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Journal of Computer Science Advancements
Published by Yayasan Adra Karima Hubbi
ISSN : 30263379     EISSN : 3024899X     DOI : https://doi.org/10.70177/jsca
Core Subject : Science,
Computer Science & IT
Journal of Computer Science Advancements 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 Journal of Computer Science Advancements follows the open access policy that allows the published articles freely available online without any subscription.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 3 Documents
 1 
Search results for , issue "Vol. 3 No. 3 (2025)" : 3 Documents clear
DESIGN OF A WEB-BASED GOODS DELIVERY INFORMATION SYSTEM WITH API SERVICES AND IOT INTEGRATION AT PT. ESA MANDIRI RUBBER Putri, Nadia Natasya; Terisia, Vany; Syamsu, Muhajir
Journal of Computer Science Advancements Vol. 3 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i3.2415

Abstract

The advancement of information technology has driven various industrial sectors, including manufacturing, to transform toward more efficient and responsive distribution systems. PT. Esa Mandiri Rubber, a rubber manufacturing company, still relies on manual processes in managing goods delivery, resulting in various issues such as delays, distribution errors, and a lack of transparency in tracking. This study aims to design a web-based goods delivery information system integrated with Internet of Things (IoT) technology and API services. The system development method used is the Waterfall method, which consists of five stages: requirement analysis, system design, implementation, testing, and maintenance. The developed system includes delivery recording, real-time tracking, IoT device data integration, and access to information through a web interface. The results of the study show that the designed system successfully replaces the previously used manual processes, enhances distribution effectiveness, and facilitates easier monitoring and reporting. Thus, this system is capable of improving operational efficiency and the quality of logistics services at PT. Esa Mandiri Rubber.
INTEGRATING COMPUTER VISION AND MECHATRONICS FOR AUTOMATED QUALITY CONTROL IN SMART PRODUCT MANUFACTURING Faizin, Kholis Nur; Al-Fahim, Ahmed; Lahti, Maria
Journal of Computer Science Advancements Vol. 3 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i3.2638

Abstract

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.
NATURAL LANGUAGE PROCESSING FOR AUTOMATED REQUIREMENT ENGINEERING IN AGILE SOFTWARE DEVELOPMENT Sungkar, Muchamad Sobri; Baibek, Serikbek; Hamdan, Salma
Journal of Computer Science Advancements Vol. 3 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i3.2646

Abstract

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.

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