Bulletin of Intelligent Machines and Algorithms
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.
Articles
15 Documents
<![CDATA[Machine Learning Based Cervical Cancer Risk Prediction with SHAP-Driven Feature Interpretation ]]>
<![CDATA[Ardiansyah, Fachrizal]]>;
<![CDATA[Raka Deny Abdi Putra]]>;
<![CDATA[Budiman]]>
<![CDATA[ Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 3 (2026): BIMA March 2026 Issue ]]>
Publisher : <![CDATA[Maheswari Publisher ]]>
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DOI: 10.65780/bima.v1i3.16
<![CDATA[Cervical cancer remains a critical public health problem, particularly in developing countries where early detection is often limited. This study presents a machine learning–based approach for cervical cancer risk prediction that emphasizes both predictive accuracy and interpretability. Several supervised algorithms, namely K-Nearest Neighbors, Random Forest, XGBoost, and CatBoost, were evaluated using the Cervical Cancer (Risk Factors) dataset from the UCI Machine Learning Repository following comprehensive data preprocessing and systematic hyperparameter optimization. The experimental results show that CatBoost achieved the best overall performance, with an optimized accuracy of 97.01% and improved sensitivity in detecting high-risk cases, supported by stable k-fold cross-validation results. To enhance clinical transparency, explainable artificial intelligence was incorporated via SHAP, revealing that key predictors such as the Schiller test, age, and reproductive factors played dominant roles in the model’s decisions. These findings demonstrate that the proposed framework is not only accurate and stable but also interpretable and clinically relevant, making it well-suited to support early detection and decision-making in cervical cancer screening, especially in resource-limited healthcare settings.]]>
<![CDATA[Comparative Analysis of Machine Learning Regression Models for Paddy Yield Prediction ]]>
<![CDATA[Chery Cardinawati Sitohang]]>;
<![CDATA[Fitri Kinkin]]>;
<![CDATA[Budiman]]>
<![CDATA[ Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 3 (2026): BIMA March 2026 Issue ]]>
Publisher : <![CDATA[Maheswari Publisher ]]>
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DOI: 10.65780/bima.v1i3.17
<![CDATA[Accurate paddy yield prediction is essential to support food security, agricultural planning, and data-driven decision-making. The increasing availability of agricultural data has encouraged the adoption of machine learning approaches to overcome the limitations of conventional yield estimation methods. This study presents a comparative analysis of five regression-based machine learning algorithms—Linear Regression, K-Nearest Neighbors Regressor, Decision Tree Regressor, Random Forest Regressor, and Support Vector Regression—for paddy yield prediction. The experiments were conducted using the Paddy dataset from the UCI Machine Learning Repository, which consists of 2,789 samples and 45 variables (44 input features and 1 target variable). The dataset was preprocessed through data cleaning, feature standardization, and an 80:20 train–test split. Model performance was evaluated using Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and the coefficient of determination (R²). Experimental results show that Linear Regression achieved the best overall performance with an R² value of 0.9896 and an RMSE of 942.09, indicating strong predictive accuracy and stability. Despite its simplicity, Linear Regression outperformed more complex models, suggesting that the underlying relationships between input variables and paddy yield in the dataset are predominantly linear. These findings highlight the importance of systematic model evaluation and demonstrate that simpler regression models can remain effective and interpretable for practical paddy yield prediction and agricultural decision support systems.]]>
<![CDATA[Sentiment Analysis Model for the Free Lunch Program in Indonesia on Twitter (X) Based on Machine Learning ]]>
<![CDATA[Amelia Tifany Dewi]]>;
<![CDATA[Nur Alamsyah]]>;
<![CDATA[Sinaga, Arnold Ropen]]>
<![CDATA[ Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 3 (2026): BIMA March 2026 Issue ]]>
Publisher : <![CDATA[Maheswari Publisher ]]>
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DOI: 10.65780/bima.v1i3.18
<![CDATA[Social media has become a primary platform for the public to voice their opinions on various public policies, including the free lunch program initiated by the Indonesian government. This study aims to analyze public sentiment toward this program through the Twitter (X) platform by utilizing machine learning algorithms. Data collection was conducted from January 2025 to June 2025, with a total of 2,045 comments successfully gathered. Sentiment labeling was performed manually, and only positive and negative sentiments were considered. The data, in the form of relevant comments, were pre-processed and classified into positive and negative sentiments. Three algorithms used in this study are Support Vector Machine (SVM), Naïve Bayes, and Random Forest. Evaluation was performed using data splitting schemes of 70:30 and 80:20, along with 5-fold cross-validation. Unlike previous studies, which primarily focused on sentiment analysis of general social issues or specific topics without emphasizing public policy, this study specifically investigates the public's sentiment regarding a government policy (the free lunch program) and compares the performance of different machine learning models. The results of the study show that the Random Forest model outperformed SVM and Naïve Bayes, achieving an accuracy of 89.41% with a standard deviation of 0.0138. Meanwhile, SVM achieved an accuracy of 88.96% and Naïve Bayes 88.72%. These findings suggest that Random Forest is the most optimal and consistent model for sentiment analysis of public policies on social media.]]>
<![CDATA[Ensemble Learning for Early Warning Systems in Higher Education: A Comparative Study of Student Attrition ]]>
<![CDATA[Arifudin, Muhamad Achya]]>;
<![CDATA[Setiana, Elia]]>;
<![CDATA[Nugraha, Arif Bakti]]>
<![CDATA[ Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 3 (2026): BIMA March 2026 Issue ]]>
Publisher : <![CDATA[Maheswari Publisher ]]>
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DOI: 10.65780/bima.v1i3.19
<![CDATA[Student attrition poses a substantial challenge to higher education institutions, affecting their reputation and financial sustainability. Conventional single machine learning models often exhibit limited sensitivity when analyzing educational data, which is typically marked by severe class imbalance favoring graduating students over dropouts. This study introduces an Early Warning System based on a Hybrid Stacking Ensemble framework to improve student attrition prediction. The approach leverages complementary biases from Bagging and Boosting as base learners, which are then combined using a Logistic Regression meta-learner to refine prediction weights. To counteract class imbalance and majority-class bias, the Synthetic Minority Over-sampling Technique was employed during preprocessing. Empirical evaluations reveal that the Hybrid Stacking Ensemble attains a classification accuracy of 88.81% and a Recall of 80.99%, surpassing standalone models and other ensemble methods. Feature importance rankings highlight second-semester academic performance and administrative-financial factors—particularly tuition payment punctuality—as key dropout predictors. These results affirm the value of integrating diverse classifiers to discern intricate, nonlinear student behavior patterns. In essence, this work establishes a reliable, evidence-based framework enabling administrators to shift from reactive to proactive, precision-targeted strategies that foster student retention and institutional success.]]>
<![CDATA[Early Stage Diabetes Prediction using Machine Learning with Hyperparameter Tuning GridSearchCV ]]>
<![CDATA[Deden Alif]]>;
<![CDATA[Prima Rexa Waluya]]>;
<![CDATA[Ikhsan Hadian Permana]]>
<![CDATA[ Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 3 (2026): BIMA March 2026 Issue ]]>
Publisher : <![CDATA[Maheswari Publisher ]]>
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DOI: 10.65780/bima.v1i3.15
<![CDATA[This study evaluates the performance of ensemble-based machine learning models for early-stage diabetes prediction. Three classifiers Random Forest, XGBoost, and LightGBM were assessed under baseline and hyperparameter-tuned configurations using an 80–20 train–test split. Model performance was measured using accuracy, precision, recall, and F1-score. The results show that all models achieved high predictive performance, with test accuracy reaching up to 99.04%. Random Forest demonstrated stable and consistent results without significant improvement after tuning. XGBoost showed performance enhancement after hyperparameter optimization, improving its generalization ability. LightGBM achieved competitive baseline performance but experienced a slight decrease after tuning. Learning curve analysis indicates that all models benefit from increased training data, with reduced overfitting as dataset size grows. Overall, Random Forest and tuned XGBoost emerged as the most reliable models for early-stage diabetes prediction, demonstrating strong generalization and high classification accuracy.]]>
