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
30 Documents
<![CDATA[A Progressive Training Framework for Robust YOLOv11-Based Vehicle Detection Across Domain Shifts in Real-World CCTV Environments ]]>
<![CDATA[Aditya Gunaldhi]]>;
<![CDATA[Budiman]]>;
<![CDATA[Chairul Habibi]]>;
<![CDATA[Nur Alamsyah]]>
<![CDATA[ Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 5 (2026): BIMA July 2026 Issue ]]>
Publisher : <![CDATA[Maheswari Publisher ]]>
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DOI: 10.65780/bima.v1i5.27
<![CDATA[The performance of deep learning-based vehicle detection models often deteriorates when applied to real-world CCTV environments due to domain shift caused by variations in lighting, occlusion, glare, and changes in camera viewpoint. This study aims to develop a YOLOv11-based progressive training framework to improve the model’s generalization ability under heterogeneous operational conditions. The proposed method consists of four stages: base training, fine-tuning, and a supervised progressive domain adaptation strategy implemented through sequential fine-tuning on labeled target-domain CCTV images before OpenVINO-based inference optimization. The model is trained using a source dataset and adapted to a target dataset representing real-world CCTV conditions. Evaluation was conducted using Precision, Recall, mAP@50, mAP@50–95, loss curve analysis, per-vehicle-class evaluation, visual testing on CCTV video, as well as latency and throughput measurements. The results show that base training achieved an mAP@50 of 0.919 and built a robust feature representation, while fine-tuning maintained performance stability with an mAP@50 of 0.915. Although domain adaptation reduced mAP@50 to 0.811 due to domain shift, the model demonstrated improved generalization capabilities and maintained more consistent detection under low-light conditions, glare, occlusion, and heavy traffic. OpenVINO INT8 optimization increased inference speed from 4.86 FPS to 9.08 FPS with minimal accuracy loss. These findings demonstrate that the progressive training framework effectively bridges differences in data distribution while producing a vehicle detection model that is more robust, efficient, and suitable for deployment in real-time CCTV-based traffic monitoring systems.]]>
<![CDATA[Content-Based Filtering with TF-IDF and Cosine Similarity for Bandung Raya Tourism Recommendation ]]>
<![CDATA[Mira Aldina]]>;
<![CDATA[Galih]]>;
<![CDATA[Siti Nur]]>
<![CDATA[ Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 5 (2026): BIMA July 2026 Issue ]]>
Publisher : <![CDATA[Maheswari Publisher ]]>
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DOI: 10.65780/bima.v1i5.28
<![CDATA[Bandung Raya offers hundreds of tourist destinations spread across Bandung City, Bandung Regency, and West Bandung Regency, which causes information overload and makes it difficult for tourists to select destinations that match their preferences. Recommender systems, as one of the most widely applied branches of machine learning, provide a way to filter such information automatically. This study develops a machine learning-based recommender system for tourism destinations in Bandung Raya using Content-Based Filtering, in which destination descriptions are represented as numerical vectors through TF-IDF weighting and compared using Cosine Similarity. The dataset consists of 331 destinations with name, description, category, and region attributes. Text preprocessing is performed in five stages using the Sastrawi library for the Indonesian language, producing a TF-IDF matrix of 331 by 583 and a similarity matrix of 331 by 331. The model is deployed as a website using Flask as the backend, React as the frontend, and a REST API as the interface, supporting both name-based search and free-text query search. Functional validation uses Black-Box Testing, while recommendation quality is measured using Precision at K and Mean Average Precision. All eight functional scenarios passed, with Precision at 5 of 96.00 percent, Precision at 10 of 90.00 percent, and Mean Average Precision of 98.86 percent, indicating that, for the five evaluated queries, relevant destinations are ranked highly.]]>
<![CDATA[A Trigger Aware, Event Centric, and Uncertainty Calibrated Neuro-Symbolic Framework for Actionable Cyber Threat Intelligence from Indonesian Online News ]]>
<![CDATA[Elia Setiana]]>;
<![CDATA[Muhamad Achya Arifudin]]>;
<![CDATA[Nur Alamsyah]]>
<![CDATA[ Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 5 (2026): BIMA July 2026 Issue ]]>
Publisher : <![CDATA[Maheswari Publisher ]]>
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DOI: 10.65780/bima.v1i5.31
<![CDATA[Online news can provide timely cyberthreat signals, but duplicative reporting, fragmented event descriptions, resource-constrained Indonesian language text, and uncalibrated model confidence limit its operational use. This study presents A Trigger-Aware, Event-Centric, and Uncertainty-Calibrated Neuro-Symbolic Framework for Actionable Cyber Threat Intelligence from Indonesian Online News (TRACE-CTI-ID), a proof-of-concept framework that integrates exact deduplication, event-centric clustering, trigger-aware semantic representation, neuro-symbolic fusion, ordinal risk estimation, conformal uncertainty, mitigation mapping, and an event-centric knowledge graph. The experiment used 711 Liputan6 records collected on March 14, 2025. Exact deduplication reduced the corpus to 79 unique headlines, which were automatically consolidated into 26 events. Splitting the separate events resulted in 30 training articles, 5 calibration articles, and 44 test articles with zero event leakage. The calibrated neuro-symbolic model achieved a micro-F1 of 0.283 and a macro-F1 of 0.441, outperforming the baseline TF-IDF of 0.074 and 0.013, respectively. However, ordinal severity prediction remained weak with an accuracy of 0.136, a macro-F1 of 0.138, and a mean absolute error of 2.023. Conformal coverage was also unstable, and the abstention mechanism did not direct uncertain articles to human review. These findings demonstrate the technical feasibility of the integrated pipeline while also demonstrating that silver labeled, title only, and single source data are insufficient for final operational validation. Therefore, the key contribution is a transparent, leak aware evaluation architecture and protocol that can be strengthened through full text collection from multiple sources and independent expert annotation.]]>
<![CDATA[Intelligent Cold Chain Control System Based on Data Fusion for Refrigerated Truck Fleets ]]>
<![CDATA[Luckman Hasannudin]]>;
<![CDATA[Very Kurnia Bakti]]>;
<![CDATA[Abdul Basit]]>
<![CDATA[ Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 5 (2026): BIMA July 2026 Issue ]]>
Publisher : <![CDATA[Maheswari Publisher ]]>
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DOI: 10.65780/bima.v1i5.32
<![CDATA[As a maritime nation, Indonesia holds substantial marine-resource potential, yet its distribution faces serious challenges because fishery commodities are highly perishable and prone to rapid quality decline. Failure to maintain consistent temperatures throughout the cold chain frequently causes significant economic losses for business operators. This study aims to design and build an Intelligent Cold Chain Control System Based on Data Fusion for Refrigerated Truck Fleets using an ESP32-S3 microcontroller. The system integrates Internet of Things (IoT) technology with a data fusion technique that combines readings from an RTD PT100 temperature sensor paired with a MAX31865 module, an MPU6050 vibration sensor, and a NEO-8M GPS module for real-time location tracking, using an SD card as a buffer medium and the MQTT protocol for transmitting data to a server. The research method applied is the Prototype method, comprising requirements analysis, initial system design, prototype development, user evaluation, prototype improvement, prototype testing, and implementation. Data were collected through field observation, interviews, and a literature study at CV. Mutiara Samudera Indonesia, Tegal. Test results show that the PT100 sensor was able to read temperatures down to −15.5°C with a deviation of only 0.2°C against a reference instrument, the NEO-8M GPS module successfully displayed location coordinates within a tolerance of a few meters compared with Google Maps, and the MPU6050 sensor was able to detect vibration changes on three axes in a stable manner. This prototype is expected to support quality monitoring and distribution efficiency for marine commodities carried by refrigerated truck fleets.]]>
<![CDATA[Topic Modelling of TikTok Application User Complaints in Google Play Store Reviews Using Latent Dirichlet Allocation ]]>
<![CDATA[Ghevira Devanda]]>;
<![CDATA[Inaya Zehan Kalyzta]]>;
<![CDATA[Zaqi Kurniawan]]>
<![CDATA[ Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 5 (2026): BIMA July 2026 Issue ]]>
Publisher : <![CDATA[Maheswari Publisher ]]>
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DOI: 10.65780/bima.v1i5.33
<![CDATA[TikTok is a short-video-sharing platform with an extremely large user base in Indonesia, and as a result it also receives numerous negative reviews concerning both technical performance and platform policies on the Google Play Store. This study aims to identify and cluster the main topics of TikTok user complaints using a Machine Learning-based approach built on the Latent Dirichlet Allocation (LDA) algorithm. Data were collected through a quota-based web scraping procedure using the google-play-scraper library, targeting Indonesian-language reviews with a rating below three (rating 1 and 2) up to a target of 1,000 reviews, yielding a raw sample of 1,000 reviews (814 one-star and 186 two-star). The preprocessing stage included cleaning, case folding, normalization using a colloquial lexicon enriched with a TikTok domain-specific correction dictionary, stopword removal, and stemming using Sastrawi, which reduced the data to 814 clean documents. Document representation was constructed using a Bag of Words (BoW) approach through the Gensim library, with vocabulary filtering (no_below = 3, no_above = 0.6) that produced a dictionary consisting of 397 unique tokens. The optimal number of topics was determined through a multi-metric evaluation combining Coherence Score (u_mass), Coherence Score (c_v), and Perplexity for candidate models ranging from two to five topics, complemented by a manual check of topic interpretability. Despite mixed signals from the c_v and Perplexity metrics, the two-topic model was selected based on its u_mass score (-8.7713) and its more interpretable, non-fragmented thematic structure. The document distribution results show that Topic 1 is more dominant, comprising 445 documents (54.67%), representing technical application complaints such as bugs, slow performance, failure to open the application, and excessive advertisements. Meanwhile, Topic 2, with 369 documents (45.33%), relates to complaints about content moderation and restrictions on the live streaming feature. Visualization using pyLDAvis confirmed that the two topics are clearly separated without overlap, reinforcing the validity of the modelling results. As the sample is restricted to Indonesian-language, one- and two-star reviews collected at a single point in time, the findings are intended to reflect this specific segment of TikTok users rather than the full population of TikTok reviews. This study provides objective insights for application developers in prioritizing system improvements and evaluating content moderation policies more effectively.]]>
<![CDATA[Comparison of Naive Bayes, SVM, and Logistic Regression for Sentiment Analysis of the Makan Bergizi Gratis Program ]]>
<![CDATA[Andika]]>;
<![CDATA[Julio Cesar Alessandro]]>;
<![CDATA[Adjie Perkasa Tarigan]]>;
<![CDATA[Zaqi Kurniawan]]>
<![CDATA[ Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 6 (2026): BIMA issue September 2026 ]]>
Publisher : <![CDATA[Maheswari Publisher ]]>
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DOI: 10.65780/bima.v1i6.34
<![CDATA[The Makan Bergizi Gratis Program (MBG) became one of the widely discussed public issues on platform X and generated diverse responses from users. These responses included supportive, critical, and neutral opinions, making sentiment analysis relevant for understanding public opinion toward the program. This study compares three text classification algorithms, namely Naive Bayes, LinearSVC based Support Vector Machine, and Logistic Regression, to analyze the sentiment of tweets concerning the Makan Bergizi Gratis Program. Data were collected from platform X on 19 June 2026 using tweet crawling and were subsequently filtered and manually annotated. The final dataset consisted of 1,061 Indonesian language tweets classified into negative, neutral, and positive sentiment. Two annotators independently assigned sentiment labels, followed by discussion to resolve disagreements. The inter annotator agreement reached 94.62%, with a Cohen's Kappa coefficient of 0.9097. Text preprocessing consisted of cleaning, normalization, tokenization, stopword removal, and stemming. TF-IDF features were generated within a pipeline to ensure that feature fitting was performed only on the training portion of each evaluation fold. The data were divided into training and testing sets using an 80:20 stratified split. Hyperparameter optimization was performed for each classifier, followed by evaluation using accuracy, macro precision, macro recall, macro F1 score, confusion matrix, and stratified 5 fold cross validation. The test results show that SVM achieved the highest accuracy of 72.77%, precision of 75.11%, recall of 65.17%, and macro F1 score of 68.28%. Logistic Regression obtained a macro F1 score of 67.05%, while Naive Bayes obtained 63.69%. SVM also achieved the highest average macro F1 score in cross validation at 60.25%, compared with 59.94% for Logistic Regression and 56.53% for Naive Bayes. These results indicate that SVM provided the strongest overall performance for the dataset examined in this study.]]>
<![CDATA[Feature Selection and Data Imbalance Handling for Breast Cancer Classification Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset ]]>
<![CDATA[Yoga Rizki Rahmawan]]>;
<![CDATA[Budiman]]>
<![CDATA[ Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 6 (2026): BIMA issue September 2026 ]]>
Publisher : <![CDATA[Maheswari Publisher ]]>
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DOI: 10.65780/bima.v1i6.39
<![CDATA[Data quality is one of the factors that influence the performance of machine learning algorithms in classification tasks. Issues such as a high number of features and class distribution imbalance can lead to a decrease in accuracy and the model's ability to recognize data patterns. This study aims to analyze the impact of feature selection methods and data imbalance handling techniques on the performance of a breast cancer classification model using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The feature selection methods used include Principal Component Analysis (PCA), Chi-Square, SelectKBest, and Recursive Feature Elimination (RFE), while the data balancing techniques applied are SMOTE and UnderSampling. All combinations of these methods were tested using the Logistic Regression, Decision Tree, and Random Forest algorithms. The results of the study show that feature selection methods and data imbalance handling techniques influence the performance of classification models. Numerically, the combination of PCA and undersampling in the logistic regression algorithm yielded the highest performance, with an accuracy of 97%, precision of 95%, recall of 98%, and an F1-score of 96%. However, based on an evaluation of the learning curve, generalization ability, and model interpretability, the combination of Chi-Square and undersampling with the Logistic Regression algorithm was selected as the best model. This combination yielded an accuracy of 95%, precision of 95%, recall of 90%, and an F1-score of 93%, and demonstrated a "good fit" with strong generalization ability. The research results prove that selecting the appropriate preprocessing method can improve the quality and reliability of breast cancer classification models.]]>
<![CDATA[Web-Based Extreme Weather Monitoring and Early Warning System for Traditional Fishermen Using IoT Sensor Data ]]>
<![CDATA[Alam Kibar]]>
<![CDATA[ Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 6 (2026): BIMA issue September 2026 ]]>
Publisher : <![CDATA[Maheswari Publisher ]]>
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DOI: 10.65780/bima.v1i6.43
<![CDATA[Extreme weather can disrupt traditional fishing activities and increase safety risks, particularly when local weather information is difficult to access before departure. This study develops a web-based extreme weather monitoring and early warning system for traditional fishermen using Internet of Things (IoT) sensor data. The system integrates an ESP32 microcontroller with a DHT22 sensor, rain sensor, and anemometer to acquire temperature, humidity, rainfall, and wind-speed data at ten-second intervals. Sensor data are processed using thresholds combined with a scoring scheme, transmitted through HTTP to a PHP server, stored in MySQL, and visualized through a web dashboard. The system also provides Telegram Bot notifications at 06:00 WIB and 17:00 WIB and event-triggered warnings when the classified condition reaches the danger category. Functional testing using black-box testing produced successful results for the tested features, while Perfetto UI measurements showed response times between 0.56 and 1.34 seconds, with an average of 0.95 seconds. The implementation demonstrates that a low-cost, locally deployed IoT monitoring system can combine real-time weather observation, simple risk classification, web visualization, and automatic notification to support weather awareness before traditional fishermen depart.]]>
<![CDATA[Classification of Pension Program Service Workload Levels Across Payment Cities at PT ASABRI (Persero) Bandung Branch Office Using Machine Learning Models ]]>
<![CDATA[Angela Nisrina Zhafir]]>;
<![CDATA[Nur Alamsyah]]>
<![CDATA[ Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 6 (2026): BIMA issue September 2026 ]]>
Publisher : <![CDATA[Maheswari Publisher ]]>
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DOI: 10.65780/bima.v1i6.44
<![CDATA[This study classifies service workload levels across 13 cities served by the PT ASABRI (Persero) Bandung Branch Office to mitigate evaluation bias by implementing the CRISP-DM methodology on transaction data from 2023 to 2024. The target variable was constructed by combining multiple operational variables into a composite score index, using percentile cutoffs to define High, Medium, and Low workload categories. Under an Out-of-Time Validation (OOTV) framework, data from 2023 served as training data and data from 2024 as testing data, followed by the application of the Synthetic Minority Oversampling Technique (SMOTE) exclusively to the training data to neutralize class imbalance. Comparative evaluation identified a Random Forest model optimized via RandomizedSearchCV as the top-performing model, achieving an accuracy of 83.33%, a macro F1-score of 81.17%, and minimal performance discrepancy between training and test sets. Architecture stability was demonstrated through 5-fold cross-validation, yielding an average accuracy of 72.42% with a minimal standard deviation of 5.69%. Global Feature Importance analysis identified the total number of family dependents (TOTAL_JIWA_DILAYANI) as the dominant operational factor, with an importance score of 0.4145. This comprehensive modeling provides a measurable Early Warning System that enables management to prioritize evaluation areas more systematically while minimizing subjective assumptions.]]>
<![CDATA[Optimization of Cross-Selling Strategies and Retail Product Layouts of Building Materials Based on Market Basket Analysis Using the Apriori Algorithm ]]>
<![CDATA[Yolanda Joice Maria Lumbantobing]]>;
<![CDATA[Nur Alamsyah]]>;
<![CDATA[Titan Parama Yoga]]>
<![CDATA[ Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 6 (2026): BIMA issue September 2026 ]]>
Publisher : <![CDATA[Maheswari Publisher ]]>
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DOI: 10.65780/bima.v1i6.46
<![CDATA[In an era of increasingly fierce retail competition, optimizing product layout and sales strategies is a matter of urgency for businesses seeking to maintain and increase revenue. CV. Tolong Kita Jaya, a building materials store, is currently facing challenges in determining an effective product placement strategy and has not yet maximized its cross-selling potential. This is due to a lack of analysis of past consumer purchasing patterns, resulting in frequent missed opportunities to sell related products. Therefore, this study aims to address this issue by implementing a data mining approach. This study employs the comprehensive Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, ranging from business understanding to the evaluation stage. For modeling, this study implements Market Basket Analysis (MBA) using the Apriori algorithm. This model was specifically chosen to extract association rules from a set of historical transaction data to identify relationships among building materials that are frequently purchased together. The test results from this modeling revealed several product combinations with high and valid support, confidence, and lift ratio metrics. The key findings of this study provide a strategic foundation for redesigning retail layouts, in which items with strong associations are positioned in close proximity. Furthermore, these findings facilitate the formulation of actionable cross-selling strategies by management, such as the creation of promotional sales bundles, which are ultimately projected to increase the overall sales volume of building materials.]]>
