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All Journal JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Teknik Informatika (JUTIF)
Hadis, Muhammad Sabirin
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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A MACHINE LEARNING APPROACH TO EYE BLINK DETECTION IN LOW-LIGHT VIDEOS Rasyid, Muhammad Furqan; Rizal, Muhammad; Musu, Wilem; Hadis, Muhammad Sabirin
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 3 (2023): JUTIF Volume 4, Number 3, June 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.3.1024

Abstract

Inadequate lighting conditions can harm the accuracy of blink detection systems, which play a crucial role in fatigue detection technology, transportation and security applications. While some video capture devices are now equipped with flashlight technology to enhance lighting, users occasionally need to remember to activate this feature, resulting in slightly darker videos. Consequently, there is a pressing need to improve the performance of blink detection systems to detect eye accurately blinks in low light videos. This research proposes developing a machine learning-based blink detection system to see flashes in low-light videos. The Confusion matrix was conducted to evaluate the effectiveness of the proposed blink detection system. These tests involved 31 videos ranging from 5 to 10 seconds in duration. Involving male and female test subjects aged between 20 and 22. The accuracy of the proposed blink detection system was measured using the confusion matrix method. The results indicate that by leveraging a machine learning approach, the blink detection system achieved a remarkable accuracy of 100% in detecting blinks within low-light videos. However, this research necessitates further development to account for more complex and diverse real-life situations. Future studies could focus on developing more sophisticated algorithms and expanding the test subjects to improve the performance of the blink detection system in low light conditions. Such advancements would contribute to the practical application of the system in a broader range of scenarios, ultimately enhancing its effectiveness in fatigue detection technology.
NILAI OPTIMAL CLIP LIMIT METODE CLAHE UNTUK MENINGKATKAN AKURASI PENGENALAN WAJAH PADA VIDEO CCTV NURZAENAB, NURZAENAB; HADIS, MUHAMMAD SABIRIN; ANGRIAWAN, RANDY
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 5 No 2 (2020): OCTOBER
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1454.09 KB) | DOI: 10.24252/instek.v5i2.16201

Abstract

Pengenalan wajah adalah salah satu penelitian yang banyak diminati komunitas peneliti.  Secara umum, pengenalan wajah melewati proses pre-processing wajah, ekstraksi wajah, klasifikasi wajah, dan rekognisi wajah. Penelitian ini menawarkan nilai parameter cliplimit yang optimal untuk meningkatkan akurasi pengenalan wajah pada video CCTV, karena sampai saat ini nilai cliplimit yang digunakan secara default adalah 0,01 dan belum tentu sesuai dengan kontras data uji setiap penelitian. Tantangan pada penelitian ini adalah data latih dan data uji yang berbeda dari resolusi citra sampai environment, dengan demikian tahap pre-processing sangat diperhatikan untuk dapat menghasilkan citra yang baik dari segi kontras sehingga fitur-fitur wajah dapat diekstraksi dengan baik. Akurasi yang diperoleh menggunakan nilai cliplimit secara default lebih rendah 12,25% dari nilai cliplimit yang diusulkan yaitu 0,005. Sehingga nilai cliplimit yang diusulkan dapat digunakan untuk pengenalan wajah pada video CCTV dengan resolusi citra 96 x 96 dpi. Kata Kunci— CLAHE, Cliplimit, Pengenalan Wajah, Video CCTV.
Co-Authors A.A. Ketut Agung Cahyawan W Anggraini, Indah ANGRIAWAN, RANDY Annisa Nurul Puteri Hendra, Akbar Akbar Humairah B, Nurul Isra Mashud, Mashud Mashud Muhammad Furqan Rasyid MUHAMMAD RIZAL Nuraeni Nuraeni NURZAENAB NURZAENAB NURZAENAB, NURZAENAB Prawati, Bau Endang Ramadhani, Indri Pratiwi Rizal, Muhammad Muhammad Sada, Karolina Owa Wilem Musu