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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 ILKOM Jurnal Ilmiah 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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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.
Robotics training to improve STEM skills of Islamic boarding school students in Batam Eko Rudiawan Jamzuri; Hendawan Soebhakti; Senanjung Prayoga; Rifqi Amalya Fatekha; Anugerah Wibisana; Fitriyanti Nakul; H. Hasnira; Riska Analia; S. Susanto; Ryan Satria Wijaya; Ika Karlina Laila Nur Suciningtyas; Widya Rika Puspita; Eka Mutia Lubis; Adlian Jefiza; B. Budiana; Ahmad Riyad Firdaus
Journal of Community Service and Empowerment Vol. 5 No. 1 (2024): April
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/jcse.v5i1.26895

Abstract

One potential approach to addressing the challenges posed by the advent of Industry 4.0 and Society 5.0 is to offer robotics training. This endeavor aims to enhance students' foundational understanding of STEM (Science, Technology, Engineering, and Mathematics) disciplines. The study involved collaborating with the Pondok Pesantren Granada, an Islamic Boarding School located in Batam, to provide robotics training as community service activities. The study included 29 trainees: 15 from class XI and 7 from classes X and XII. The teaching was conducted using a combination of didactic instruction, interactive discourse, and hands-on exercises. Trainees are administered a written examination to assess their proficiency level before and after the training program. The training outcomes exhibited a significant improvement in the mean STEM proficiency of trainees, with an increase of 38.15%. Furthermore, a series of activities have been effectively implemented, resulting in trainee satisfaction ratings exceeding 50% concerning course materials, trainer, and teaching equipment. A mere 17% of the individuals undergoing training expressed dissatisfaction with the allocated time, particularly the hands-on component's duration.
SqueezeNet Image Embedding and Support Vector Machine for Recognizing Hand Gestures in Indonesian Sign Language System Islami, Megan Shahra; Jamzuri, Eko Rudiawan
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2476.98-106

Abstract

This research proposes a hand gesture recognition method for the Sistem Isyarat Bahasa Indonesia (SIBI) sign language, integrating SqueezeNet for image feature extraction and Support Vector Machine (SVM) for classification. The study focuses on 24 static gestures representing alphabetic letters, excluding J and Z due to their motion-based representation. The dataset consists of 5280 RGB images (227×227 pixels), with 220 samples per gesture, obtained from a public Kaggle source. SqueezeNet, a lightweight CNN architecture, is used to generate 1000-dimensional feature vectors, which are then classified using an SVM with an RBF kernel (C = 1.0) to effectively handle non-linear decision boundaries. A 10-fold cross-validation was applied without data augmentation to evaluate baseline performance. The proposed method achieved 99.51% classification accuracy, with an average precision of 94.04%, recall of 94.02%, and F1-score of 94.02%. Certain gestures, such as G, H, and Q, achieved near-perfect recognition, while others, like V, presented greater classification challenges with a recall of 80.5%. Compared to existing models such as MobileNet (98% accuracy) and VGG16 (86% accuracy) on the same dataset, the SqueezeNet–SVM combination provides competitive or superior accuracy with significantly reduced computational requirements. These results highlight the method’s potential for real-time integration into mobile or embedded sign language translation applications, bridging communication gaps between the deaf and hearing communities. Future work will focus on improving performance for difficult gestures, applying data augmentation to enhance generalization, and developing a prototype mobile application for real-world testing in relevant environments.
The Robotics Training and RoboRoarZ Competition as a STEM-Based Community Service Program for Enhancing STEM Competencies of Senior High School Students in Kepulauan Riau Emelia Rosari Siregar; Anugerah Wibisana; Ahmad Riyad Firdaus; Hendawan Soebhakti; Eko Rudiawan Jamzuri; Senanjung Prayoga; Rifky Afriza; Naurah Nazhifah; Ryan Satria Wijaya; Rifqi Amalya Fatekha
Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Vol. 8 No. 1 (2026): Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/abdimaspolibatam.v8i1.13481

Abstract

The low adoption of STEM (Science, Technology, Engineering, and Mathematics) education in senior high schools (SMA/SMK) across Kepulauan Riau Province, Indonesia, has been identified as a critical gap in preparing students for Industry 4.0 and Society 5.0. To address this challenge, Politeknik Negeri Batam conducted a community service program entitled Robotics Training and RoboRoarZ Competition for Enhancing STEM Competencies of Senior High School Students in Kepulauan Riau. The program involved 200 students from 22 schools organized into 25 competing teams, and was implemented over two days on 12–13 August 2025. Participants received hands-on training using the SMORPHI modular reconfigurable robot, covering hardware assembly, programming, and autonomous navigation. A robotics competition, RoboRoarZ Indonesia 2025, was subsequently held featuring two categories: Autonomous Challenge (line-follower) and Imaginary Challenge (simulation). Pre-test and post-test assessments revealed a significant improvement in participants’ robotics knowledge, with average scores increasing from 45.2 to 72.8 (a 61.1% gain), and the pass rate rising from 28% to 84%. Questionnaire data further confirmed high positive response rates across indicators of interest, motivation, and career aspiration in robotics, consistently exceeding 88%. The findings demonstrate that competition-based STEM learning combining workshop instruction with project-based and problem-solving activities is an effective and scalable model for improving STEM competencies among secondary school students in underserved regions.
Implementation of 5-DOF Robot Arm Control with Inverse Kinematics Through Interactive GUI Ryan Satria Wijaya; Muhamad Ilham; Eko Rudiawan Jamzuri; Anugerah Wibisana; Rifky Afriza; Emelia Rosari Siregar; Senanjung Prayoga
Journal of Applied Electrical Engineering Vol. 10 No. 1 (2026): JAEE, June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaee.v10i1.12658

Abstract

This work presents a control system for a five-joint (5-DOF) robotic arm designed for educational use, combining geometric inverse kinematics with a custom Python graphical interface. Instead of relying on iterative approaches, a closed-form inverse kinematics formulation is implemented to directly derive joint configurations from Cartesian coordinates. System functionality is supported through communication between a host computer and an ESP32 microcontroller via UART serial interface, enabling real-time control, workspace verification, and kinematic validation using forward kinematics. For simplicity in operation, the wrist orientation is kept fixed so that the control system focuses primarily on positional accuracy. experimental results show an average IK computation time of 1,7 ms, a mean positioning error of 2,46 mm, and a peak deviation of 4,91 mm across representative workspace targets. The obtained results confirm that the system achieves stable and reliable performance suitable for low-cost laboratory experimentation and educational robotics applications.
Co-Authors Adlian Jefiza Ahmad Mufid Panisti Ahmad Riyad Firdaus Ahmad Riyad Firdaus Alvin Anisatul Munawaroh ANUGERAH WIBISANA Anugerah Wibisana B. Budiana Budi Sugandi Daniel Sutopo Pamungkas, Daniel Sutopo Eka Mutia Lubis Emelia Rosari Siregar Fitriyanti Nakul Geminiesty Lathifasari Djavendra Hanjaya Mandala Hasnira Hasnira Hendawan Soebhakti Heru Wijanarko Ika Karlina Laila Nur Suciningtyas Ikhsan, Habyb Nur Islami, Megan Shahra Jacky Baltes Julanba, Vafin Kaputra, Aldi Lindawani Siregar Mayang Sari MOCHAMAD ARI BAGUS NUGROHO Muhamad Ilham Mulyadi, Indra Hardian Naufal Abdurrahman Prasetyo Naurah Nazhifah Ricardo Silitonga, Jony Arif Ridwan Ridwan Rifky Afriza Rifqi Amalya Fatekha Riska Analia Ryan Satria Wijaya Ryan Satria Wijaya Ryan Satria Wijaya S. Susanto Santonius, Santonius Senanjung Prayoga Senanjung Prayoga Sinaga, Jepelin Amstrong Siti Aisyah Sumantri Kurniawan Risandriya Susanto Susanto Susanto Susanto Utama, Eldio Very Very Vivin Oktowinandi Wahyu Wibowo Widya Rika Puspita