https://publikasi.dinus.ac.id/jcta <div style="border: 3px #086338 Dashed; padding: 10px; background-color: #ffffff; text-align: left;"> <ol> <li><strong>Journal Title </strong>: Journal of Computing Theories and Applications</li> <li><strong>Online ISSN </strong>: <a href="https://portal.issn.org/resource/ISSN/3024-9104">3024-9104</a> </li> <li><strong>Frequency </strong>: Quarterly (February, May, August, and November) </li> <li><strong>DOI Prefix</strong>: 10.62411/jcta</li> <li><strong>Publisher </strong>: Universitas Dian Nuswantoro</li> </ol> </div> <div id="focusAndScope"> <p><strong data-start="133" data-end="190">Journal of Computing Theories and Applications (JCTA)</strong> is a peer-reviewed international journal that covers all aspects of foundations, theories, and applications in computer science. All accepted articles are published online, assigned a <strong data-start="527" data-end="547">DOI via Crossref</strong>, and made <strong data-start="558" data-end="593" data-is-only-node="">freely accessible (Open Access)</strong>. The journal follows a rapid editorial and peer-review process. Authors typically receive the first decision within one week, while the overall first-round peer-review process is generally completed within four weeks.</p> <p>Artificial Intelligence<br />Big Data<br />Bioinformatics<br />Biometrics<br />Cloud Computing<br />Computer Graphics<br />Computer Vision<br />Cryptography<br />Data Mining<br />Fuzzy Systems<br />Game Technology<br />Image Processing<br />Information Security<br />Internet of Things<br />Intelligent Systems<br />Machine Learning<br />Mobile Computing<br />Multimedia Technology<br />Natural Language Processing<br />Network Security<br />Pattern Recognition<br />Quantum Informatics<br />Signal Processing<br />Soft Computing<br />Speech Processing</p> <p><br />Special emphasis is given to recent trends related to cutting-edge research within the domain.</p> </div> Universitas Dian Nuswantoro en-US Journal of Computing Theories and Applications 3024-9104 https://publikasi.dinus.ac.id/jcta/article/view/16046 <p>Abstract: Detecting chili leaf diseases remains challenging due to the non-uniform manifestation of symptoms, local discoloration, small lesion regions, and visual similarity between disease patterns and natural leaf background variations. Although YOLO-based detectors provide favorable computational efficiency, lightweight variants often struggle to distinguish subtle lesion characteristics, while conventional attention mechanisms such as CBAM primarily rely on global feature aggregation and may overlook regional activation variability. To address these limitations, this study proposes a YOLOv9s-based detection framework integrated with a Region-Dispersion Channel Spatial Attention (RDCSA) module. The proposed module incorporates regional dispersion statistics, namely mean, standard deviation, and range, as channel descriptors to capture inter-region feature variability before applying spatial attention refinement. Experiments were conducted on the COLD dataset containing 532 original images from five chili leaf condition categories using a split-before-augmentation protocol to ensure objective evaluation. RDCSA was integrated at the P5 feature level and evaluated through attention placement analysis, component-wise ablation, sensitivity analysis, stability assessment, and comparison with modern attention mechanisms. The proposed YOLOv9s + RDCSA model achieved an mAP@50 of 0.894, mAP@50–95 of 0.773, precision of 0.858, recall of 0.861, and an F1-score of 0.859 with only a marginal increase in model parameters. The results suggest that regional dispersion-based attention improves feature discrimination while preserving computational efficiency, particularly for disease symptoms characterized by heterogeneous spatial patterns. Nevertheless, performance remains influenced by visually ambiguous symptom categories, indicating that further validation across multiple datasets and field conditions is required. Overall, the proposed RDCSA module enhances detection capability without substantially increasing computational overhead, making it a promising attention mechanism for lightweight plant disease detection systems.</p> Miwan Kurniawan Hidayat Jufriadif Na'am Ferda Ernawan Copyright (c) 2026 Miwan Kurniawan Hidayat, Jufriadif Na'am, Ferda Ernawan https://creativecommons.org/licenses/by/4.0 2026-06-06 2026-06-06 4 1 1 20 10.62411/jcta.16046