dieksekusi sepenuhnya di sisi klien menggunakan MediaPipe Pose Landmarker dan WebRTC, sedangkan kalibrasi ambang batas dinamis menggunakan metode Personalized Baseline. Data pemantauan dan riwayat gamifikasi dikelola secara lokal melalui Local Storage. Hasil pengujian menunjukkan sistem mampu mengekstrak koordinat leher, memantau kemiringan postur, dan memberikan notifikasi ergonomi secara akurat tanpa transmisi data ke peladen. Metode Personalized Baseline terbukti efektif menekan tingkat peringatan palsu, menjadikan sistem ini sebagai solusi preventif CVS yang adaptif, ringan, dan mampu menjamin privasi pengguna.
PENGEMBANGAN SISTEM PREDIKSI KASUS DEMAM BERDARAH DENGUE BERDASARKAN CURAH HUJAN MENGGUNAKAN ALGORITMA LONG SHORT-TERM MEMORY (LSTM) BERBASIS WEB LARAVEL DI KOTA PADANG Billy Kelvin Tressa; Syafrijon Syafrijon; Khairi Budayawan; Delvi Asmara
Progresif: Jurnal Ilmiah Komputer Vol. 22 No. 3 (2026): Juli
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i3.4065

Abstract

Dengue Hemorrhagic Fever (DHF) is an infectious disease that poses a serious public health problem in tropical regions, with its spread strongly influenced by rainfall; however, current surveillance systems remain manual and reactive, often delaying intervention. This study aims to design and implement a rainfall-based DHF case prediction system using a Long Short-Term Memory (LSTM) algorithm tailored to each sub-district. The system was developed using the Prototyping method and integrated into a web application, with Laravel as the main interface and Flask as the prediction server. Testing was conducted through model accuracy evaluation using MAE, RMSE, and MAPE metrics compared against the ARIMA model, along with Black Box Testing of system functionality. Results show that LSTM outperformed ARIMA in 9 of 11 sub-districts (81.8%), and all Black Box test scenarios ran as designed, confirming that the system can proactively support local health decision-making. Keywords: dengue hemorrhagic fever; rainfall; LSTM; prediction; web application   Abstrak Demam Berdarah Dengue (DBD) merupakan penyakit menular yang menjadi permasalahan kesehatan masyarakat serius di wilayah tropis, dengan penyebaran yang sangat dipengaruhi oleh curah hujan; namun sistem surveilans yang berjalan saat ini masih bersifat manual dan reaktif sehingga intervensi sering terlambat. Penelitian ini bertujuan merancang dan mengimplementasikan sistem prediksi kasus DBD berbasis curah hujan menggunakan algoritma Long Short-Term Memory (LSTM) yang spesifik untuk masing-masing kecamatan. Sistem dikembangkan menggunakan metode Prototyping dan diintegrasikan ke dalam aplikasi web dengan arsitektur Laravel sebagai antarmuka utama dan Flask sebagai peladen prediksi. Pengujian dilakukan melalui evaluasi akurasi model menggunakan metrik MAE, RMSE, dan MAPE yang dibandingkan dengan model ARIMA, serta pengujian Black Box terhadap fungsionalitas sistem. Hasil pengujian menunjukkan LSTM unggul pada 9 dari 11 kecamatan (81,8%) dibanding ARIMA, dan seluruh skenario Black Box berjalan sesuai rancangan, mengonfirmasi bahwa sistem mampu mendukung pengambilan keputusan kesehatan secara proaktif di tingkat kecamatan.
Co-Authors Billy Kelvin Tressa Delvi Asmara M Rizal Saputra Syafrijon Syafrijon