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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Pilar Nusa Mandiri Paradigma Nuansa Informatika Journal of Computing Innovations and Emerging Technologies Bulletin of Intelligent Machines and Algorithms Jurnal Informatika
Nur Alamsyah
Universitas Informatika Dan Bisnis Indonesia
Computer Science & IT

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Journal : bulletin of intelligent machines and algorithms

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A Progressive Training Framework for Robust YOLOv11-Based Vehicle Detection Across Domain Shifts in Real-World CCTV Environments Aditya Gunaldhi; Budiman; Chairul Habibi; Nur Alamsyah
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 5 (2026): BIMA July 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i5.27

Abstract

The performance of deep learning-based vehicle detection models often deteriorates when applied to real-world CCTV environments due to domain shift caused by variations in lighting, occlusion, glare, and changes in camera viewpoint. This study aims to develop a YOLOv11-based progressive training framework to improve the model’s generalization ability under heterogeneous operational conditions. The proposed method consists of four stages: base training, fine-tuning, and a supervised progressive domain adaptation strategy implemented through sequential fine-tuning on labeled target-domain CCTV images before OpenVINO-based inference optimization. The model is trained using a source dataset and adapted to a target dataset representing real-world CCTV conditions. Evaluation was conducted using Precision, Recall, mAP@50, mAP@50–95, loss curve analysis, per-vehicle-class evaluation, visual testing on CCTV video, as well as latency and throughput measurements. The results show that base training achieved an mAP@50 of 0.919 and built a robust feature representation, while fine-tuning maintained performance stability with an mAP@50 of 0.915. Although domain adaptation reduced mAP@50 to 0.811 due to domain shift, the model demonstrated improved generalization capabilities and maintained more consistent detection under low-light conditions, glare, occlusion, and heavy traffic. OpenVINO INT8 optimization increased inference speed from 4.86 FPS to 9.08 FPS with minimal accuracy loss. These findings demonstrate that the progressive training framework effectively bridges differences in data distribution while producing a vehicle detection model that is more robust, efficient, and suitable for deployment in real-time CCTV-based traffic monitoring systems.
A Trigger Aware, Event Centric, and Uncertainty Calibrated Neuro-Symbolic Framework for Actionable Cyber Threat Intelligence from Indonesian Online News Elia Setiana; Muhamad Achya Arifudin; Nur Alamsyah
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 5 (2026): BIMA July 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i5.31

Abstract

Online news can provide timely cyberthreat signals, but duplicative reporting, fragmented event descriptions, resource-constrained Indonesian language text, and uncalibrated model confidence limit its operational use. This study presents A Trigger-Aware, Event-Centric, and Uncertainty-Calibrated Neuro-Symbolic Framework for Actionable Cyber ​​Threat Intelligence from Indonesian Online News (TRACE-CTI-ID), a proof-of-concept framework that integrates exact deduplication, event-centric clustering, trigger-aware semantic representation, neuro-symbolic fusion, ordinal risk estimation, conformal uncertainty, mitigation mapping, and an event-centric knowledge graph. The experiment used 711 Liputan6 records collected on March 14, 2025. Exact deduplication reduced the corpus to 79 unique headlines, which were automatically consolidated into 26 events. Splitting the separate events resulted in 30 training articles, 5 calibration articles, and 44 test articles with zero event leakage. The calibrated neuro-symbolic model achieved a micro-F1 of 0.283 and a macro-F1 of 0.441, outperforming the baseline TF-IDF of 0.074 and 0.013, respectively. However, ordinal severity prediction remained weak with an accuracy of 0.136, a macro-F1 of 0.138, and a mean absolute error of 2.023. Conformal coverage was also unstable, and the abstention mechanism did not direct uncertain articles to human review. These findings demonstrate the technical feasibility of the integrated pipeline while also demonstrating that silver labeled, title only, and single source data are insufficient for final operational validation. Therefore, the key contribution is a transparent, leak aware evaluation architecture and protocol that can be strengthened through full text collection from multiple sources and independent expert annotation.
Co-Authors Acep Hendra Aditya Gunaldhi Akbar, Imannudin Alif Januantara Prima Arafat Febriandirza Artika Arista Budiman Chairul Habibi Esa Prakasa Fahreja Ramadhan Gunthur Bayu Wibisono Hani Fitria Rahmani Henki Bayu Seta Ihsan AlFauzi Ilham Ramadhan Muhamad Achya Arifudin Niqotaini, Zatin Nursyanti, Reni R. Yadi Rakhman Alamsyah Rezza Novian Noor Rochmat Setiana, Elia Tiara Permata Hati Titan Parama Yoga Titan Parama Yoga Valencia Claudia Jennifer Kaunang Venia R Danestiara Wala Erpurini