TAHLIA, INDRIYANA NOVA (2026) SISTEM MONITORING KELELAHAN MATA BERBASIS ESTIMASI JARAK PANDANG DAN ANALISIS KEDIPAN MATA MENGGUNAKAN MEDIAPIPE FACE MESH. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
Prolonged use of digital devices increases the risk of Computer vision Syndrome (CVS), which is influenced by non-ergonomic viewing distance and reduced blink frequency. This study aims to develop a real-time eye fatigue monitoring system based on Mediapipe Face Mesh by integrating Inter-Pupillary Distance (IPD) for viewing distance estimation and Eye Aspect Ratio (EAR) for blink detection, enhanced with a time-windowing approach. The system was developed using the Spiral Model and evaluated using Mean Absolute Error (MAE), Confusion Matrix, and Black Box Testing. The experimental results show that the proposed system achieved a Mean Absolute Error (MAE) of 0.34 cm for viewing distance estimation and a blink detection accuracy of 93%, demonstrating improved detection stability while reducing false positives. Furthermore, the Black Box Testing results indicated that all system functionalities operated as intended according to the predefined test scenarios. Overall, the proposed system is capable of performing real-time eye fatigue monitoring with accurate viewing distance estimation, stable blink detection, and reliable system functionality. Keywords: Computer vision Syndrome, Mediapipe Face Mesh, Eye Aspect Ratio, Inter-Pupillary Distance, Time-windowing. Penggunaan perangkat digital dalam waktu yang lama dapat meningkatkan risiko Computer vision Syndrome (CVS) yang dipengaruhi oleh jarak pandang yang tidak ergonomis dan penurunan frekuensi kedipan mata. Penelitian ini bertujuan mengembangkan sistem monitoring kelelahan mata berbasis Mediapipe Face Mesh menggunakan estimasi jarak pandang Inter-Pupillary Distance (IPD) dan analisis kedipan mata Eye Aspect Ratio (EAR) yang dioptimalkan dengan time-windowing. Pengembangan sistem dilakukan menggunakan Spiral Model, sedangkan evaluasi meliputi Mean Absolute Error (MAE), Confusion Matrix, dan Black Box Testing. Hasil penelitian menunjukkan bahwa sistem mampu mengestimasi jarak pandang dengan nilai MAE sebesar 0,34 cm serta mendeteksi kedipan mata dengan akurasi 93%, sehingga mampu meningkatkan stabilitas deteksi dan mengurangi false positive. Selanjutnya, pengujian Black Box Testing menunjukkan bahwa setiap fungsi pada sistem dapat beroperasi sesuai dengan skenario yang telah dirancang. Berdasarkan hasil tersebut, sistem yang dikembangkan mampu melakukan monitoring kelelahan mata secara real-time dengan estimasi jarak yang akurat, deteksi kedipan yang stabil, dan fungsionalitas sistem yang baik. Kata kunci: Computer vision Syndrome, Mediapipe Face Mesh, Eye Aspect Ratio, Inter-Pupillary Distance, time-windowing.
| Item Type: | Thesis (S1) |
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| NIM/NIDN Creators: | 41522010238 |
| Uncontrolled Keywords: | Computer vision Syndrome, Mediapipe Face Mesh, Eye Aspect Ratio, Inter-Pupillary Distance, time-windowing. |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika 700 Arts/Seni, Seni Rupa, Kesenian > 770 Photography and Photographs/Seni Fotografi dan Foto > 771 Techniques and Procedures/Teknik Seni Fotografi dan Foto, Prosedur Seni Fotografi dan Foto > 771.3 Cameras and Accessories/Kamera dan Asesoris Kamera > 771.36 Camera Shutters/Kamera Shutters |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | khalimah |
| Date Deposited: | 18 Aug 2026 05:01 |
| Last Modified: | 18 Aug 2026 05:01 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/103286 |
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