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All Journal ComEngApp : Computer Engineering and Applications Journal Teknika Jurnal Teliska Jurnal Ampere Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Otomasi Kontrol dan Instrumentasi Jurnal Teknologi Informasi dan Pendidikan TECHNOLOGIC
Muslimin, Selamat
Politeknik Negeri Sriwijaya
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Journal : Indonesian Journal of Artificial Intelligence and Data Mining

 1 
Development of a Hand Gesture Detection-Based Robot System with MediaPipe Muslimin, Selamat; Prihatini, Ekawati; Martin, Tri
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.37678

Abstract

This research presents the development of an intelligent robot that can be summoned simply by waving a hand, without the need for physical buttons or voice commands. The system utilizes MediaPipe technology to detect and recognize hand gestures in real time through a camera. When a user waves their hand toward the camera, the system processes the motion and identifies it as a signal to call the robot. Image processing is handled by a Raspberry Pi, while movement control is managed by an Arduino, which regulates the direction and speed of the motors. The robot automatically moves toward the user and stops at a certain point to wait for further confirmation. Test results show that the robot can accurately detect gestures under various lighting conditions and distances. This approach enables more natural and efficient human–robot interaction, making it well-suited for modern contactless service systems
A Robot Model for Detecting Smoking Violations Using YOLOv5 and PID-Based Navigation Control Muslimin, Selamat; Megaarta, Muhammad Andaru; Triandika, Rayhan
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37345

Abstract

Smoking violations in restricted areas, especially in public spaces exposed to secondhand smoke, remain a significant concern. This study develops an autonomous robot designed to detect smoking violations using YOLOv5 and Raspberry Pi. The robot's camera captures real-time images to identify smoking behavior, with YOLOv5 accurately detecting cigarette objects. For navigation, the robot employs a PID control system, complemented by an encoder and a compass sensor, ensuring precise movement. The results demonstrate that the robot achieves a confidence level of 87% in detecting smoking behavior at a distance of 250 cm, with a frame rate of 8 FPS. The PID-based navigation system ensures minimal error of ±5 cm over a 2-meter distance. These findings emphasize the robot's effectiveness in both detecting smoking violations and navigating accurately, making it an effective tool for the enforcement of smoke-free zone regulations.
Real-Time Detection of Autistic Children's Activities Using YOLOv8 on Social Monitoring Robots Prihatini, Ekawati; Muslimin, Selamat; Hadi, Kurnia
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.37380

Abstract

Children with autism spectrum disorder require special attention in both therapy and daily activity monitoring. One approach that can assist is the utilization of a Social Monitoring Robot (SMR) with the capability of automatic activity monitoring. This study aims to develop a real-time activity detection system for children with autism using the You Only Look Once version 8 (YOLOv8) algorithm on the SAR platform. The system is designed to recognize key activities such as eating, studying, and walking, through video input from a webcam processed by a Raspberry Pi. The recognition process is carried out by detecting bounding boxes and confidence scores for the child and their activities. The detection results are then visualized through a Human Machine Interface (HMI). Based on the testing, the system is capable of detecting and classifying children's activities with a fairly high level of reliability under real-world environmental conditions. These results indicate that the implementation of YOLOv8 in an SMR-based monitoring system has the potential to enhance supervision and intervention for children with autism in a more responsive and personalized manner.
Co-Authors Amperawan Amperawan, Amperawan Andika Pratama, Destra Bibrosi, Nur Alif Zaki Caesandra, Wahyu Dewi Permata Sari Dira, Arbi Pratam Faisal Damsi, Faisal Hadi, Kurnia Iskandar Iskandar Marniati, Yessi Martin, Tri Megaarta, Muhammad Andaru Nyayu Latifah Husni, Nyayu Latifah Permata Sari, Dewi Pola Risma Prihatini, Ekawati Ramadhan, M. Daffa Rasyad, Sabilal Renny Maulidda, Renny Savitri, Nurul Thoriq, Noval Al Triandika, Rayhan Yaksyah, Rafi Yudi Wijanarko, Yudi