About the Journal

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:

  • Artificial Intelligence: Methodologies, algorithms, and architectures for building intelligent systems, including knowledge representation, reasoning, learning, and perception.
  • Machine Learning: Supervised, unsupervised, semi-supervised, and reinforcement learning algorithms; applications in real-world problems.
  • Deep Learning: Advanced neural network architectures such as CNNs, RNNs, Transformers, and their applications in various domains including image, video, text, and signal processing.
  • Computer Vision: Image processing, object detection and recognition, image segmentation, motion analysis, and visual scene understanding in intelligent systems.
  • Data Mining: Techniques for extracting patterns and knowledge from large datasets.
  • Optimisation Algorithms: Theory and applications of optimisation techniques in continuous and discrete domains.
  • Computational Intelligence: Evolutionary algorithms, fuzzy logic, and swarm intelligence systems.
  • Natural Language Processing (NLP): Advances in language understanding, translation, and text analysis.
  • Applications: Applications of artificial intelligence and algorithms in healthcare, finance, industry, education, and other fields.
  • Robotics and Autonomous Systems: Intelligent robots, human-robot interaction, and autonomous vehicles.

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.

Announcements

Current Issue

Vol. 1 No. 1 (2025): BIMA issue November 2025
View Vol. 1 No. 1 (2025): BIMA issue November 2025

The inaugural issue of the Bulletin of Intelligent Machines and Algorithms (BIMA)—Vol. 1 No. 1 (November 2025)—marks a milestone in disseminating cutting-edge research in artificial intelligence, data science, and machine learning applications across interdisciplinary domains. Published by Maheswari Publisher, this volume reflects the journal’s commitment to advancing both the theoretical and applied aspects of intelligent systems, emphasizing model interpretability, transparency, and social impact.

BIMA is a bimonthly peer-reviewed journal, issued in January, March, May, July, September, and November. Each edition curates original research articles focusing on innovations in computational intelligence, algorithmic optimization, and applied machine learning in various sectors including business, finance, agriculture, multimedia, and public sentiment analysis.

Highlights of Volume 1 Number 1 (November 2025)

This inaugural issue presents five featured papers that collectively showcase the diverse and transformative applications of intelligent algorithms:

Digital Marketing Optimization through Explainable Ensemble Learning
Introduce a Stacking Ensemble model for revenue prediction in digital marketing. By integrating SHAP (Shapley Additive Explanations), the study enhances transparency in understanding the influence of pricing and quantity variables, offering actionable insights for business strategy optimization.

Machine Learning for Music Tempo Prediction
Explores the prediction of Beats Per Minute (BPM) using advanced ensemble regression models such as LightGBM, XGBoost, and Random Forest. The research demonstrates how feature engineering and interpretability contribute to improving the precision of audio-based tempo estimation for music analytics and recommendation systems.

Sentiment Analysis in Public Infrastructure Projects
Comparative evaluation of Naïve Bayes, SVM, K-NN, and Random Forest for classifying Indonesian Twitter sentiments toward the Jakarta–Bandung High-Speed Rail project. The study provides empirical evidence of SVM’s superior performance and its relevance for real-time opinion mining in national policy discourse.

Explainable Deep Transfer Learning in Agriculture
Interpretable VGG-19–based transfer learning framework for tomato leaf disease classification. By combining fine-tuning, data augmentation, and explainability tools, the study demonstrates robust performance in precision agriculture and highlights the importance of transparent AI for agricultural sustainability.

High-Precision Fraud Detection with Ensemble and Deep Models
Investigates hybrid machine learning strategies for credit card fraud detection using Random Forest and 1D CNN on imbalanced datasets. The study contributes a reproducible framework combining SMOTE and ensemble deep learning to achieve high precision and recall, reinforcing AI’s role in financial security and fraud analytics.

Editorial Note

This first issue underscores the journal’s mission to bridge academic innovation and real-world impact. The selected works reflect an integrated vision of explainable, ethical, and efficient AI, encouraging interdisciplinary dialogue and practical implementations across multiple sectors.

Researchers, practitioners, and policymakers are invited to contribute to BIMA’s forthcoming editions, continuing this spirit of innovation and collaboration in advancing intelligent systems research.

 

 

 

 

 

 

Published: 2025-11-11

Articles

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