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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Informatics (IJEEI) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JURNAL NASIONAL TEKNIK ELEKTRO Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL INTEGRASI JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Applied Electrical Engineering Journal of Community Service and Empowerment Journal of Applied Computer Science and Technology (JACOST) Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Engagement: Jurnal Pengabdian Kepada Masyarakat
Eko Rudiawan Jamzuri
Department Of Electrical Engineering, Politeknik Negeri Batam, Indonesia
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Mechanical Engineering Social Sciences Other

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Journal : Journal of Applied Computer Science and Technology (JACOST)

 1 
Desktop Application for Traceability System on The Printed Circuit Board (PCB) Storage Process Alvin; Rudiawan Jamzuri, Eko
Journal of Applied Computer Science and Technology Vol 5 No 1 (2024): Juni 2024
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v5i1.670

Abstract

This paper discusses the development of desktop applications for traceability systems. The application was developed to facilitate data recording and tracking in an electronics manufacturing company's storage process of Printed Circuit Board (PCB) products. The application is developed using the Visual Basic language and Microsoft Excel databases. Additionally, the application is integrated with a barcode scanner to simplify the data entry process from PCBs and employee ID cards. Through the trial process conducted on the developed application, it has generally functioned in accordance with the development goals. Program control validation has been tested through several application access attempts from users registered as operators and administrators. The application has successfully recorded data from inbound and outbound processes, demonstrating storage and tracking functionality. Furthermore, the application has displayed the actual status data of the PCBs present in the warehouse. In terms of user satisfaction, seven users stated that this application was effective and efficient compared to the manual data recording process previously used by the company. This result was obtained from a questionnaire after the application was implemented in the company warehouse.
Performance Comparison of Support Vector Machine (SVM) and k-Nearest Neighbors (kNN) in Verifying Material Orientation Utama, Eldio; Rudiawan Jamzuri, Eko
Journal of Applied Computer Science and Technology Vol 6 No 1 (2025): Juni 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v6i1.1037

Abstract

In automated manufacturing, verifying material orientation is essential to ensure the product assembly proceeds without errors. For instance, in the beverage industry, incorrect orientation of materials, such as bottle caps, can lead to failures in the packaging process, resulting in improperly sealed bottles that may compromise product quality and safety. This study compares the performance of Support Vector Machine (SVM) and k-Nearest Neighbors algorithms for verifying material orientation verification through automated optical inspection. The images were processed using the Inception V3 Convolutional Neural Network (CNN) to extract relevant image features, which were then classified using SVM and kNN algorithms. As a result, SVM achieved high classification performance during testing, with classification accuracy, precision, recall, and F1 score of 1.0 compared to kNN, which achieved only 0.967. However, kNN demonstrated superior computational efficiency, with a training time of 1.126 seconds and a validation time of 0.713 seconds, compared to SVM's training time of 3.101 seconds and validation time of 1.479 seconds. These results indicate that while both methods are highly effective for material orientation verification, kNN offers significant advantages in terms of computational speed, making it more suitable for real-time applications. The implications of this study highlight the potential for integrating the proposed method in industrial applications, promoting enhanced efficiency and reducing error rates in automated assembly lines.
Co-Authors Adlian Jefiza Ahmad Riyad Firdaus Ahmad Riyad Firdaus Alvin Analia, Riska Anisatul Munawaroh ANUGERAH WIBISANA Budi Sugandi Budiana, B. Daniel Sutopo Pamungkas, Daniel Sutopo Eka Mutia Lubis Eka Mutia Lubis Fatekha, Rifqi Amalya Fitriyanti Nakul Fitriyanti Nakul Geminiesty Lathifasari Djavendra Hanjaya Mandala Hasnira, H. Hendawan Soebhakti Hendawan Soebhakti Heru Wijanarko Ika Karlina Laila Nur Suciningtyas Ika Karlina Laila Nur Suciningtyas Ikhsan, Habyb Nur Jacky Baltes Jefiza, Adlian Julanba, Vafin Kaputra, Aldi Lindawani Siregar Lubis, Eka Mutia Mayang Sari MOCHAMAD ARI BAGUS NUGROHO Mulyadi, Indra Hardian Nakul, Fitriyanti Prasetyo, Naufal Abdurrahman Prayoga , Senanjung Prayoga, Senanjung Puspita, Widya Rika Ricardo Silitonga, Jony Arif Ridwan Ridwan Rifqi Amalya Fatekha Riska Analia Ryan Satria Wijaya S. Susanto, S. Santonius, Santonius Sinaga, Jepelin Amstrong Siti Aisyah Soebhakti, Hendawan Sumantri Kurniawan Risandriya Susanto Susanto Susanto Susanto Utama, Eldio Vivin Oktowinandi Wahyu Wibowo