Article Per Year (5 Year)
p-Index From 2020 - 2025
2.293
P-Index
This Author published in this journals
All Journal J@TI (TEKNIK INDUSTRI) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah Mikrotek Prosiding SNATIKA Vol 01 (2011) REKAYASA Jurnal Ilmiah Pangabdhi Jurnal Teknik Mesin Journal of Robotics and Control (JRC) International Journal of Science, Engineering and Information Technology International Journal of Robotics and Control Systems
Faikul Umam
Mechatronics Engineering, University Of Trunojoyo Madura
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Physics Social Sciences Other

Published : 24 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 24 Documents
Search

 1   2   3 
Enhanced Human Hitting Movement Recognition Using Motion History Image and Approximated Ellipse Techniques Diyasa, I Gede Susrama Mas; P, Made Hanindia; Zamri, Mohd; Agussalim, Agussalim; Humairah, Sayyidah; A, Denisa Septalian; Umam, Faikul
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1599

Abstract

Recognition of human hitting movement in a more specific context of sports like boxing is still a hard task because the existing systems use manual observation which could be easily flawed and highly inaccurate. However, in this study, an attempt is made to present an automated system designed for this purpose to detect a specific hitting movement commonly known as a punch using video input and image processing techniques. The system employs Motion History Image (MHI) to model trajectories of motions and combine them with other parameters to reconstruct movements which tend to have a temporal component. Thus, CCTV cameras set at different positions (front, back, left and right) enable the system to identify several types of punches including Jab, Hook, Uppercut and Combination punches. The most important aspect of this work is the proposal of MHI and the Ellipse approximation which is quicker in the integration of both than other sophisticated systems which take a considerable duration in computations. Therefore, the system classifies C_motion, Sigma Theta, and Sigma Rho parameters to distress hitting from non-hitting movements. Evaluation on a dataset captured from multiple viewpoints establishes that the system performs well achieving the goal of 93 percent when detecting both the hitting and the non-hitting motion. These results demonstrate the system’s superiority to the system based such detection methods. This study paves the way for other applications in real-time such as sports analysis, security surveillance, and healthcare requiring greater efficiency in and accuracy of human movement assessment. The focus of future work may be in the direction of improving the recognition of slower movements, also modifying the system for more dynamic conditions in the future.
Enhanced Precision Control of a 4-DOF Robotic Arm Using Numerical Code Recognition for Automated Object Handling Sukri, Hanifudin; Ibadillah, Achmad Fiqhi; Thinakaran, Rajermani; Umam, Faikul; Dafid, Ach.; Kurniawan, Adi; Morshed, Md. Monzur; Kurniawan, Denni
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

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

Abstract

This research develops a 4-DOF robotic arm system that utilizes numerical codes for accurate, automated object handling, supporting advancements in sustainable industrial automation aligned with the UN Sustainable Development Goals (SDGs), particularly Industry, Innovation, and Infrastructure (SDG 9). Key contributions include the integration of EasyOCR for reliable code recognition and a control mechanism that enables precise positioning. The robotic system combines a webcam for visual sensing, servo motors for movement, and a gripper for object manipulation. EasyOCR effectively recognizes numerical codes on randomly positioned objects against a uniform background while the microcontroller calculates servo angles to guide the arm accurately to target positions. Testing results show a success rate exceeding 94% for detecting codes 1 to 4, with minor servo angle errors requiring adjustments in arm extension by 30 mm to 50 mm. Positional error analysis reveals an average error of less than 1.5 degrees. Although environmental factors like lighting can influence code visibility, this approach outperforms traditional methods in adaptability and precision. Future research will focus on enhancing code recognition under variable lighting and expanding the system's adaptability for diverse object types, broadening its applications in industries demanding high efficiency.
Optimizing K-Nearest Neighbors with Particle Swarm Optimization for Improved Classification Accuracy Dafid, Ach.; Sudianto, Achmad Imam; Thinakaran, Rajermani; Umam, Faikul; Adiputra, Firmansyah; Izzuddin; Sitepu Debora , Ribka
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30775

Abstract

This study aims to improve the performance of the K-Nearest Neighbors (KNN) algorithm in classifying public reviews of Batik Madura through optimizing the K value using the Particle Swarm Optimization (PSO) algorithm. Public reviews collected from the Google Maps platform are used as a dataset, with positive, negative, and neutral sentiment categories. Optimization of the K value is carried out to overcome the constraints of KNN performance, which is highly dependent on the K parameter, with PSO providing a more efficient approach than the grid search method. However, PSO also presents challenges such as sensitivity to parameter tuning and potential computational overhead. This study has succeeded in developing a web-based system using the Python Streamlit framework, which makes it easy for users to access sentiment analysis results. Testing shows that optimizing the K value with PSO increases the accuracy of KNN to 88.5% with an optimal K value of 19. However, this accuracy is not compared to other optimization techniques, leaving its relative advantage unverified. The results are expected to help Batik Madura entrepreneurs in evaluating public perception and guiding strategic innovations. Research outputs include a prototype, intellectual property registration, and journal publication, although the role of deep learning models is only briefly noted without further development.
Penerapan Sistem Kontrol Adaptif Proportional Integral Derivative (PID) pada Mesin Penimbang Mie dengan Konveyor Dafid, Ach; Umam, Faikul; Budiarto, Hairil
Rekayasa Vol 18, No 2: Agustus, 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v18i2.31610

Abstract

Indonesia is the second-largest instant noodle consumer in the world after China, with consumption reaching more than 12 billion packs per year. This high demand drives the need for innovation in the production process, especially in the weighing and cutting aspects, which are still carried out manually in small and medium industries. Manual processes not only require more time and energy, but also result in variations in packaging weight that are not uniform and reduce production efficiency. This study aims to design and implement a Proportional Integral Derivative (PID) adaptive control system on a noodle weighing machine with a conveyor. The system was developed using a load cell sensor to measure the noodle dough weight, a servo motor as a cutting actuator, and a DC motor as a conveyor drive, all of which are controlled by an Arduino ATmega 2560 microcontroller. The research methodology includes mechanical design, electronic design, control system programming, sensor calibration, and performance testing. The test results show that the system is able to produce noodle portions with a target weight of 50 grams consistently. The prototype has conveyor dimensions of 100×20×8 cm with a speed of 26 cm/ms, controlled using tuned PID parameters (Kp=1.5; Ki=1; Kd=1.7). From 20 trials, the system produced an average error of 0.75% and a success rate of 99.25%. Thus, the application of the PID adaptive control system has been proven to improve weighing precision, conveyor speed stability, and production efficiency. This innovation is expected to be a simple and affordable solution to support the automation of small and medium industries in Indonesia in facing increasingly fierce food market competition.
Co-Authors - - M.latif A, Denisa Septalian Achmad Fiqhi Ibadillah Adi Kurniawan Aeri Rachmad Agussalim, Agussalim Alfian Ma’arif Andharini Dwi Cahyani Ari Basuki Ari Basuki Dafid, Ach Darwaman, Mochammad Ilham Devie Rosa Anamisa Diyasa, I Gede Susrama Mas Firmansyah Adi Putra Firmansyah Adiputra Hairil Budiarto Hairil Budiarto Humairah, Sayyidah Izzuddin Kukuh Winarso Kurniawan, Denni Maulana, Fahmi Misri Gozan Mochamad Yusuf Efendi, Mochamad Yusuf Morshed, Md. Monzur Muhammad Fuad Nizar Amir Nuniek Fahriani, Nuniek Nurwahyudi, Ilham P, Made Hanindia Rachmad Hidayat Sitepu Debora , Ribka Sri Wahyuni Sri Wahyuni Sri Wahyuni Sudianto, Achmad Imam Sukri, Hanifudin Suwarno, Iswanto Thinakaran, Rajermani Triwidyaningrum, Vivi Wahyu Caesarendra Wahyu, Fiki Milatul Yuli Panca Asmara Zamri, Mohd