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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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The Application of the Adaptive Neuro Fuzzy Inference System (ANFIS) Method in Estimating State of Charge (SOC) and State of Health (SOH) of Lithium-Ion Batteries Muslimin, Selamat; Prihatini, Ekawati; Husni, Nyayu Latifah; Caesandra, Wahyu
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2357

Abstract

The increasing reliance on lithium-ion batteries (LIBs) for electric vehicles and portable electronics demands accurate monitoring of battery performance, particularly the State of Charge (SOC) and State of Health (SOH). Conventional estimation methods—such as Coulomb counting, Kalman filtering, and equivalent circuit modeling—face challenges under dynamic conditions due to drift and limited adaptability. Recent studies have explored machine learning and neuro-fuzzy approaches to enhance prediction accuracy, yet many lack integration of real-time hybrid learning or struggle with high estimation error in noisy data environments. This research aims to apply the Adaptive Neuro-Fuzzy Inference System (ANFIS) to estimate SOC and SOH using experimental data from a 48V lithium-ion battery. The novelty lies in combining voltage, current, and capacity data within a MATLAB-based ANFIS framework that employs a hybrid learning algorithm integrating backpropagation and Recursive Least Squares Estimation (RLSE). Training data for SOC estimation used charging voltage and current, while SOH estimation incorporated discharging data and capacity. Results show that ANFIS achieved high accuracy with RMSE of 0.1466 and MAE of 0.021 for SOC, and RMSE of 0.012 and MAE of 0.0017 for SOH. The estimated SOH was 33.61%, closely aligned with actual values. These findings confirm ANFIS as a robust and adaptive method for real-time battery diagnostics. Future work will explore multi-input hybrid models, the integration of IoT-based BMS telemetry, and testing across diverse battery chemistries to generalize the model's performance and extend its application in smart energy systems.
Sistem Navigasi Gerak Robot Lawn Mower Menggunakan Pengendali Fuzzy Logic Muslimin, Selamat; Maulidda, Renny; Wijanarko, Yudi; Permata Sari, Dewi
Jurnal Otomasi Kontrol dan Instrumentasi Vol 11 No 2 (2019): Jurnal Otomasi Kontrol dan Instrumentasi
Publisher : Pusat Teknologi Instrumentasi dan Otomasi (PTIO) - Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/joki.2019.11.2.6

Abstract

Teknologi robot yang semakin berkembang pesat telah banyak membantu manusia dalam mempermudah menyelesaikan salah satu kegiatan yaitu memotong rumput. Robot lawn mower dirancang untuk memotong rumput, menghindari rintangan dan bergerak di sepanjang lintasan yang direncanakan. Sehingga kemampuan untuk mengenali lingkungan, perencanaan lintasan dan pengambilan keputusan harus dimiliki. Robot lawn mower adalah jenis robot yang mampu melakukan pergerakan secara otomatis. Sistem navigasi dan penerapan kecerdasan artifisial merupakan hal utama agar robot dapat bergerak secara mandiri. Dalam hal ini, pengendali fuzzy logic diterapkan untuk menemukan titik koordinat yang telah ditanamkan dalam algoritma fuzzy logic yaitu maju, belok kanan, belok kiri dan putar balik. Sensor GPS Neo-6M digunakan untuk membaca titik koordinat dan sensor kompas HMC5883L digunakan untuk membaca arah dalam sistem navigasi robot yang kemudian diproses oleh pengendali dan menghasilkan keluaran berupa putaran roda yang digerakkan oleh motor
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