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All Journal Jurnal EECCIS JTRM (Jurnal Teknologi dan Rekayasa Manufaktur)
Castrena Abadi, Sarosa
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Rancang Bangun Teaching Aid Motor Servo AC R88M Berbasis PLC NX1P2 Sebagai Alat Bantu Pengajaran Castrena Abadi, Sarosa; Rokhim, Ismail; Wiyono, Andri; Rahmah, Mustika
JTRM (Jurnal Teknologi dan Rekayasa Manufaktur) Vol 5 No 2 (2023): Volume: 5 | Nomor: 2 | Oktober 2023
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat (P3M) Politeknik Manufaktur Bandung (Polman Bandung)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.48182/jtrm.v5i2.141

Abstract

Teaching aids are used in the field of education to streamline the implementation of the learning process. In this research, AC servo motor learning media with type R88M-1M10030-S2. The learning media will focus on controlling the position and speed of the servo motor for the x, y and z axes. This research uses the experimental work method. Testing of this research is carried out in Motion Variety parameter testing, NC function testing, HMI function testing, and user evaluation testing. It was found that the percentage of deviation value was 4.9%. In position testing, the deviation value of the X and Z axes has a small average final error with an X-axis error value of 0.21% and a Z-axis of 0.24%. It can be seen that the average Y-axis error is the largest with an average error value of 5.84%. Overall the average value of the HMI evaluation is 89% for the quality of the AC Servo Motor Teaching Aid HMI that has been made. This figure is classified as very good to continue using the tool as an auxiliary medium for the teaching process.
Computer Vision-Based Object Identification And Handling System: Case Study of KRSRI Robot (Indonesian Search And Rescue Robot Competition) Naufalfalah, Tamim; Castrena Abadi, Sarosa; Eko Setiawan, Aan
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 20 No. 2 (2026)
Publisher : Faculty of Engineering, Universitas Brawijaya

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Abstract

Search And Rescue (SAR) robots are designed to locate and rescue victims from disaster sites to safe zones. In the 2024 Indonesian Search And Rescue Robot Contest (KRSRI), to earn points during rescue missions, the robot must identify victims among dummies with the victim oriented at a 45° angle and accurately place them in the safe zone. This research utilizes input from an Arducam camera and leverages the YOLOv4-Tiny Computer Vision algorithm, which offers reliable and adaptive detection and recognition capabilities under varying victim rescue conditions. The system outputs control commands for the robot's movement and manipulator arm. The final project successfully implemented the YOLOv4-Tiny model on a SAR robot, achieve real-time object detection at a minimum light intensity of 6 lux and a maximum distance of 60 cm. The system demonstrated a mAP of 99% and an IoU rate of 91.58%, with an average processing speed of 14.52 FPS. The success rate was 87.50% with an average time of 18.99 seconds for rescuing victims without dummies, and 70.83% with an average time of 46.78 seconds for rescuing victims among dummies. For victim placement, success rates and average times were as follows: 86.67% and 15.29 seconds for the gray safe zone, 93.33% and 15.23 seconds for the yellow safe zone, and 100% with 14.29 seconds for the marker safe zone. Given the high accuracy and speed, this algorithm is effective for scoring points in the Indonesian Search And Rescue Robot competition.
Co-Authors Eko Setiawan, Aan Ismail Rokhim, Ismail Naufalfalah, Tamim Rahmah, Mustika WIYONO, ANDRI