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All Journal Jurnal Computech & Bisnis (e-Journal) Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Innovation in Research of Informatics (INNOVATICS) International Journal of Quantitative Research and Modeling Jurnal Pengabdian Masyarakat Tapis Berseri SisInfo : Jurnal Sistem Informasi dan Informatika JUSTIFY : Jurnal Sistem Informasi Ibrahimy Nuansa Informatika Jurnal Bhakti Karya dan Inovatif Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism) International Journal of Science and Mathematics Education International Journal of Computer Technology and Science Bulletin of Intelligent Machines and Algorithms
Setiana, Elia
Universitas Informatika Dan Bisnis Indonesia
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Mathematics Physics Social Sciences Other

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Journal : bulletin of intelligent machines and algorithms

 1 
Explainable Deep Transfer Learning for Robust Tomato Leaf Disease Classification Elia Setiana; Mukhammad Restu Febriansyah Putra; Muhammad Fajar Romadhon
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 1 (2025): BIMA November 2025 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i1.4

Abstract

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
Ensemble Learning for Early Warning Systems in Higher Education: A Comparative Study of Student Attrition Muhamad Achya Arifudin; Elia Setiana; Arif Bakti Nugraha
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 3 (2026): BIMA March 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i3.19

Abstract

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.
Enhancing Breast Cancer Diagnosis with Ensemble Learning: Leveraging Convolutional Neural Networks and Pretrained Models through Averaged Predictions Elia Setiana; Reni Nursyanti; Nur Alamsyah; Nayla Nurul Azkiya
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 4 (2026): BIMA May 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i4.26

Abstract

Breast cancer continues to be a serious health issue at the global level, and early detection can significantly improve patient outcomes. This research uses imaging techniques to examine the design of an improved classification model in breast cancer detection. This project uses deep learning approaches through Convolutional Neural Networks (CNN) and ensemble learning models to potentially improve classification accuracy. To further enhance performance while controlling for class imbalance and overfitting, we leverage several models, such as ResNet18 and VGG16, with data augmentation and pre-trained models. Our methods included standard preprocessing of medical images, splitting datasets into training, testing and validation sets, and training each model with the Adam optimizer. Performance measurement included accuracy, precision, recall, and F1 score metrics. Overall, prototypes recently created displayed clear advantages based on finding results achieved through an ensemble method, which demonstrated improved model stability and reduced significant misclassification errors, and model accuracy reached 0.96. This research is crucial while developing strong deep-learning models to aid in breast cancer detection, ultimately allowing us to set a base for developing better diagnostic inference systems in medical-based applications. These systems may help improve early detection and overall patient care.
A Trigger Aware, Event Centric, and Uncertainty Calibrated Neuro-Symbolic Framework for Actionable Cyber Threat Intelligence from Indonesian Online News Elia Setiana; Muhamad Achya Arifudin; Nur Alamsyah
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 5 (2026): BIMA July 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i5.31

Abstract

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.
Co-Authors Acep Hendra Aggi Panigoro Sarifiyono Akbar, Imannudin Balkhi Razzas Qasvini Budiman Budiman Budiman Chairul Habibi Danestiara, Venia R Dhani Ariatmanto Dirham Triyadi Dirham Triyadi Graha Prakarsa Hamzah, Encep Ikhsan Nur, Muhammad Kaunang, Valencia Claudia Jennifer Kusrini M. Rizki Ramadhan Marwondo Muhamad Achya Arifudin Muhamad Arya Ananda Muhammad Fajar Romadhon Muhammad Rizki Ramadhan Mukhammad Restu Febriansyah Putra Muliasari, Lina Nayla Nurul Azkiya Nugraha, Arif Bakti NUR ALAMSYAH Nur Alamsyah Nur Alamsyah Nursyanti, Reni Osin Lirfaija Salamanang R. Yadi Rakhman A R. Yadi Rakhman A4 R. Yadi Rakhman Alamsyah Reni Nursyanti Reni Nursyanti Reni Nursyanti Rijwan Rijwan S.W. Manurip, Atanasius Angga Sardjono Sardjono Syahira Putri Himmaniah Taufik Hidayah Teofilus Hendrawan, Yesaya Titan Parama Yoga TONNY HIDAYAT Valencia Claudia Jennifer Valencia Claudia Jennifer Kaunang Valencia Claudia Jennifer Kaunang Valencia Claudia Jennifer Kaunang Venia R Danestiara Venia Restreva Danestiara Wiyanudin