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All Journal Applied Information System and Management Bulletin of Intelligent Machines and Algorithms
Muhamad Achya Arifudin
Universitas Informatika dan Bisnis Indonesia
Computer Science & IT Languange, Linguistic, Communication & Media

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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.
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.
A Systematic Review of Deep Learning and Computer Vision Methods for Accurate Object Volume Measurement Muhamad Achya Arifudin; Kusrini; Andi Sunyoto; Ferry Wahyu Wibowo
Applied Information System and Management (AISM) Vol. 9 No. 1 (2026): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v9i1.50286

Abstract

Precise and efficient object volume measurement is crucial across diverse industrial domains, including logistics, manufacturing, and agriculture, where traditional methods are often labor-intensive and error-prone. DL and CV technologies offer a compelling alternative, providing greater accuracy, speed, and safety through non-contact, real-time, and adaptive solutions. To address existing knowledge gaps, this SLR is believed to be the first to integrate and analyze the three critical dimensions of DL holistically- and CV-based volume measurement: core methodologies, practical applications across various industries, and outstanding challenges, thereby providing a unified and comprehensive understanding of the state of the art that previous, more fragmented reviews have failed to deliver. Regarding methodology, dominant DL techniques include CNNs, Mask R-CNN, and U-Net for segmentation; GANs for 3D model generation; and PointNet/voxel networks for 3D data processing, with sensor integration impacting model architecture. Applications span agriculture, logistics, manufacturing, and construction, demonstrating high accuracy with error rates as low as 0.75% and MAPE typically ranging from 3.2% to 5%. Challenges involve occlusions, diverse environmental conditions, data scarcity, and computational costs. Prioritized research directions include lightweight models, multi-task learning, improved generalization, greater robustness, and explainable AI. Overall, this SLR comprehensively synthesizes current DL and CV methodologies, their practical applications, and future research directions in object volume measurement.
Co-Authors Andi Sunyoto Ferry Wahyu Wibowo Kusrini Nugraha, Arif Bakti Nur Alamsyah Setiana, Elia