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BIMA (Bulletin of Intelligent Machines and Algorithms) is an international peer-reviewed journal dedicated to promoting research in the fields of artificial intelligence, machine learning, and algorithms. BIMA serves as a platform for publishing the latest research findings and innovative applications in these rapidly evolving fields. The journal aims to contribute to the academic and professional development of researchers, practitioners, and educators by publishing high-quality articles that provide in-depth insights into the theoretical, practical, and computational aspects of intelligent systems and algorithms.
Focus and Scope
BIMA publishes original research articles, reviews, and technical reviews on various topics related to intelligent machines and algorithms. The scope of this journal includes, but is not limited to:
Publication Frequency
BIMA is published bimonthly, with issues scheduled for January, March, May, July, September, and November. Each issue contains peer-reviewed articles that reflect the latest developments in the field of intelligent machines and algorithms.
BIMA Vol. 1 No. 5 July 2026 highlights the application of intelligent machines, machine learning, and artificial intelligence across various fields. This issue discusses the development of YOLOv11 using progressive training and domain adaptation to improve the robustness of vehicle detection in real-world CCTV conditions, including OpenVINO optimization to support real-time inference. Another article develops a recommendation system for tourist destinations in the Greater Bandung area based on Content-Based Filtering, TF-IDF, and Cosine Similarity, with evaluation results showing a high level of recommendation relevance. This issue also presents TRACE-CTI-ID, a neuro-symbolic framework for generating cyber threat intelligence from Indonesian-language online news using an event-centric approach and uncertainty calibration. In the field of logistics, an ESP32-S3-based intelligent cold chain system integrates temperature, vibration, and location data to monitor a fleet of refrigerated trucks. Additionally, Latent Dirichlet Allocation is applied to identify the main themes of TikTok user complaints from Google Play Store reviews.