https://research.adra.ac.id/index.php/jcsa <p style="text-align: justify;"><strong>Journal of Computer Science Advancements</strong> is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of science, engineering and information technology. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the <strong>Journal of Computer Science Advancements</strong> follows the open access policy that allows the published articles freely available online without any subscription.</p> Yayasan Adra Karima Hubbi en-US Journal of Computer Science Advancements 3026-3379 https://research.adra.ac.id/index.php/jcsa/article/view/2638 <p>Smart manufacturing’s (Industry 4.0) complexity demands automated quality control (AQC), as manual inspection is a major bottleneck. A critical gap exists in integrating “passive” Computer Vision (CV) detection with “active” mechatronic intervention, creating a “siloed” research problem. This research aims to design, develop, and validate a closed-loop AQC framework, integrating deep learning CV and mechatronics to autonomously perform the full QC cycle from detection to real-time physical intervention. An experimental systems integration design was employed. A Convolutional Neural Network (CNN) was trained on a 17,000-image dataset. A Robotic Operating System (ROS) framework was utilized as the integration layer for “hand-eye” calibration, synchronizing the CV node with a 6-axis robotic arm on a test rig. The CV model achieved 99.7% mAP (42ms latency) and calibration yielded ±0.35mm precision. The fully integrated system validation achieved a 99.15% Defect Detection Rate (DDR), a 0.11% False Positive Rate (FPR), and a 97.4% Successful Rejection Rate (SRR). The research empirically validates a holistic, closed-loop AQC framework, successfully solving the “siloed” gap. The system provides a proven, scalable blueprint for moving beyond passive detection to fully autonomous quality control in smart manufacturing.</p> Kholis Nur Faizin Ahmed Al-Fahim Maria Lahti Copyright (c) 2025 Kholis Nur Faizin, Ahmed Al-Fahim, Maria Lahti https://creativecommons.org/licenses/by-sa/4.0 2025-06-16 2025-06-16 3 3 112 126 10.70177/jsca.v3i3.2638 https://research.adra.ac.id/index.php/jcsa/article/view/2646 <p>Manual Requirement Engineering (RE) in Agile software development creates a significant bottleneck. The reliance on natural language user stories at scale results in high-volume backlogs prone to ambiguity, duplication, and incompleteness, leading to costly, downstream development defects. This research aims to design, develop, and empirically validate a novel, hybrid Natural Language Processing (NLP) framework, termed the Agile Requirement Quality (ARQ) framework, to automate the detection of these common requirement defects. The goal is to reduce cognitive load and improve defect detection velocity during backlog refinement. A mixed-methods Design Science Research (DSR) methodology was employed. We developed the ARQ artifact (a hybrid BERT and heuristic model) and validated it both in-vitro against a 5,000-story “gold standard” annotated corpus (Fleiss’ Kappa 0.86) and in-situ through a quasi-experiment with professional Agile teams. The findings demonstrate high efficacy. In-vitro validation achieved high accuracy (overall 95.2%, with F1-scores of 0.87 for ambiguity and 0.94 for duplication). The in-situ experiment was conclusive: the ARQ-assisted team achieved a 73% increase in defect detection and an 87.5% reduction in “defect leakage” compared to the control team, registering high usability (88.5 SUS). This study provides robust empirical evidence that NLP-driven automation is a viable, high-impact strategy for mitigating risk in Agile RE. The framework functions as a practical “augmented intelligence” tool, significantly reducing defect leakage and improving quality assurance velocity.</p> Muchamad Sobri Sungkar Serikbek Baibek Salma Hamdan Copyright (c) 2025 Muchamad Sobri Sungkar, Serikbek Baibek, Salma Hamdan https://creativecommons.org/licenses/by-sa/4.0 2025-06-18 2025-06-18 3 3 127 140 10.70177/jsca.v3i3.2646