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. 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. Indexing & Abstracting International Libraries Journal Sponsors & Partners
Early Stage Diabetes Prediction using Machine Learning with Hyperparameter Tuning GridSearchCV
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.
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Ensemble Learning for Early Warning Systems in Higher Education: A Comparative Study of Student Attrition
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.
Sentiment Analysis Model for the Free Lunch Program in Indonesia on Twitter (X) Based on Machine Learning
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.
Comparative Analysis of Machine Learning Regression Models for Paddy Yield Prediction
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.
Machine Learning Based Cervical Cancer Risk Prediction with SHAP-Driven Feature Interpretation
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.
Interpretable Multiclass Obesity Classification Using Optimized Logistic Regression Based on Anthropometric and Lifestyle Data
Obesity is a global public health challenge associated with increased risks of chronic diseases and significant socioeconomic burdens. Conventional obesity classification relies predominantly on body mass index (BMI), which is static and insufficient to capture the multidimensional nature of lifestyle and behavioral factors. This study aims to develop an adaptive and interpretable machine learning–based framework for multiclass obesity classification that addresses the limitations of BMI-centered approaches. An optimized Logistic Regression model is proposed and evaluated using anthropometric and lifestyle-related features, including dietary habits and physical activity patterns. The methodology involves comprehensive data preprocessing, feature encoding, stratified data splitting, hyperparameter optimization, and performance evaluation using confusion matrix analysis, learning curves, and SHAP-based interpretability. Experimental results demonstrate that the optimized Logistic Regression model achieves a high classification accuracy of 94.26% on the test dataset, accompanied by stable generalization performance, as indicated by a relatively small generalization gap between training and validation data. Learning curve analysis confirms robust learning behavior without significant overfitting, while SHAP analysis reveals that both anthropometric and lifestyle features contribute meaningfully to classification decisions. The findings indicate that Logistic Regression offers a balanced trade-off between predictive performance, generalization ability, and interpretability. This study demonstrates that an interpretable, data-driven machine learning approach can serve as a reliable alternative to conventional obesity classification frameworks and support decision-making in health-related applications.
Improving Dengue Case Prediction in Bandung City using Random Forest and SHAP on Climate Demographic Data
Dengue Hemorrhagic Fever remains a major public health concern in urban areas of Indonesia, particularly in Bandung City, due to its fluctuating incidence and strong dependence on environmental and population factors. This study focuses on improving dengue case prediction by integrating climate and demographic data through systematic feature engineering and explainable machine learning based on the Random Forest algorithm. Historical dengue case data from Bandung City were used to develop and evaluate the proposed prediction model. The evaluation results show that the Random Forest model achieved an R² value of 0.9032 and an RMSE of 2.3748, indicating reliable predictive performance and good generalization capability. The applied feature engineering strategy effectively enhanced data representation by capturing temporal dynamics, case growth patterns, and interactions among climate variables. Furthermore, model interpretability was improved through the application of Explainable Artificial Intelligence using SHAP, which revealed that temporal features derived from previous dengue case trends were the most influential factors, followed by climate interaction variables. These findings demonstrate that the proposed approach improves prediction accuracy while providing transparent and epidemiologically meaningful insights to support data driven dengue early warning systems at the regional level.
High-Recall URL Phishing Detection via Multilayer Perceptron: Feature Selection, Learning Curves, and Confusion-Matrix Verification
Phishing attacks that exploit malicious URLs remain a significant and growing threat in the modern digital ecosystem due to their low operational costs, high scalability, and effectiveness in deceiving users. As more and more online services support important activities such as banking, e-commerce, government, and education, the need for fast, accurate, and lightweight phishing detection mechanisms is becoming increasingly urgent. This study proposes an end-to-end URL-based phishing detection framework that emphasizes reproducibility, robustness, and operational feasibility, with a particular focus on the Multilayer Perceptron (MLP) classifier. Using the PhiUSIIL phishing URL dataset, this research evaluates the performance of MLP against nine widely used machine learning algorithms, including linear, probabilistic, tree-based, and ensemble models. The methodology integrates systematic data cleaning, hierarchical data partitioning, feature normalization, ANOVA-based feature selection, and class imbalance handling to ensure fair and consistent evaluation. Model performance is assessed using accuracy, precision, recall, and F1-score, complemented by learning curve analysis and confusion matrix verification to examine generalization stability and critical error patterns. Experimental results show that while most models achieve very high overall performance, the MLP classifier consistently demonstrates superior stability and detection capabilities, achieving accuracy (99.98%), precision (99.97%), recall (100%), and F1-score (99,98%) with zero false negatives in phishing classification. These findings confirm that lexical and structural URL features alone are sufficient for effective phishing detection and highlight MLP as a practical, efficient, and reliable model for application in large-scale, real-time cybersecurity environments.
Phishing Detection, URL-Based Classification, Multilayer Perceptron, Machine Learning, Feature Selection, Cybersecurity
Explainable Machine Learning For Early HIV Detection Using Extra Trees and SHAP Algorithms
Human Immunodeficiency Virus (HIV) remains a global health challenge that requires accurate and reliable early detection approaches. The use of machine learning offers potential in classifying HIV status based on clinical, demographic, and behavioral data. However, the limitations of interpretability in black-box models are an obstacle to clinical application. This study proposes an Explainable Machine Learning approach for early HIV detection by integrating the Extra Trees algorithm and the Shapley Additive exPlanations (SHAP) method. The model was developed using an HIV dataset obtained from the Kaggle platform and processed through standard data preprocessing stages without class balancing. Performance evaluation was conducted using classification metrics, confusion matrices, and learning curves to assess accuracy and learning stability. The results of the experiment show that the Extra Trees model achieved 88% accuracy with strong generalization. SHAP and mean absolute SHAP analyses revealed the dominant features that contributed to the prediction of HIV status consistently at the global and local levels. These findings show that integrating Extra Trees and SHAP produces an HIV early-detection model that is not only competitive in performance but also transparent and clinically relevant, potentially supporting the development of reliable artificial intelligence-based medical decision support systems.
HIV; early detection; Extra Trees; Explainable Machine Learning; SHAP
An LSTM-Based Approach for Short-Term Solar Power Forecasting with Diurnal and Intra-Day Variability
The increasing penetration of solar photovoltaic (PV) systems into modern power grids demands accurate, reliable short-term power forecasting to ensure operational stability and efficient energy management. However, solar power generation exhibits strong nonlinearity, non-stationarity, and pronounced temporal dependencies, driven by diurnal cycles and rapid environmental variations, which pose significant challenges for conventional forecasting approaches. This study aims to develop an efficient Long Short-Term Memory (LSTM)-based framework for short-term DC power prediction that effectively captures the temporal dynamics of solar power generation while maintaining low computational complexity. The proposed approach utilizes historical power and operational data collected from two utility-scale solar PV plants in India. A comprehensive time-series preprocessing pipeline is applied, including temporal feature extraction, categorical transformation, and Min–Max normalization. Multiple LSTM architectures with varying numbers of hidden units are systematically evaluated to identify an optimal balance between model complexity and predictive performance. Model training is conducted using the Adam optimizer with exponential learning rate decay and early stopping to prevent overfitting. Experimental results demonstrate that the proposed LSTM model with a 25–50 unit configuration achieves the best performance, yielding a test Mean Squared Error of 51.92 and a prediction error of only 0.36%. Visual and quantitative analyses confirm that the model accurately reconstructs diurnal patterns and intra-day fluctuations, with strong generalization capability on unseen data. The findings indicate that a carefully configured LSTM can deliver high forecasting accuracy without relying on complex hybrid architectures or additional weather data, making it suitable for practical solar energy management applications.
High-Precision Credit Card Fraud Detection on Imbalanced Data Using Random Forest and 1D Convolutional Neural Networks
Credit card fraud has become a significant challenge for the financial industry, resulting in substantial monetary losses and eroding consumer trust. Detecting fraudulent transactions is particularly challenging due to the severe class imbalance and high dimensionality of transaction data. This study proposes a systematic pipeline for fraud detection, integrating stratified sampling, Synthetic Minority Over-sampling Technique (SMOTE), and comparative evaluation of Random Forest (RF) and 1D Convolutional Neural Network (CNN) models. The performance of both models is assessed using standard metrics, including Accuracy, Precision, Recall, F1-Score, and Area Under the Receiver Operating Characteristic Curve (AUC). Experimental results demonstrate that RF achieves high precision (99.45%) on unseen test data, ensuring reliable detection of legitimate transactions. In comparison, CNN achieves near-perfect recall (99.95%) on training data, indicating a strong capacity to identify fraudulent patterns. Temporal analysis of transaction data further reveals distinct patterns between legitimate and fraudulent activities, highlighting the predictive importance of the Time feature. The findings provide practical guidance for deploying machine learning models in real-world financial settings: RF offers a reliable solution for immediate implementation, whereas CNN presents a promising approach for future enhancement after further validation.
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A Comprehensive Machine Learning Approach for Predicting Beats Per Minute (BPM) in Music Using Audio Features
Predicting Beats Per Minute (BPM) in music is a significant challenge due to the complexity of the relationship between various audio features, such as rhythm, energy, and mood. Traditional methods are often unable to handle the complexity of feature variations and interactions. This study aims to develop a more accurate and reliable machine learning model to predict song BPM based on extracted audio features. We use advanced machine learning algorithms, including LightGBM, XGBoost, and Random Forest, to train models with a dataset covering ten audio features. Evaluation is performed using a k-fold cross-validation scheme with RMSE, MAE, and R² Score metrics. The experimental results show that boosting-based models such as LightGBM produce the best performance, with the lowest RMSE of 10.48, the lowest MAE of 7.62, and the highest R² Score of 0.83. However, these models still show a tendency to regress to the mean, indicating that some more extreme BPM variations are not fully captured. These findings emphasize the importance of improvements in feature engineering techniques and data rebalancing to improve BPM prediction accuracy in practical applications, such as music recommendation systems and tempo analysis.
Explainable Deep Transfer Learning for Robust Tomato Leaf Disease Classification
Automated identification of plant diseases is crucial for advancing precision agriculture and enabling farmers to make informed, timely decisions. This study presents a deep learning-based framework for multi-class classification of tomato leaf diseases using transfer learning with the VGG-19 architecture. A dataset comprising 10,000 images across ten classes, including nine disease categories and one healthy class, was preprocessed and augmented to improve model robustness and generalization. The training strategy employed a two-stage approach: initial feature extraction with frozen, pre-trained layers, followed by selective fine-tuning to adapt the convolutional features to the target domain. Comprehensive evaluation using accuracy, precision, recall, F1-score, and confusion matrices demonstrated the model’s high discriminative capability, achieving an overall accuracy of 93% on the validation set. The results further revealed strong performance in identifying most disease categories, while highlighting classification challenges between visually similar classes, such as Tomato Mosaic Virus and Tomato Target Spot. The contributions of this research include the development of an optimized training pipeline, a reproducible evaluation framework, and insights into the role of transfer learning for agricultural image classification. The findings highlight the potential of deep learning to support automated tomato disease monitoring, with implications for improving crop health management and enhancing agricultural productivity
Comparative Analysis of Machine Learning Algorithms for Indonesian Twitter Sentiment Classification on the Jakarta–Bandung High-Speed Rail Project
The rapid growth of social media in Indonesia has opened up new opportunities to gauge public opinion on major national initiatives. One of the most controversial projects is the Jakarta–Bandung High-Speed Railway (KCJB), which has sparked mixed responses due to its financial, environmental, and socio-political implications. To meet the need for systematic analysis, this study applies sentiment analysis to Indonesian Twitter data to evaluate public perspectives on the KCJB project. This research uses a step-by-step methodology, including data collection via the Twitter API, text preprocessing, manual tagging into positive and negative sentiments, and feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Four machine learning algorithms—Naïve Bayes, Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Random Forest—were trained and verified on stratified data splits, with performance evaluated using accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). The results show that SVM consistently outperforms other models, achieving up to 73% accuracy with balanced precision and recall, as well as the highest AUC value. These findings confirm the robustness of SVM in handling high-dimensional Indonesian text. In addition to its academic contribution to sentiment analysis in languages with limited resources, this research offers practical implications by providing data-driven insights for policymakers and businesses for real-time monitoring, strategic communication, and informed decision-making.
Stacking Ensemble Revenue Predictions in Digital Marketing: A SHAP-Based Analysis of Price and Quantity as Key Predictors
This study focuses on developing a digital marketing conversion prediction model using an ensemble stacking approach combined with explainable artificial intelligence (XAI) methods to improve model transparency. The primary objective of this study is to investigate the impact of price and product quantity on revenue predictions, as well as to gain a clearer understanding of the factors that influence customer purchasing behaviour in the context of digital sales. The methodology used includes data collection from a Kaggle dataset containing 3,000 records and 15 features related to customer demographics, product information, and marketing channels. The preprocessing stage ensures data quality, followed by feature engineering and model development using an ensemble stacking model consisting of Logistic Regression, Gaussian Naïve Bayes, and Support Vector Classification. Model evaluation was conducted using precision, recall, F1-score, and ROC-AUC metrics, with performance improvements achieved through cross-validation and probabilistic calibration. The study results showed that model accuracy reached 0.97, with significant contributions from price and product quantity features, as seen in the SHAP analysis. The ensemble stacking model provided stable and reliable predictions. These findings underscore the importance of effective pricing strategies and product volume optimisation in driving revenue growth. The use of SHAP enhances interpretability, enabling businesses to make more informed decisions. This research contributes to the development of transparent and practical machine learning applications in digital marketing, providing valuable implications for business strategy optimization.