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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Informatika Komputer METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi ILKOM Jurnal Ilmiah Jurnal ULTIMATICS MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURTEKSI Jurnal Teknologi Terpadu Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Makara Human Behavior Studies in Asia Journal of Applied Business and Technology Jurnal Media Pengabdian Komunikasi
Prihandoko Prihandoko
Universitas Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences

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Journal : METHOMIKA: Jurnal Manajemen Informatika

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Integrasi Algoritma YOLOv8 dan Streamlit untuk Visualisasi Real-Time dan Akurat dalam Penghitungan Kerumunan di Kawasan Stasiun Bekasi Prihandoko, Prihandoko; Rumapea, Sri Agustina; Pratama, Abdul Hanif
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No1.pp179-187

Abstract

Crowd management in public transportation areas has become a critical challenge with the rise of urban populations. This study develops a real-time web-based people detection and counting system by integrating the YOLOv8 algorithm with the Streamlit framework. A case study was conducted at the entrance of Bekasi Station. The model was developed using the AI Project Life Cycle approach, and the system was built following the Waterfall methodology. Data were obtained from video recordings, which were extracted into images, annotated, and processed into training and testing datasets. The YOLOv8 model was trained for 50 epochs, yielding strong performance with an mAP@0.5 of 91.7%, a maximum precision of 93.6%, and an F1-score of 87%. Tests on 15 images showed an average accuracy of 80.37% and an error rate of 19.63%. The model's performance declined on out-of-dataset images due to variations in lighting and extreme crowd density. The system was tested using black-box testing and demonstrated that all main features—image upload, object detection, visualization, and result download—functioned correctly. The system has been successfully deployed on Streamlit Cloud. These results indicate that the system offers a practical, lightweight, and responsive solution to support crowd monitoring in public areas. In future development phases, the system can be extended to support real-time video stream processing and integrated with an object tracking and classification module to accurately identify and differentiate the ingress and egress flow of individuals within a defined surveillance area.
Co-Authors Adang Suhendra Agus Perdana Windarto Alyauma Hajjah Amri Jahi Asqia, Misna Chandra, Reza Darwis S. Gani Deny Jollyta Edy Prihantoro Erick Santoso Fawwaz, Muhamad Faishal Gusrianty, Gusrianty I Gusti Putu Purnaba Iwan Tjitradjaja Johan Johan Luky Adrianto Muhammad Hadi Nurahman, Nurahman Pratama, Abdul Hanif Putra, Anas Sofyan Azhar Surya Ramadhan, Muhamad Iqbal Ramdhan, William Setyawati, Dyah Mieta Siddik, Muhammad Sonya, Izatul Putri Sri Agustina Rumapea Sukrianto, Darmanta Yanto, Musli Yuhandri, Muhammad Habib Yuniar Banowosari, Lintang