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All Journal Jurnal Ilmiah Mikrotek Jurnal Simantec Jurnal Otomasi Kontrol dan Instrumentasi Journal of Robotics and Control (JRC) JAREE (Journal on Advanced Research in Electrical Engineering) Journal of Technology and Informatics (JoTI)
Muhammad Fuad
Fakultas Teknik, Universitas Trunojoyo
Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Other

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ESTIMASI JARAK MENGGUNAKAN SENSOR KINECT Muhammad Fuad
Jurnal Ilmiah Mikrotek Vol 1, No 1 (2013): AGUSTUS
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pengukuran jarak menggunakan sensor berupa kamera digital sangat bergantung pada proses kalibrasiyang didukung algoritma pengolahan citra yang rumit. Pendekatan ini menghasilkan citra dengan framerate yang rendah. Hal ini mengakibatkan hasil dari metoda ini cenderung kurang baik jika diterapkanpada sistem realtime. Akurasi pengukuran jarak dengan kamera dapat diperbaiki dengan bantuanbeberapa sensor tambahan. Publikasi ini memaparkan suatu sistem yang mampu untuk melakukanestimasi jarak setiap piksel menggunakan sebuah sensor Kinect berbasis masukan video Depth. Sistem inidapat diterapkan dalam robot untuk mengukur jarak terhadap penghalang guna mendukung kemampuannavigasi. Tiga pendekatan dalam estimasi jarak tiap piksel dengan menggunakan sensor Kinectdibandingkan dalam tulisan ini. Performa akuisisi data jarak berdasarkan masukan video depthmenggunakan pustaka Microsoft Kinect SDK dibandingkan dengan hasil yang dicapai OpenNI. Sebuahmetoda baru untuk estimasi jarak berbasis nilai hue dari video depth yang dibaca dengan pustakaLibfreeNect diusulkan dalam penelitian ini. Analisis terhadap hasil pengujian pada pengukuran terhadapbeberapa titik penting dalam bingkai citra didiskusikan. 
PERANCANGAN SISTEM KONTROL GERAKAN PADA ROBOT LINE TRACER Ahmad Sahru Romadhon; Muhammmad Fuad
Jurnal Ilmiah Mikrotek Vol 1, No 1 (2013): AGUSTUS
Publisher : Universitas Trunojoyo Madura

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Abstract

Perancangan gerakan pada robot line tracer merupakan suatu sistem yang dapat mengontrolgerakan robot sehingga robot dapat bergerak dengan baik. Untuk itu dibutuhkan kontrol PIDyang dapat mengontrol robot line tracer dalam melakukan gerakan pada bidang datar berwarnaputih dengan garis berwarna hitam dalam lingkungan statis, sehingga robot dapat mengikutigaris hitam tersebut dengan baik. Dari penelitian ini, diperoleh hasil pengujian dalam ruangandengan cahaya yang redup robot dapat berjalan dengan baik pada lintasan yang lurus dan gagalpada lintasan lengkung, sedangkan pengujian dalam ruangan dengan cahaya yang terang robotdapat berjalan dengan baik pada lintasan yang lurus maupun pada lintasan lengkung.
PENERAPAN METODE FUZZY ITEM RESPONSE THEORY PADA e-LEARNING COMPUTERIZED ADAPTIVE TEST Diah Kusumawati; Andharini Dwi Cahyani; Muhammad Fuad
Jurnal Simantec Vol 4, No 2 (2014)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/simantec.v4i2.1389

Abstract

ABSTRAKSaat ini e-Learning sudah banyak diterima oleh masyarakat dunia, terbukti dengan maraknya implementasi e-Learning di lembaga pendidikan maupun industri. E-Learning merupakan suatu jenis sistem pembelajaran yang memungkinkan sampainya bahan ajar ke siswa dengan menggunakan media internet, atau media jaringan komputer lain. Tes adaptif merupakan sistem ujian otomatis yang dilakukan secara adaptif, menyesuaikan tingkat kesulitan soal dengan kemampuan masing-masing peserta ujian. Soal yang diberikan tergantung dari jawaban soal sebelumnya: benar atau salah. Dan hasilnya adalah level atau tingkat kemampuan peserta ujian. Salah satu metode yang digunakan dalam tes adaptif yaitu model Fuzzy Item Response Theory (FIRT) yang diimplementasikan dalam pembuatan aplikasi tes adaptif untuk lingkungan pendidikan. Berdasarkan hasil eksperimen, dapat disimpulkan bahwa Adaptive Test yang dikembangkan dengan menerapkan Fuzzy Item Response Theory mampu menempatkan siswa pada tingkat kemampuan yang sesuai dengan kemampuannya, nilai siswa lebih meningkat dengan menggunakan Adaptive Test dibandingkan dengan Konvensional Test hal tersebut dibuktikan dengan uji coba yang telah dilakukan dengan melihat hasil dari rata-rata nilai tiap siswa. Nilai siswa yang menggunakan Adaptive Test hasilnya lebih meningkat dibandingkan dengan nilai siswa yang menggunakan Konvensional Test. Dari rata-rata nilai siswa yang menggunakan Adaptive Test yakni 80, sedangkan rata-rata nilai siswa yang menggunakan Konvensional Test yakni 60.882353.Kata Kunci : E-Learning, Fuzzy Item Response Theory (FIRT), Adaptive Test, Konvensional Test.ABSTRACTNowadays e-learning has been widely accepted by the world society, it proved by the widespread implementation of e-Learning in the education institution and industry. E-Learning is one of the educational system that enables the students to get the teaching materials by use internet, computer networks, or the medias. Adaptive test is an automatic test system that adaptively did by adapting the difficulty level of the questions with each test participator’s abilities. The given questions are dependent by previuous answer : true or false. And the result is the level of the test participator ability. One of the used method in adaptive test is fuzzy item response theory (FIRT) model that implemented in adaptive test application production for educational environment. Based on the experimental result, can be concluded that, by appliying fuzzy item response theory , it can put the student to their ability level. The student’s score are increase by using adaptive test if it compared with conventional test. It proved with an experiment by look the average result from every student. Student’s scores who is using adaptive test is increaser than using the conventional test score. By the average score of students who use adaptive test are 80, while the average score of students who use the conventional test are 60.882353.Keywords: E-Learning, Fuzzy Item Response Theory (FIRT), Adaptive Test, Conventional Test
Gestur Berbasis Estimasi Sudut Gulung untuk Pengendalian Manipulator Muhammad Fuad
Jurnal Otomasi Kontrol dan Instrumentasi Vol 8 No 2 (2016): 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.2016.8.2.2

Abstract

Salah satu tantangan dalam pengendalian robot manipulator dengan menggunakan gestur tubuh secara intuitif terletak pada kesulitan penentuan sudut gulung dari end-effector. Penelitian ini mengusulkan suatu metoda untuk melakukan estimasi sudut gulung dengan menafsirkan perubahan tata letak dari fitur-fitur citra yang terbaca dari aliran data video. Sebuah kamera web yang dipasang pada lengan pengguna menangkap perubahan dari lingkungan dan mengubah informasi ini menjadi perintah untuk mengendalikan sudut gulung. SCORBOT -ER 9 Pro digunakan dalam percobaan dengan menerapkan kemampuan untuk mengendalikan sumbu kelima dari manipulator ini.  
Cooperative Position-based Formation-pursuit of Moving Targets by Multi-UAVs with Collision Avoidance Siti Nurjanah; Trihastuti Agustinah; Muhammad Fuad
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 6, No 2 (2022): October
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v6i2.310

Abstract

The paper focuses on the issue of capturing a moving target for multiple unmanned aerial vehicles (UAVs). The problem involves a group of UAVs to create a formation-pursuit in the encirclement of a moving target. Dynamic task allocation algorithm is used in 3D dynamic environments to efficiently allocate the target to several existing UAVs. Target information is disseminated to neighbor UAVs by the temporary leader of UAVs. For the formation-pursuit using a position-based strategy, destination points to create formation are made at the sphere coordinates around a moving target. Then the destination points are tracked using a fuzzy state feedback controller. Optimized artificial potential field (APF) algorithm is used to avoid collisions with targets, other UAVs, and static obstacles. Each UAV can choose the optimal trajectory to avoid obstacles and reset the formation after passing them. The simulation results show that multi-UAVs successfully surrounded and formed formation-pursuit of a moving target without colliding with the closest Euclidean distance between UAVs of 1.32957 m. UAVs with a target is 1.94359 m, and UAVs with static obstacles within a range of 1.60632 m.Keywords—formation-pursuit, multi-UAVs, obstacle avoidance, task allocation, tracking control.
Towards Controlling Mobile Robot Using Upper Human Body Gesture Based on Convolutional Neural Network Fuad, Muhammad; Umam, Faikul; Wahyuni, Sri; Fahriani, Nuniek; Nurwahyudi, Ilham; Darwaman, Mochammad Ilham; Maulana, Fahmi
Journal of Robotics and Control (JRC) Vol 4, No 6 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i6.20399

Abstract

Human-Robot Interaction (HRI) has challenges in investigation of a nonverbal and natural interaction. This study contributes to developing a gesture recognition system capable of recognizing the entire human upper body for HRI, which has never been done in previous research. Preprocessing is applied to improve image quality, reduce noise and highlight important features of each image, including color segmentation, thresholding and resizing. The hue, saturation, value (HSV) color segmentation is executed by utilizing blue color backdrop and additional lighting to deal with illumination issue. Then thresholding is performed to get a black and white image to distinguish between background and foreground. The resizing is completed to adjust the image to match the size expected by the model. The preprocessed data image is used as input for gesture recognition based on Convolutional Neural Network (CNN). This study recorded five gestures from five research subjects in difference gender and body posture with total of 450 images which divided into 380 and 70 images for training and testing respectively. Experiments that performed in an indoor environment showed that CNN achieved 92% of accuracy in the gesture recognition. It has lower level of accuracy compare to AlexNet model but with faster training computation time of 9 seconds. This result was obtained by testing the system over various distances. The optimal distance for a camera setting from user to interact with mobile robot by using gesture was 2.5 m. For future research, the proposed method will be improved and implemented for mobile robot motion control.
Obstacle Avoidance Based on Stereo Vision Navigation System for Omni-directional Robot Umam, Faikul; Fuad, Muhammad; Suwarno, Iswanto; Ma'arif, Alfian; Caesarendra, Wahyu
Journal of Robotics and Control (JRC) Vol 4, No 2 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i2.17977

Abstract

This paper addresses the problem of obstacle avoidance in mobile robot navigation systems. The navigation system is considered very important because the robot must be able to be controlled from its initial position to its destination without experiencing a collision. The robot must be able to avoid obstacles and arrive at its destination. Several previous studies have focused more on predetermined stationary obstacles. This has resulted in research results being difficult to apply in real environmental conditions, whereas in real conditions, obstacles can be stationary or moving caused by changes in the walking environment. The objective of this study is to address the robot’s navigation behaviors to avoid obstacles. In dealing with complex problems as previously described, a control system is designed using Neuro-Fuzzy so that the robot can avoid obstacles when the robot moves toward the destination. This paper uses ANFIS for obstacle avoidance control. The learning model used is offline learning. Mapping the input and output data is used in the initial step. Then the data is trained to produce a very small error. To support the movement of the robot so that it is more flexible and smoother in avoiding obstacles and can identify objects in real-time, a three wheels omnidirectional robot is used equipped with a stereo vision sensor. The contribution is to advance state of the art in obstacle avoidance for robot navigation systems by exploiting ANFIS with target-and-obstacles detection based on stereo vision sensors. This study tested the proposed control method by using 15 experiments with different obstacle setup positions. These scenarios were chosen to test the ability to avoid moving obstacles that may come from the front, the right, or the left of the robot. The robot moved to the left or right of the obstacles depending on the given Vy speed. After several tests with different obstacle positions, the robot managed to avoid the obstacle when the obstacle distance ranged from 173 – 150 cm with an average speed of Vy 274 mm/s. In the process of avoiding obstacles, the robot still calculates the direction in which the robot is facing the target until the target angle is 0.
Modified Extremum Seeking Control for Target Tracking and Formation Control in Pursuit-Evasion Game Setiawan, Fachruddin Ari; Agustinah, Trihastuti; Fuad, Muhammad
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 6, No 2 (2022): October
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v6i2.320

Abstract

In a pursuit-evasion game, the mobile robot pursuer's ability to navigate from its initial position to the evader while maintaining a safe distance from other objects requires a good obstacle avoidance system. This study aims to perform target tracking in evader sieges and obstacle avoidance against other pursuer robots and static obstacles by proposing a modified extreme seeking controller (ESC). A modified backstepping control (BC) was used as an autopilot control for a nonholonomic mobile robot to execute the modified ESC command. The modified BC based on the modified ESC requires the positions of the targeted evader, pursuers, and obstacles. The pursuer uses this information to capture an evader by arranging the desired formation without colliding with static obstacles or other robots. The results of the simulations show that the pursuers successfully surround the evader and construct the formation without colliding with obstacles. The proposed method resulted in the closest distance of 2.071 m between the pursuers, 1.954 m between each pursuer and the evader, and 2.425 m between the pursuers and static obstacles.
A Real-Time Human-Drone Interaction System for Cornfield Perimeter Monitoring Using Hand Gesture Control Fadzillah Akbar Subkhi; Muhammad Fuad; Sri Wahyuni; Achmad Imam Sudianto; Tri Widyaningrum, Vivi; Ach. Dafid
Journal of Technology and Informatics (JoTI) Vol. 8 No. 1 (2026): Vol. 8 N. 1 (2026)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v8i1.1314

Abstract

Perimeter monitoring in agricultural fields is essential for maintaining security and ensuring continuous observation of field conditions. This study develops a real-time human–drone interaction system using hand-gesture recognition based on MediaPipe Hands and a Support Vector Machine (SVM) classifier. A custom dataset of 24,000 images across 12 gesture classes was collected and converted into 42 hand landmarks (x, y, z), normalized relative to the wrist point. The SVM model with an RBF kernel was trained using an 80:20 split and achieved a testing accuracy of 99.18%. The system operates at 109 FPS with an average latency of 9.16 ms, enabling rapid and reliable drone responses to gesture commands. Field testing in a cornfield with FPV camera visualization demonstrated that the system consistently recognized gestures in varying outdoor lighting, allowing drones to execute precise perimeter checks and maneuvers. These results highlight the significant potential of integrating gesture recognition with drone control, providing a practical, real-world solution that advances smart farming, increases agricultural efficiency, and supports technological progress toward Sustainable Development Goals. The proposed system thus offers a lightweight, responsive, and impactful tool for modern agricultural perimeter monitoring.
Brush-shaped Motion Gesture of UGV Using Hand Gesture Recognition Agus, Agus Murdiono; Muhammad Fuad; Hairil Budiarto; Faikul Umam; Vivi Tri Widyaningrum; Achmad Imam Sudianto
Journal of Technology and Informatics (JoTI) Vol. 8 No. 1 (2026): Vol. 8 N. 1 (2026)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v8i1.1315

Abstract

Manual observation of corn leaf diseases in agricultural fields often faces challenges related to time, effort, and accuracy. To address these challenges, brush-shaped motion patterns, such as zig-zag and boustrophedon trajectories, provide an effective solution by enabling uniform area coverage while reducing redundant traversal, energy consumption, and sensing gaps, making them well-suited for precision agriculture applications. Building on this approach, the system utilizes the MediaPipe framework for hand landmark tracking and the K-Nearest Neighbors (KNN) algorithm to recognize six navigation commands: forward, backward, stop, turn_right, turn_left, and capture. These commands are transmitted via Wi-Fi with an average latency of 0.001964 s. To ensure navigation accuracy during pattern execution, corrections are made using rotary encoders. Gesture classification experiments on 6,000 samples achieved a maximum accuracy of 99.46% across two participants, with stable KNN performance under both indoor and outdoor lighting variations, as well as hand distances ranging from 50 cm. Furthermore, the capture gesture produced an average image acquisition latency of 0.3037 s at various UGV observation positions. In summary, these results demonstrate that integrating real-time gesture control with UGV maneuvers enables systematic field surveys for maize leaf disease monitoring and supports Sustainable Development Goal (SDG) 2 through precision agriculture technology.
Co-Authors Achmad Imam Sudianto Agus, Agus Murdiono Ahmad Sahru Romadhon Alfian Ma’arif Andharini Dwi Cahyani Dafid, Ach Darwaman, Mochammad Ilham Diah Kusumawati Fadzillah Akbar Subkhi Faikul Umam Hairil Budiarto Maulana, Fahmi Nuniek Fahriani, Nuniek Nurwahyudi, Ilham Setiawan, Fachruddin Ari Siti Nurjanah Sri Wahyuni Sri Wahyuni Suwarno, Iswanto Trihastuti Agustinah Trihastuti Agustinah Vivi Tri Widyaningrum Vivi Tri Widyaningrum, Vivi Wahyu Caesarendra