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Journal of Computing Theories and Applications
Published by Universitas Dian Nuswantoro
ISSN : -     EISSN : 30249104     DOI : 10.62411/jcta
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Journal of Computing Theories and Applications (JCTA) is a refereed, international journal that covers all aspects of foundations, theories and the practical applications of computer science. FREE OF CHARGE for submission and publication. All accepted articles will be published online and accessed for free. The review process is carried out rapidly, about two until three weeks, to get the first decision. The journal publishes only original research papers in the areas of, but not limited to: Artificial Intelligence Big Data Bioinformatics Biometrics Cloud Computing Computer Graphics Computer Vision Cryptography Data Mining Fuzzy Systems Game Technology Image Processing Information Security Internet of Things Intelligent Systems Machine Learning Mobile Computing Multimedia Technology Natural Language Processing Network Security Pattern Recognition Signal Processing Soft Computing Speech Processing Special emphasis is given to recent trends related to cutting-edge research within the domain. If you want to become an author(s) in this journal, you can start by accessing the About page. You can first read the Policies section to find out the policies determined by the JCTA. Then, if you submit an article, you can see the guidelines in the Author Guidelines or Author Guidelines section. Each journal submission will be made online and requires prospective authors to register and have an account to be able to submit manuscripts.
Arjuna Subject : Ilmu Komputer (semua kategori) - Visi Komputer dan Pengenalan Pola
Articles 143 Documents
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AgriMisinfo-ID: A Multi-Platform Dataset and Ensemble Transformer-Based Detection System for Agricultural Misinformation in Indonesian Social Media Brian Rizqi Paradisiaca Darnoto; Nelly Oktavia Adiwijaya; Dony Bahtera Firmawan; Fadhel Akhmad Hizham; Nandini Putri Hanifa Jannah; Talitha Puspitasari; Fabyan Yastika Permana
Journal of Computing Theories and Applications Vol. 4 No. 1 (2026): JCTA 4(1) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.17025

Abstract

Agricultural misinformation on social media poses risks to Indonesian food security and farmer livelihoods. False claims about fertilizers, pesticides, crop varieties, and farming practices can spread rapidly across social media platforms such as Instagram, YouTube, and Facebook, as well as through publicly available datasets, potentially influencing agricultural decisions and outcomes. This paper introduces AgriMisinfo-ID, a bilingual, multi-platform dataset for agricultural misinformation detection, containing 5,288 labeled samples collected from social media and supplementary public datasets across Indonesian agricultural contexts. A hybrid detection system is proposed that combines an ensemble of fine-tuned Transformer models, IndoBERT and XLM-RoBERTa, with a Knowledge Verification module that cross-references agricultural claims against Wikidata and Wikipedia. Training uses Focal Loss to address class imbalance, together with GPT-4o-mini-based paraphrase augmentation for minority classes. Across three random seeds, the weighted ensemble achieves an F1-Macro of 0.5870 ± 0.0028 and an accuracy of 0.8027 ± 0.0039 on the test set, outperforming the individual models, a TF-IDF/SVM baseline, and an equal-weight ensemble in terms of F1-Macro. The Knowledge Verification module provides evidence-based verdicts that can support the inspection and auditability of model decisions. This work provides a reproducible benchmark for agricultural misinformation research in bilingual, low-resource settings.
Cog-CoT: A Cognitive Chain-of-Thought Framework for Bloom's Taxonomy-Aligned Educational Question Answering Alfarabi Muzli; Ratih Nur Esti Anggraini; Diana Purwitasari
Journal of Computing Theories and Applications Vol. 4 No. 1 (2026): JCTA 4(1) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.17084

Abstract

Large Language Models (LLMs) have shown strong potential in educational question answering, yet they often exhibit cognitive misalignment by generating responses that emphasize linguistic fluency rather than the cognitive depth required by different levels of Bloom's Taxonomy. This limitation arises from three structural gaps: Bloom classifiers are typically disconnected from the generation process, Chain-of-Thought (CoT) reasoning lacks explicit cognitive scaffolding, and Retrieval-Augmented Generation (RAG) verifies factual consistency only at the final output. To address these limitations, this paper proposes Cognitive Chain-of-Thought (Cog-CoT), a four-module framework that integrates Bloom's Taxonomy into the reasoning process of LLMs. The framework consists of (1) a LinearSVC-based Cognitive Classifier (weighted F1 = 0.9915) for Bloom-level prediction, (2) a Hierarchical Cognitive Decomposer that constructs a bottom-up sequence of Bloom-aligned sub-questions, (3) a Cog-CoT Reasoning module employing six level-specific prompt templates with step-wise cosine similarity verification against retrieved context (  = 0.65, MAX_RETRY = 3), and (4) a Bottom-Up Aggregator that synthesizes verified intermediate responses into a coherent final answer. Experiments using three open-weight LLMs (Gemma-3-4B-IT, LLaMA-3.1-8B-Instruct, and Qwen2.5-7B-Instruct) on an Indonesian Social Studies dataset and external English benchmarks (SQuAD 2.0 and ASQA) demonstrate that Cog-CoT consistently outperforms Zero-Shot prompting, achieving improvements of 4.16%–7.36% in GT Cosine Similarity while also producing superior performance across BLEU-4, ROUGE-L, METEOR, and BERTScore. These results demonstrate the effectiveness of integrating cognitive scaffolding with structured reasoning and retrieval verification for educational question answering.
Evaluating Disruption Recovery across Pareto-Representative Solutions in Multi-Objective University Course Timetabling Hanifah Salsabila Ryadi; Ristu Saptono; Muhammad Fahmy Nadhif
Journal of Computing Theories and Applications Vol. 4 No. 1 (2026): JCTA 4(1) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.17110

Abstract

Multi-objective university course timetabling produces a Pareto front of trade-off solutions, but ultimately, one timetable must be selected for deployment. Existing studies commonly base this choice on objective values, robustness criteria, or stakeholder preferences, while the downstream recovery implications of selecting different Pareto representatives remain insufficiently understood. This study proposes a deployment-oriented evaluation framework to examine whether the selected Pareto-representative timetable affects recovery after calendar-based disruptions. The framework keeps the original optimization objectives fixed, selects the best-f1, best-f2, and compromise timetables from each run, projects them onto an academic calendar with actual dates and holidays, and evaluates them under five disruption scenarios using greedy direct rescheduling followed by target-meeting fulfillment repair. The framework is evaluated using institutional scheduling and calendar data from the odd and even semesters of the 2025/2026 academic year, which provide a natural contrast in room-slot occupancy and disruption exposure. Weekly timetables are generated using NSGA-II by minimizing a weighted soft-constraint penalty (f1) and average room-capacity waste (f2), with the experiments repeated across five independent random seeds. No representative timetable produced a consistent recovery advantage under the tested setting: differences in the direct rescheduling success rate were small relative to seed-to-seed variation, even when the representative timetables differed substantially in their class placements. The larger contrast occurred between semesters. The denser odd semester, with approximately 91% room-slot occupancy, directly recovered about one-third of affected meetings, whereas the less dense even semester, with approximately 52% occupancy, directly recovered nearly all affected meetings. Both semesters achieved 100% target-meeting fulfillment after repair. Overall, the stronger observed contrast occurred between semester settings and was more consistent with differences in structural slack than with the selected representative category.

Page 15 of 15 | Total Record : 143

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