Article Per Year (5 Year)
p-Index From 2021 - 2026
1.784
P-Index
This Author published in this journals
All Journal Jurnal Masyarakat Informatika Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknik Informatika (JUTIF)
Ali, Rabei Raad
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search
Journal : Jurnal Teknik Informatika (JUTIF)

 1 
FOOTBALL PLAYER TRACKING, TEAM ASSIGNMENT, AND SPEED ESTIMATION USING YOLOV5 AND OPTICAL FLOW Hartono, Matthew Raymond; Sari, Christy Atika; Ali, Rabei Raad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4165

Abstract

Football analysis is indispensable in improving team performance, developing strategy, and assessing the capabilities of players. A powerful system that combines YOLOv5 for object detection with optical flow tracks football players, assigns them to their respective teams, and estimates their speeds accurately. In the most crowded scenarios, the players and the ball are detected by YOLOv5 at 94.8% and 93.7% mAP, respectively. KMeans clustering based on jersey color assigns teams with 92.5% accuracy. Optical flow is estimating the speed with less than 2.3%. The perspective transformation using OpenCV improves trajectory and distance measurement, overcoming the challenges in overlapping players and changing camera angles. Experimental results underlined the system's reliability for capturing player speeds from 3 to 25 km/h and gave insight into the dynamic nature of team possession. However, there is still some challenge: 6% accuracy degradation in high overlap and illuminative changes. The future work involves expanding the dataset for higher robustness and ball tracking, which will comprehensively explain the dynamics of a match. The paper presents a flexible framework for automated football video analysis that paves the way for advanced sports analytics. This would also, in turn, enhance informed decision-making by coaches, analysts, and broadcasters by providing them with actionable metrics during training and competition. The proposed system joins the state-of-the-art YOLOv5 with optical flow and thereby forms the backbone of near-future football analysis.
VGG-16 ARCHITECTURE ON CNN FOR AMERICAN SIGN LANGUAGE CLASSIFICATION Meitantya, Mutiara Dolla; Sari, Christy Atika; Rachmawanto, Eko Hari; Ali, Rabei Raad
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2160

Abstract

Every country has its sign language such as in Indonesia there are 2 types namely Indonesian Sign Language System called SIBI and BISINDO (Indonesian Sign Language). American Sign Language (ASL) is a sign language that is widely used in the world. In this research, the classification of American Sign Language (ASL) using the Convolutional Neural Network (CNN) method using VGG-16 architecture with Adam optimizer. The data used is 14000 ASL image data with 28 classes consisting of letters A to Z plus space and nothing with a division of 90% training data and 10% validation data. From this research, the overall accuracy is obtained with a value of 98% and the accuracy value of validation data evaluation is 89.07%.
Enhancing MPEG-1 Video Quality Using Discrete Wavelet Transform (DWT) with Coefficient Factor and Gamma Adjustment Krismawan, Andi Danang; Susanto, Ajib; Rachmawanto, Eko Hari; Muslih, Muslih; Sari, Christy Atika; Ali, Rabei Raad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4422

Abstract

Low-quality video caused by compression artifacts, noise, and loss of detail remains a significant challenge in video processing, affecting applications in streaming, surveillance, and medical imaging. Existing enhancement techniques often struggle with excessive noise amplification or high computational complexity, making them inefficient for real-time applications. This study proposes an improved video enhancement method using Discrete Wavelet Transform (DWT) with optimized coefficient factor and gamma adjustment. DWT is a mathematical approach that decomposes video frames into frequency subbands, enabling selective enhancement of important details. To analyze the impact of different wavelets, this study evaluates Coif5, db1, sym4, and sym8 wavelets. The sym8 wavelet, known for its high symmetry and ability to minimize artifacts, achieves the best results in preserving fine details and structural integrity. The coefficient factor is dynamically adjusted to sharpen details while preventing noise amplification, and gamma adjustment is applied to optimize brightness and contrast. The proposed method was evaluated using Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). Experimental results show that sym8 wavelet with gamma 0.7 and coefficient factor 0.3 provides the best balance, achieving an MSE of 0.062, a PSNR of 12.050 dB, and an SSIM of 0.674, outperforming Coif5, db1, and sym4 wavelets. The results indicate that wavelet selection significantly impacts video enhancement performance, with sym8 providing superior contrast enhancement and noise suppression. This study contributes to real-time video processing and AI-based applications, ensuring enhanced visual quality with minimal computational overhead.
Multi-Class Brain Tumor Segmentation and Classification in MRI Using a U-Net and Machine Learning Model Hendrik, Jackri; Pribadi, Octara; Hendri, Hendri; Hoki, Leony; Tarigan, Feriani Astuti; Wijaya, Edi; Ali, Rabei Raad
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.2025.6.5.5369

Abstract

Brain tumor diagnosis remains a critical challenge in medical imaging, as accurate classification and precise localization are essential for effective treatment planning. Traditional diagnostic approaches often rely on manual interpretation of MRI scans, which can be time-consuming, subjective, and prone to variability across radiologists. To address this limitation, this study proposes a two-stage framework that integrates machine learning (ML) based classifiers for tumor type recognition and a U-Net architecture for tumor segmentation. The classifier was trained to distinguish four tumor categories: glioma, meningioma, pituitary, and no tumor, while the U-Net model was employed to delineate tumor regions at the pixel level, enabling volumetric assessment. The novelty of this research lies in its dual focus that combines classification and segmentation within a single framework, which enhances clinical applicability by offering both diagnostic and spatial insights. Experimental results demonstrated that among the evaluated classifiers, XGBoost achieved the highest accuracy of 86 percent, surpassing other models such as Random Forest, SVC, and Logistic Regression, while the U-Net model delivered consistent segmentation performance across tumor types. These findings highlight the potential of hybrid ML and deep learning solutions to improve reliability, efficiency, and objectivity in brain tumor analysis. In real-world practice, the proposed framework can serve as a valuable decision-support tool, assisting radiologists in early detection, reducing diagnostic workload, and supporting personalized treatment strategies.
An Intelligent IoT-Based Hydroponic Irrigation System for Strawberry Cultivation Using Extreme Gradient Boosting Decision Model Bijanto, Bijanto; Abidin, Zainal; Asy’ari, Fajar Husain; Ali, Rabei Raad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5173

Abstract

Most existing implementations rely on static rule-based or fuzzy logic control, which lack adaptability to dynamic environmental changes and often require manual tuning by experts. These limitations are particularly challenging for small-scale farmers who face constraints in technical knowledge, infrastructure, and operational flexibility. To address these issues, this study proposes an intelligent hydroponic irrigation system that embeds the Extreme Gradient Boosting (XGBoost) algorithm as a decision-making model. The system collects real-time sensor data including temperature, humidity, and light intensity, and uses the trained XGBoost classifier to determine irrigation needs with binary output (FLUSH or NO). The system was implemented on a vertical hydroponic setup for strawberry cultivation, and evaluated over a 21-day observation period. The results show that the XGBoost-based model was effective in maintaining consistent vegetative growth, with plants in upper-tier pipes achieving an average height above 25 cm by the end of the cycle. This demonstrates that the model could support responsive and resource-efficient irrigation control. Beyond technical performance, the research highlights the urgency of adopting data-driven smart farming systems to ensure sustainable food production, optimize limited resources, and empower small-scale farmers with accessible and scalable solutions. However, the proposed XGBoost model is still limited to local crops; therefore, when introducing new plant types or additional sensor inputs, parameter adjustments and retraining are required to maintain accuracy. Future improvements may include dynamic model retraining and integration with real-time feedback systems to enhance system autonomy and resilience in broader agricultural settings.
Co-Authors Ajib Susanto Asy’ari, Fajar husain Bijanto, Bijanto Candra Irawan Christy Atika Sari Dibyo Adi Wibowo Edi Wijaya Eko Hari Rachmawanto Erna Zuni Astuti Ery Mintorini Feriani Astuti Tarigan Gumelar, Rizky Syah Hartono, Matthew Raymond Hendri Hendri Hoki, Leony Jackri Hendrik Krismawan, Andi Danang Laksana, Deddy Award Widya Lalang Erawan Meitantya, Mutiara Dolla Moch. Sjamsul Hidajat Mulyono, Ibnu Utomo Wahyu Muslih Muslih Nabila, Qotrunnada Octara Pribadi Pratama, Reza Arista Prayugo, Moh. Lambang Wintaka, Aristides Bima Zainal Abidin