Archives

  • BIMA March 2026 Issue
    Vol. 1 No. 3 (2026)

    This issue presents various research studies discussing the application of machine learning in the fields of education, healthcare, agriculture, and public policy. In general, the articles published in this issue emphasize the importance of data-driven predictive models in improving decision-making accuracy and system efficiency. Research in the field of education indicates that ensemble learning approaches can improve the early detection of at-risk students, particularly on imbalanced datasets. In the healthcare sector, machine learning models have proven effective for the early prediction of diseases such as diabetes and cervical cancer, with the addition of explainable AI approaches to enhance the interpretability of results. In the agricultural sector, comparative studies show that simple models can still deliver optimal performance in predicting crop yields. Meanwhile, social media-based sentiment analysis provides insights into public perceptions of government policies in a more objective manner. Overall, this issue highlights the critical role of machine learning in addressing complex problems adaptively, accurately, and practically across various sectors.

  • BIMA July 2026 Issue
    Vol. 1 No. 5 (2026)

    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.

  • BIMA January 2026 Issue
    Vol. 1 No. 2 (2026)

    This issue of the Bulletin of Intelligent Machines and Algorithms (BIMA) brings together five research articles that explore practical applications of artificial intelligence and machine learning across multiple domains. The published works address current challenges in renewable energy forecasting, healthcare analytics, cybersecurity, epidemiological prediction, and health-related data classification. Several contributions highlight the growing role of explainable and interpretable models in supporting reliable decision-making, particularly in health and public policy contexts. Other studies focus on efficient learning architectures that achieve strong predictive performance while remaining suitable for real-world deployment. Collectively, the articles in this issue reflect an emphasis on methodological soundness, applicability, and transparency. Through these contributions, BIMA continues to support the dissemination of applied research that advances intelligent systems while maintaining relevance to real-world problems and decision-making needs.

  • BIMA May 2026 Issue
    Vol. 1 No. 4 (2026)

    BIMA Vol. 1 No. 4 May 2026 presents various research studies in the fields of machine learning, deep learning, and intelligent system development. This issue covers the development of YOLO26n for apple leaf disease detection, which delivers high performance while maintaining a lightweight architecture to support precision agriculture. Another study compares various machine learning models for predicting the likelihood of Airbnb bookings in Singapore, using GridSearchCV optimization and learning curve analysis to improve model reliability. In the field of text analysis, a comparison of LightGBM and CNN for sentiment classification of game reviews shows that CNN achieves slightly higher accuracy, although class imbalance remains a challenge. This issue also features an ensemble learning approach for breast cancer diagnosis through the combination of CNN, ResNet18, and VGG16, which yields more stable predictions. Additionally, research on gameplay logs produces a structured dataset through preprocessing and feature engineering as a foundation for the development of adaptive mathematics learning. Overall, this issue showcases the application of intelligent algorithms in agriculture, tourism, sentiment analysis, healthcare, and education.

  • BIMA November 2025 Issue
    Vol. 1 No. 1 (2025)

    The inaugural issue of the Bulletin of Intelligent Machines and Algorithms (BIMA) marks the journal’s first contribution to the dissemination of research in artificial intelligence, data science, and machine learning. This issue presents five selected articles that demonstrate the application of intelligent algorithms across diverse interdisciplinary domains, including digital marketing, multimedia analysis, public sentiment studies, agriculture, and financial security. The contributions highlight a strong emphasis on model interpretability, algorithmic robustness, and practical relevance. Several studies integrate explainable machine learning techniques to support transparent decision-making, while others focus on ensemble and deep learning approaches to improve predictive accuracy in real-world settings. Together, the articles reflect BIMA’s commitment to publishing methodologically sound and application-oriented research, establishing a foundation for the journal’s role in advancing reliable and impactful intelligent systems research.