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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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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.
Object Detection and Pose Estimation with RGB-D Camera for Supporting Robotic Bin-Picking JAMZURI, EKO RUDIAWAN; ANALIA, RISKA; SUSANTO, SUSANTO
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 11, No 1: Published January 2023
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v11i1.128

Abstract

ABSTRAKTujuan dari penelitian ini adalah untuk mendeteksi objek dan mengestimasi pose objek menggunakan kamera RGB-D. Dalam penelitian ini, kami mengusulkan pemrosesan data pada citra RGB dan citra depth saja, tanpa menggunakan point cloud, seperti pada umumnya. Metode yang diusulkan mendeteksi posisi dan orientasi objek menggunakan DRBox-v2 dari Region of Interest (ROI), yang sebelumnya diperoleh dari pendeteksian pada penanda ArUco. Hasil deteksi objek kemudian diskalakan dan digunakan pada citra depth untuk mendapatkan perkiraan posisi dan orientasi objek. Dari sisi pendeteksi objek, usulan metode memperoleh nilai Average Precision (AP) sebesar 0,740. Sedangkan untuk estimator pose, usulan metode menghasilkan kesalahan posisi rata-rata 13,36 mm dan kesalahan orientasi rata-rata 0,75 derajat. Metode yang diusulkan berpotensi menjadi alternatif sistem deteksi objek dan estimasi pose pada kamera RGB-D yang tidak memerlukan pemrosesan point cloud dan tidak memerlukan model referensi objek.Kata kunci: deteksi objek, estimasi pose, DRBox, ArUco, bin-picking ABSTRACTThis study aims to detect objects and estimate the object's pose using an RGB-D camera. In this study, we proposed data processing on RGB images and depth images only, without using point clouds, as in general. The proposed method detected the object's position and orientation using the DRBox-v2 from the Region of Interest (ROI), which was previously obtained from detecting ArUco markers. The object detection results were then scaled and used in the depth image to get the object's approximate position and orientation. In object detection, the proposed method obtained an Average Precision (AP) value of 0.740. As for the pose estimator, our method generated an average position error of 13.36 mm and an average orientation error of 0.75 degrees. Therefore, this method can be an alternative object detection and pose estimation system on an RGB-D camera that does not require point cloud processing and an object reference model.Keywords: object detection, pose estimation, DRBox, ArUco, bin-picking
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.
Hand Sign Recognition of Indonesian Sign Language System SIBI Using Inception V3 Image Embedding and Random Forest Sari, Mayang; Jamzuri, Eko Rudiawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6156

Abstract

This paper presents a sign language recognition system for the Indonesian Sign Language System SIBI using image embeddings combined with a Random Forest classifier. A dataset comprising 5280 images across 24 classes of SIBI alphabet symbols was utilized. Image features were extracted using the Inception V3 image embedding, and classification was performed using Random Forest algorithms. Model evaluation conducted through K-Fold cross-validation demonstrated that the proposed model achieved an accuracy of 59.00%, an F1-Score of 58.80%, a precision of 58.80%, and a recall of 59.00%. While the performance indicates room for improvement, this study lays the groundwork for enhancing sign language recognition systems to support the preservation and broader adoption of SIBI in Indonesia.
Sensor Fusion – Based Localization for ASV with Linear Regression Optimization Wijaya, Ryan Satria; Jamzuri, Eko Rudiawan; Wibisana, Anugerah; Sinaga, Jepelin Amstrong; Julanba, Vafin
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10048

Abstract

ASV (Autonomous Surface Vehicle) is one of popular innovations in the maritime field that is widely used for various missions on the water surface. The ASV itself has the ability to operate automatically without human intervention. Therefore, ASV requires an accurate and reliable localization system. This research focuses on developing an ASV localization system using waterflow sensors optimized through linear regression and integrated with orientation data from an IMU sensor through sensor fusion to obtain global coordinate position estimation. The experiments conducted showed a significant improvement in accuracy after optimization, with the Root Mean Square Error (RMSE) of the waterflow sensor data decreasing from 161.65 meters to 0.28 meters. Moreover, the yaw data reading by IMU achieved accuracy with RMSE 1.54 degrees. The localization system in the final test achieved RMSE values of 0.07 meters for the X-axis, 0.14 meters for the Y-axis, and 1.9 degrees for yaw during the ASV global positioning experiment. In addition, a GUI (Graphical User Interface) was developed for visualization with average communication latency of 113.6 milliseconds. This localization system is a promising solution in stable water condition.
Comparative Study of YOLOv5, YOLOv7 and YOLOv8 for Robust Outdoor Detection Wijaya, Ryan Satria; Santonius, Santonius; Wibisana, Anugerah; Jamzuri, Eko Rudiawan; Nugroho, Mochamad Ari Bagus
Journal of Applied Electrical Engineering Vol. 8 No. 1 (2024): JAEE, June 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaee.v8i1.7207

Abstract

Object detection is one of the most popular applications among young people, especially among millennials and generation Z. The use of object detection has become widespread in various aspects of daily life, such as face recognition, traffic management, and autonomous vehicles. The use of object detection has expanded in various aspects of daily life, such as face recognition, traffic management, and autonomous vehicles. To perform object detection, large and complex datasets are required. Therefore, this research addresses what object detection algorithms are suitable for object detection. In this research, i will compare the performance of several algorithms that are popular among young people, such as YOLOv5, YOLOv7, and YOLOv8 models. By conducting several Experiment Results such as Detection Results, Distance Traveled Experiment Results, Confusion Matrix, and Experiment Results on Validation Dataset, I aim to provide insight into the advantages and disadvantages of these algorithms. This comparison will help young researchers choose the most suitable algorithm for their object detection task.
Detection of Misoriented Polarized Electronic Components on PCBs Using HOG Features and Neural Networks Jamzuri, Eko Rudiawan; Ikhsan, Habyb Nur
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11330

Abstract

Mounting misorientation on polar electronic components in printed circuit boards (PCBs) can cause malfunctions in electronic devices. This study proposes an automatic detection system that utilizes the Histogram of Oriented Gradients (HOG) feature and employs classification using an artificial neural network. The research was conducted by collecting data from PCB images featuring polar components, such as diodes, electrolytic capacitors, and transistors. Once the components are identified, the HOG features are extracted to generate feature vectors used in artificial neural network training. The experiment results show that this system can detect component orientation errors with a high degree of accuracy, achieving accuracy values of 99.5% for transistor components, 97% for electrolyte capacitors, and 93.6% for diodes. Additionally, F1 values and high precision are achieved for all three types of components. The ReLU activation function has been shown to perform best among other activation functions. While the results are promising, further research is necessary to automate the identification of component locations without relying on manual cropping processes.
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