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All Journal Informatik : Jurnal Ilmu Komputer Jurnal Sistem Cerdas Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Teknologi Terapan JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Jurnal Elektra Journal of Applied Computer Science and Technology (JACOST) Jurnal Puan Indonesia Jurnal Isu Teknologi Power Elektronik : Jurnal Orang Elektro
Sarosa Castrena Abadi
Politeknik Manufaktur Bandung
Religion Agriculture, Biological Sciences & Forestry Arts Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences

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Developing Sorting Algorithm for SmartEdu Conveyor using Computer Vision Technology Ridwan; Erdani, Yuliadi; Sarosa Castrena Abadi; Anugrah, Mochammad Dimas; Abdur Rohman Harits Martawireja; Rizqi Aji Pratama
Informatik : Jurnal Ilmu Komputer Vol 21 No 3 (2025): Desember 2025
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v21i3.12434

Abstract

This study aims to develop a sorting algorithm for the SmartEdu Conveyor using computer vision technology to enhance accuracy and efficiency in automated sorting systems. The system integrates a Raspberry Pi 4 as the main processing unit and employs the YOLOv8 object detection algorithm to classify geometric objects moving on a conveyor belt. Images captured by an overhead camera are processed in real time, and the results are transmitted through the MQTT protocol using the Paho MQTT library. Node-RED functions as the Human-Machine Interface (HMI), while a Programmable Logic Controller (PLC) drives double-acting pneumatic cylinders to perform the sorting mechanism. Experimental tests conducted at three conveyor speeds demonstrate that the system achieves an average accuracy confidence of 89.38% at 1 cm/s, 78.57% at 1.7 cm/s, and 59.28% at 2.3 cm/s. Further performance evaluation using the Precision–Recall curve yields a mean Average Precision (mAP) of 0.993 at an Intersection over Union (IoU) threshold of 0.5, indicating highly accurate object detection capability. The proposed YOLOv8-based sorting system demonstrates reliable real-time operation, high precision, and robust communication between vision and control modules. It will be implemented as a SmartEdu teaching aid prototype to support automation learning and industrial training applications. This work contributes to educational automation by integrating an open-source vision algorithm with industrial control architecture.
A Machine Vision–Based Automated Wheel Leak Detection System Using Real-Time Object Detection in the Water Leak Testing Process Susetyo Bagas Bhaskoro; Sarosa Castrena Abadi; Aris Budiyarto; Inkreswari Retno Hardini; M. Pribadi Lukman
Jurnal Sistem Cerdas Vol. 9 No. 1 (2026)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v9i1.637

Abstract

Water leak testing in automotive wheel manufacturing has traditionally relied on manual visual inspection of bubble formation, introducing subjectivity and limiting repeatability in quality assurance processes. This study developed and experimentally validated a real-time leak detection system based on machine vision, directly integrated with an industrial water leak tester platform. A dataset comprising 686 annotated images was constructed from recorded operational testing sequences and partitioned into 80% training and 20% validation subsets. The network was trained for 150 epochs and deployed within an integrated framework incorporating temporal decision logic and automated event logging to ensure deterministic classification under continuous video streaming. Experimental validation was conducted across five scenarios (A–E), including high-leak, low-leak, no-leak, and in-situ operational testing conditions, totaling 100 trials. The aggregated confusion matrix yielded 60 true positives and 40 true negatives with zero false positives and false negatives, resulting in accuracy, sensitivity, specificity, precision, and F1-score values of 1.0 within the evaluated domain. Receiver operating characteristic and precision–recall analyses confirmed strong class separability and stable decision boundaries. Although the results demonstrated high discriminative performance under controlled and operational settings, further large-scale validation under heterogeneous industrial environments is required to fully assess long-term robustness. The proposed framework provided an automated, objective, and real-time inspection solution aligned with Industry 4.0 principles for intelligent manufacturing systems.
Co-Authors Abdur Rohman Harits Martawireja Adhitya Sumardi Sunarya Aliffa Shalsabilah Angga Putra Anton Prafanto Anugrah, Mochammad Dimas Aris Budiyarto Aris Suryadi Asmoro, Purwandito Tulus Cepi Ramdani Dani Usman Fitria Suryatini Givy Devira Ramady Givy Devira Ramady Griffani Megiyanto Rahmatullah Hadi Supriyanto Inkreswari Retno Hardini M. Pribadi Lukman Mindit Eriyadi Muhammad Ayat Hidayat Muhammad Nursyam Rizal Nazmi Febrian Ridwan Rizqi Aji Pratama Sandy Bhawana Mulia Sendi Wiryawan SITI AMINAH Siti Aminah Suharyadi Pancono Susetyo Bagas Bhaskoro Topan Pratomo Yuda Muhammad Hamdani Yuliadi Erdani, Yuliadi