https://ejournals.umn.ac.id/index.php/TI <div style="text-align: justify;"><strong>Ultimatics : Jurnal Teknik Informatika </strong>is the Journal of the Informatics Study Program at Universitas Multimedia Nusantara which presents scientific research articles in the fields of Computer Science and Informatics, as well as the latest theoretical and practical issues, including Analysis and Design of Algorithm, Software Engineering, System and Network Security, Ubiquitous and Mobile Computing, Artificial Intelligence and Machine Learning, Algorithm Theory, World Wide Web, Cryptography, as well as other topics in the field of Informatics. Ultimatics : Jurnal Teknik Informatika is published regularly twice a year (June and December) and is published by the Faculty of Engineering and Informatics at Universitas Multimedia Nusantara.<br>Ultimatics : Jurnal Teknik Informatika has been reaccredited by the National Journal Accreditation (Arjuna), Ministry of Education, Culture, Research and Higher Education with<strong> SINTA 3</strong> rating, as stated by Decree No. 164/E/KPT/2021 starts from Vol.14 No.1 to Vol.18 No.2.<br> <strong>Online ISSN: <a href="http://issn.pdii.lipi.go.id/issn.cgi?daftar&amp;1461731210&amp;1&amp;&amp;">2581-186X</a></strong> <br><strong>Print ISSN: <a href="http://issn.pdii.lipi.go.id/issn.cgi?daftar&amp;1328790711&amp;1&amp;&amp;">2085-4552</a></strong></div> <p>&nbsp;</p> Faculty of Engineering and Informatics, Universitas Multimedia Nusantara en-US Ultimatics : Jurnal Teknik Informatika 2085-4552 <p>Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a <strong>Creative Commons Attribution-ShareAlike International License (CC-BY-SA 4.0)</strong> that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.</p> <p>Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.</p> <p><strong>Copyright without Restrictions</strong></p> <p>The journal allows the author(s) to hold the copyright without restrictions and will retain publishing rights without restrictions.</p> <p>The submitted papers are assumed to contain no proprietary material unprotected by patent or patent application; responsibility for technical content and for protection of proprietary material rests solely with the author(s) and their organizations and is not the responsibility of the ULTIMATICS or its Editorial Staff. The main (first/corresponding) author is responsible for ensuring that the article has been seen and approved by all the other authors. It is the responsibility of the author to obtain all necessary copyright release permissions for the use of any copyrighted materials in the manuscript prior to the submission.</p> https://ejournals.umn.ac.id/index.php/TI/article/view/4133 <p><strong>Pedestrian areas often serve as centers of high public activity, requiring intelligent monitoring systems to ensure the safety and comfort of their users. The application of computer vision technology, particularly object detection, offers a promising approach for identifying and estimating the number of individuals in open public spaces. This study implements the YOLOv8 algorithm to develop a human detection and crowd counting model within the pedestrian zones of the Bogor Botanical Garden. Data were collected in the form of images and videos from three strategic locations and annotated using Roboflow with a single object class labeled “person.” The model was trained on the Google Colab platform using a Region of Interest (ROI)-based approach and evaluated through confusion matrix, precision, recall, F1-score, and mean Average Precision (mAP). Results indicate a precision of 0.846, recall of 0.858, F1-score of 0.85, and mAP@50 of 0.951, although a performance drop was observed at mAP@50-95 with a score of 0.586. These findings suggest that YOLOv8 demonstrates strong real-time performance in pedestrian human detection, while challenges remain in enhancing precision under complex and varied conditions.</strong></p> Prihandoko Prihandoko Sri Agustina Rumapea Muhamad Faishal Fawwaz ##submission.copyrightStatement## http://creativecommons.org/licenses/by-sa/4.0 2025-04-29 2025-04-29 17 1 1 10 10.31937/ti.v17i1.4133