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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Mahasiswa TEUB Jurnal EECCIS e-Jurnal Arus Elektro Indonesia Jurnal Teknologi dan Sistem Komputer Network Engineering Research Operation [NERO] Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Electrical Engineering and Computer Science JAREE (Journal on Advanced Research in Electrical Engineering) International Journal of Engineering, Science and Information Technology Jurnal Elektronika dan Telekomunikasi
Rahmadwati, n/a
Brawijaya University
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Adaptive Traffic Light Signal Control Using Fuzzy Logic Based on Real-Time Vehicle Detection from Video Surveillance Zulfa Fahrunnisa; Rahmadwati Rahmadwati; Raden Arief Setyawan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28712

Abstract

Intersections often become the focal points of congestion due to poor traffic signal management, reduced productivity, increased travel duration, gas emissions, and fuel consumption. Existing traffic light systems maintained constant signal duration regardless of traffic situations, resulting in green signals for lanes with no vehicle queues that increased waiting times in other lanes. Therefore, a real-time traffic signal optimization system using Fuzzy Logic control, utilizing vehicle queue and flow rate real-time data from video surveillance, is needed. This research used recorded video from surveillance cameras in Banten Province, Indonesia, during daylight conditions. Vehicle queues and flow rate data were used as parameters to determine traffic light signals. The YOLO algorithm obtained these parameter values, then served them as inputs for the Fuzzy Logic system to determine signal duration. The accuracy of the traffic situation estimation system fluctuated within a range of 40% to 100%. Simulation results showed an improvement of approximately 18% by evaluating the total number of vehicles that exited the queue and reduced vehicle waiting time by about 21% compared to the existing system on intersection efficiency. Consequently, the proposed system can reduce pollution and fuel consumption, contributing to urban sustainability and public well-being enhancement. Despite the improvements over the previous systems, the accuracy of the vehicle detection system may vary with traffic density based on the extent of occlusions present, which is an area that needs further refinement. This research's contributions include utilizing real-time video footage from surveillance cameras above traffic lights to obtain real traffic conditions and identify potential errors such as occlusion of overlapping vehicle due to very congested roads. Another contribution is the adjustment of the Fuzzy membership function based on the vehicle detection system's ability to ensure precise determination of green signal duration, even when the input data contains errors.
Deep Learning-Based Worker Posture Classification for Ergonomic Risk Evaluation in Manufacturing Rahmadwati Rahmadwati; Farrel Rafif Ferdian; Yeni Sumantri; Dhaffin Rayhzan Ferdian
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.1878

Abstract

Continuous ergonomic monitoring in manufacturing remains challenging because conventional posture assessment methods rely on manual observation, making evaluations time-consuming, subjective, and unsuitable for continuous industrial applications. This study proposes an automated ergonomic risk assessment framework that integrates Media Pipe Pose with a Convolutional Neural Network (CNN) to classify worker postures into low-, medium-, and high-risk ergonomic categories. The framework extracts 33 anatomical body landmarks from RGB images and video frames to generate marker less posture representations for deep learning-based classification. A dataset consisting of 4,500 posture samples collected from assembly, packaging, and welding workstations was expanded to 12,000 samples through data augmentation techniques, including rotation, scaling, horizontal flipping, and brightness adjustment, to improve model robustness and generalization. The CNN model was trained and evaluated using an independent test dataset, achieving an overall classification accuracy of 94.2%, with precision, recall, and F1-score consistently exceeding 94% across all ergonomic risk categories. Comparative evaluation against a conventional REBA/RULA-based rule-driven assessment demonstrated that the proposed framework improved classification accuracy by 7.5 percentage points while eliminating the need for manual posture scoring and reducing observer subjectivity. Furthermore, computational performance analysis showed that the complete inference pipeline operated at an average of 14 ms per frame (approximately 28.5 FPS) on a standard Intel Core i7 CPU with 16 GB RAM, without requiring GPU acceleration, indicating its suitability for real-time deployment in manufacturing environments. The proposed Media Pipe–CNN framework provides an efficient, accurate, and marker less solution for automated ergonomic risk assessment, supporting intelligent occupational safety management, continuous workplace monitoring, and the implementation of smart manufacturing systems aligned with Industry 4.0 initiatives
Co-Authors Achmad Ernanda T. P. Aditya Desta Pranata Adrian Alkahfi Fauzi Afdhol Goyanda Hidayatullah Afriandika Brillian Agung Pambudi Ahmad Farid Nurrohman S. Aiman Muhamad Basymeleh Ainur Rosyidatul Husna Ajeng Atha Ardella Cahyanti Akhmad Sabarudin Akio Kitagawa Al Jihad Andi Saungnaga Alva Kosasih Alvi Kusuma Wijaya Andik Setiawan Andriyan Rizky Sigit Anggara Truna Negara Angger Abdul Razak Anisari Mei Prihatini Ardyanto Dwi Kurniawan Arga Rifky Nugraha Aulia Muhammad Aulia Wiendyka Yudha Aziz Muslim Azizurrahman Rafli Bambang Siswojo Bambang Siswojo Bambang Siswojo Boby Yusuf Habibi Budi Prasetyo Dean Passaddhi Deron Liang Dhaffin Rayhzan Ferdian Dharmawan - Diannata Rahman Y. Didit Afrian Nugraha Dyah Ayu Anggreini T Dzikrullah Akbar Eka Bayu Prinandika Eka Maulana Eka Maulana Ergan Pratu Handistya Erni Yudaningtyas Erni Yudaningtyas Erni Yudhaningtyas Faisal Maulana Ibrahim Faishal Farras Wasito Faiza Alif Fakhrina Falah Heksananda Faridzky Adhi Baskara Farrel Rafif Ferdian Febi Syahputra Frans W. P. Napitupulu Gabriel Andriano Bramantyo Garneta Rizke Ayu Cempaka Geraldio Ramadhan Safitri Gigih Gumilar Gigih Mandegani Godam Ardianto Goegoes Dwi Nusantoro Goegoes Dwi Nusantoro Golshah Naghdy Gosi Desgraha Gristita Tresna Murti Gurnita Fajar Gemilang Hadi Suyono Hary Soekotjo Dachlan Heri Susanto I Putu Manu Satyam Idam Almualif Ika Kusumaning Putri Indyanto Gadang Alfaruki Jefry Sugihatmoko Jesse Sebastian Jodie Revel Palasroha Joko Prasetyo Kevin Putra Pratama K. R. Kukuh Nur Aji Kukuh Priambodo Lovinardo Devharo Luthfan Prayoga Luthfiyah Rachmawati M. Aldiki Febriantono M. Aziz Muslim M. Hadafi Maulana I. M. Kholid Mawardi M. Yufrizal Afif Mahaestra Fachrurrozi Mahdin Rohmatillah Masykur Huda Maulana, Eka Moch. Rusli Mohammad Bimo Digdoyo Mohammad Zidnil Maarif A. Mudjirahardjo, Panca Muh Wahid Anshori Riza Muh. Ghiffari Caesa Ramadahan Muhamad Faishol Arif Muhamad Ibnu Fajar Muhamamd Dimas Ali Cahya Muhammad Aziz Muslim Muhammad Aziz Muslim Muhammad Dieny Amrullah Muhammad Dzikrullah Suratin, Muhammad Dzikrullah Muhammad Fahmi Illmi Muhammad Fauzan Edy Purnomo Muhammad Izaaz Rozan Muhammad Nurhilal Hamdi Muhammad Oktafian Ulal Ma'arif Muhammad Rizki Rafido Muhammad Sholahudin Nur Anwar Muhammad Wildan Nashrullah Muhammad Zulfikri Muhammad Zulfikri n/a Abdullah n/a Purwanto n/a Retnowati Nanang Sulistiyanto Nandito Ardaffa Putra Nugroho Dwi Aprillianto Nuni Hutami Stanto Onny Setyawati Panca Mudjirahardjo Pandu Arya Zulkarnain Ponco Siwindarto Prihadya Surya Ramdhani R. Afin Priswiyandi Radek Purnomo Raden Arief Setyawan Rafa Raihan Fadilla Rahman, Alif Rasyadan Izzatur Rakhmad Romadhoni Rama Hasani Rayyan Ghaus Rahmat Rif'an, Mochammad Rifan Pradestama Giantara Rifqi Hilman Wangsawinangun Rizki Zein Achmadi Rizky Adi Sanjaya Robintang Sotardodo Situmorang Rudy Yuwono Rusli, Mochammad Ruth Astari Anindita Safuddin Zuhri Sari, Sapriesty Nainy Shaskia Vilardl Ri Cahya Shaufi Firdausi Luthfi Sholeh Hadi P. Sholeh Hadi Pramono Subairi Subairi Sultoni Sultoni Suyono, Hadi Topan Firdaus Tri Agung Prasetio Tri Wahyu Oktaviana Putri Valen Kristian Eriski Vita Kusumasari Waru Djuriatno Wia Siisgo Alnakulla Wijono Wijono Wildan Arif Maulana Wirangga Luvianca Yeni Sumantri Yudo Jati Wicaksono Yuyu Wahyu Zainul Abidin Zulfa Fahrunnisa Zulfa Fahrunnisa Zzyo Chandra