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All Journal Electrician : Jurnal Rekayasa dan Teknologi Elektro Hanif Journal of Information Systems IJISCS (International Journal of Information System and Computer Science)
Adi Ahmad Fauzi
Universitas Muhammadiyah Lampung
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Library & Information Science

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Reduksi Noise Sensor Electrooculography Menggunakan Filter Kalman pada Kendali Mobile Robot Adi Ahmad Fauzi; Sri Suryani; Hendra Widodo
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 20 No. 1 (2026)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v20n1.3031

Abstract

Penggunaan Electrooculography (EOG) sebagai antarmuka kendali mobile robot menghadapi permasalahan utama berupa noise yang menurunkan kualitas sinyal dan akurasi sistem kendali. Penelitian ini mengkaji penerapan Filter Kalman untuk mereduksi noise pada data sensor EOG dalam sistem kendali mobile robot berbasis gerakan mata manusia. Metode penelitian meliputi perancangan modul sensor EOG, implementasi algoritma Filter Kalman, serta evaluasi kinerja sistem menggunakan Mean Squared Error (MSE) dengan variasi parameter noise pengukuran (R) dan noise proses (Q). Hasil pengujian menunjukkan bahwa konfigurasi parameter R = 10 dan Q = 1 menghasilkan keseimbangan optimal antara peredaman noise dan pelestarian karakteristik sinyal asli. Integrasi Filter Kalman terbukti meningkatkan stabilitas sinyal, akurasi, serta responsivitas sistem kendali mobile robot terhadap perintah gerakan mata. Penelitian ini memberikan kontribusi pada pengembangan sistem kendali robot berbasis EOG yang lebih andal dan efisien untuk interaksi manusia–robot.
DEVELOPMENT OF AN EYE-CONTROLLED MOBILE ROBOT SYSTEM USING EOG SIGNALS Joko Triloka; Adi Ahmad Fauzi
IJISCS (International Journal of Information System and Computer Science) Vol 9, No 3 (2025): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v9i3.1859

Abstract

The development of an eye-controlled mobile robot system using Electrooculography (EOG) signals is presented in this study. The proposed system enables robot motion control through eye movement detection, providing an alternative interaction method for individuals with limited physical mobility. The EOG sensor captures eye movement potentials, which are processed by a microcontroller to generate motion commands. A threshold-based detection algorithm was implemented to classify eye movements into four directional commands: left, right, forward, and backward. The system was tested to evaluate movement accuracy and response time. Experimental results show that the proposed system achieved an average directional detection accuracy of 88.3% and an average response time of 218 milliseconds, indicating reliable and real-time performance. The findings demonstrate that EOG-based control provides a feasible and responsive approach for human–robot interaction. Future improvements may involve noise filtering techniques and machine learning models to enhance signal stability and classification precision.
Computer Vision-Based Lettuce and Plantweed Segmentation Using YOLO and Segment Anything for Precision Agriculture Akhmad Jayadi; Adi Ahmad Fauzi; Muhammad Ikhsan; Jaka Persada Sembiring; Ahmad Rofi'i
Hanif Journal of Information Systems Vol. 4 No. 1 (2026): August Edition
Publisher : Ilmu Bersama Center

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Abstract

The presence of weeds in lettuce cultivation areas can reduce plant productivity because they compete for nutrients, water, and light. Manual weed identification requires considerable time and effort, so an automated system based on computer vision is needed . This study proposes the integration of the YOLO11s model and the Segment Anything Model (SAM) to detect and segment lettuce plants and weeds in agricultural environments. The dataset used consists of 741 training images , 212 validation images , and 106 testing images with two object classes, namely Lettuce and Plantweed . The YOLO11s model was trained for 100 epochs using an image size of 640 × 640 pixels and a batch size of 106 . 16. The training results show that the model obtained an mAP@50 value of 87.8%. And mAP@50–95 was 80.0% , indicating good object detection capability. Furthermore, the bounding box coordinates of the detection results were used as prompts in the Segment Anything Model to generate mask -shaped segmentations that follow the object contours more precisely. The experimental results show that the integration of YOLO11s and SAM is able to produce more detailed object representations compared to detection using bounding boxes alone. This approach has the potential to support various precision agriculture applications, such as plant morphology analysis, leaf area estimation, and the development of automated weed control systems .
Co-Authors Ahmad Rofi'i Akhmad Jayadi Hendra Widodo Jaka Persada Sembiring Joko Triloka Muhammad Ikhsan Sri Suryani