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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Scientific Journal of Informatics BAREKENG: Jurnal Ilmu Matematika dan Terapan
Arof, Hamzah
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Optimized Kalman filtering in dynamical environments for thumb robot motion estimation Herlambang, Teguh; Susanto, Fajar Annas; Firdaus, Aji Akbar; Kusuma, Vicky Andria; Suprapto, Sena Sukmananda; Muhaimin, Muhaimin; Arof, Hamzah
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i3.pp512-519

Abstract

Stroke, a prevalent nerve disorder in Indonesia, necessitates post-stroke rehabilitation like physical and occupational therapy. Hand and finger muscle training, crucial for restoring movement, often involves innovative solutions like finger prosthetic robotics arms. In particular, the advancement in thumb robotics emphasizes the estimation of thumb motion, where the ensemble Kalman filter square root (EnKF-SR) and H-infinity methods are deemed dependable for both linear and nonlinear models. Simulation results, using 400 ensembles, demonstrated nearly identical accuracy between the methods, exceeding 99%, with a 6-7% increase in accuracy compared to 200 ensembles. These advancements offer promising prospects for effective post-stroke rehabilitation and improved thumb movement restoration.
Implementation of flexible axis photovoltaic system based on internet of things Firdaus, Aji Akbar; Daud, Muhamad Zalani; Rajendran, Parvathy; Solihin, Mahmud Iwan; Wang, Li; Azmita, Mimi; Arof, Hamzah
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i1.pp157-164

Abstract

Electricity is a crucial aspect in human life. With population growth, ongoing regional development, and continuous construction activities, the demand for electricity and fuel in Indonesia is increasing. The substantial power consumption leads to larger financial expenditures for the community. Additionally, the use of electricity, as it has been traditionally employed, has negative environmental impacts. Solutions are needed to address these issues, and one effort involves the use of renewable energy, such as the development of solar power plants (PLTS). PLTS, also known as solar cells, is preferred as it can be used for various relevant purposes in different locations, particularly in offices, factories, residential areas, and others. However, the use of static, single-axis, and dual-axis solar panels still has drawbacks, such as suboptimal sunlight intensity and high motor power consumption. Therefore, a flexible-axis solar panel tracking system has been developed to follow the direction of sunlight, ensuring optimal power efficiency, and significant electricity generation. The flexible-axis tracker system results in a 34.13% increase in power efficiency.
COMPARISON OF K-NEAREST NEIGHBOR AND NEURAL NETWORK FOR PREDICTION INTERNATIONAL VISITOR IN EAST JAVA Novita, Dina; Herlambang, Teguh; Asy’ari, Vaizal; Alimudin, Arasy; Arof, Hamzah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp2057-2070

Abstract

Tourism is one of the government's priority sectors for economic growth. East Java is one of Indonesia's provinces and is attractive to international visitors. International visitors will appreciate the natural beauty and multiculturalism offered by East Java. In this study, predictions of international visitor visits in East Java from the entrance of Juanda International Airport were carried out using k-NN (k-Nearest Neighbor) and a neural network. The dataset used is based on BPS statistics of Jawa Timur Province in the form of the number of international visitor arrivals from January 2000 to February 2024. The datasets were distributed by dividing the data into 70% for training data and 30% for testing data. The creation of the k-NN model is carried out using k-values 2 to 7. The creation of a modern neural network using hidden layers 1 to 3. The prediction results that were made using k-NN obtained optimal RMSE at k-values 2, resulting in an RMSE of 1594,674 or an error of 3,98%. Meanwhile, the prediction results that have been made using neural networks obtained optimal RMSE at two hidden layers, which resulted in an RMSE of 1873, 355 or an error of 4,68%. So, it is recommended that the k-NN algorithm be used to predict the number of international visitors in East Java. The results of this study can be used to provide quantitative information for the government and stakeholders in adjusting the program to the development of international visitors visiting East Java.
YOLO vs. CNN Algorithms: A Comparative Study in Masked Face Recognition Dewanto, Muhammad Ridho; Farid, Mifta Nur; Rafdi Syah, Muhammad Abby; Firdaus, Aji Akbar; Arof, Hamzah
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.48723

Abstract

Purpose: This research investigates the effectiveness of YOLO (You Only Look Once) and Convolutional Neural Network (CNN) in real-time face mask recognition, addressing the challenges posed by mask-wearing in infectious disease prevention.Method: Utilizing a diverse dataset and employing YOLO's object detection and a combined Haar Cascade Algorithm with CNN, the study evaluated key performance indicators, including accuracy, framerate, and F1 Score.Results: Results indicated that CNN outperformed YOLO in accuracy (99.3% vs. 79.3%) but operated at a slightly lower framerate. YOLO excelled in recall and precision, presenting a compelling choice for specific application needs. The research underscores the importance of considering factors beyond accuracy for informed decision-making in the realm of face mask recognition.Novelty: This research evaluates the real-time performance of YOLO and CNN algorithms in masked face recognition, highlighting the crucial balance between framerate efficiency and detection accuracy.
Co-Authors Arasy Alimudin Asy’ari, Vaizal Azmita, Mimi Daud, Muhamad Zalani Dewanto, Muhammad Ridho Dina Novita, Dina Farid, Mifta Nur Firdaus, Aji Akbar Li Wang Mahmud Iwan Solihin Muhaimin Muhaimin Rafdi Syah, Muhammad Abby Rajendran, Parvathy Suprapto, Sena Sukmananda Susanto, Fajar Annas Teguh Herlambang Vicky Andria Kusuma