Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal International Journal of Renewable Energy Development Jurnal Teknik Informatika (JUTIF)
Alkhafaji, Mohammed Ayad
Unknown Affiliation
Chemistry Computer Science & IT Energy

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Design and Speed Control of SynRM using Cascade PID Controller with PSO Algorithm Alkhafaji, Mohammed Ayad; Uzun, Yunus
International Journal of Renewable Energy Development Vol 9, No 1 (2020): February 2020
Publisher : Center of Biomass & Renewable Energy, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijred.9.1.69-76

Abstract

In recent years, the variable speed motor drive is supported over a fixed speed motor drive as per essentialness safeguarding, speed or position control and improvement of transient response characteristics. The aim of any speed controller is to take main signal that represent the reference speed and to drive the framework at that reference speed. This paper exhibits the design, simulation and control of synchronous reluctance motor (SynRM). In addition, the motor speed is controlled by utilizing a conventional PID controller that has been used from the cascaded structure. The Particle Swarm Optimization (PSO) was used to find the best parameters of the PID controller. Lead-Lag controller presents from the cascaded controller as the following period of control. The Space vector pulse width modulation (SVPWM) plot has been proposed to control the motor and make the motor work with no rotor confine contingent upon the info parameters that utilization in the simulation. An examination between both of PID tuned and PSO tuned controller affirms that the PSO gives dazzling control highlights to the motor speed and have an edge over the physically changing controller. Thus, this paper present investigation and simulation for the most precise procedures to control the speed reaction and torque reaction of synchronous reluctance motor (SynRM).©2020. CBIORE-IJRED. All rights reserved
Automated Video Recognition of Traditional Indonesian Dance Using Hyperparameter-Tuned Convolutional Neural Network Purwaningrum, Santi; Susanto, Agus; Susanti, Hera; Alkhafaji, Mohammed Ayad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5157

Abstract

Traditional Indonesian dances serve as a vital expression of cultural identity and regional heritage, yet their preservation through intelligent video recognition remains limited due to technical challenges in motion complexity, costume variation, and the lack of annotated datasets. Prior research commonly employed Convolutional Neural Networks (CNNs) with manually defined hyperparameters, which often resulted in overfitting and poor adaptability when applied to dynamic and real-world video inputs. To overcome these limitations, this study proposes a robust and adaptive classification framework utilizing a hyperparameter-tuned CNN model. The approach automatically optimizes key training parameters such as learning rate, batch size, optimizer type, and epoch count through iterative experimentation, thereby maximizing the model’s ability to generalize across both static and temporal data domains. The model was trained using image datasets representing three traditional dances (Gambyong, Remo, and Topeng), and subsequently tested on segmented frames extracted from YouTube videos. Results indicate strong model performance, achieving 99.67% accuracy on the training set and 100% accuracy, precision, recall, and F1-score across all testing videos. The proposed method successfully bridges the gap between still-image learning and real-world motion recognition, making it suitable for practical applications in digital archiving and cultural documentation. This study’s contribution lies not only in the model’s technical effectiveness but also in its support for preserving intangible cultural assets through intelligent and automated video-based recognition. Future work may incorporate temporal modelling or multi-camera perspectives to further enrich motion understanding and extend the system to broader performance domains.
Co-Authors Agus Susanto Hera Susanti, Hera Purwaningrum, Santi Uzun, Yunus