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All Journal Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknik Informatika (JUTIF) Jurnal Informatika: Jurnal Pengembangan IT
Putra, Muhammad Pajar Kharisma
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Computer Science & IT Decision Sciences, Operations Research & Management

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Journal : Jurnal Teknik Informatika (JUTIF)

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LEVERAGING DEEP LEARNING APPROACH FOR ACCURATE ALPHABET RECOGNITION THROUGH HAND GESTURES IN SIGN LANGUAGE Nugroho, Nadiyan Syah Wahyu; Putra, Muhammad Pajar Kharisma
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.2025.6.1.3912

Abstract

Sign language is one way of communication used by people who cannot speak or hear (deaf and speech impaired), so not everyone can understand sign language. Therefore, to facilitate communication between normal people and deaf and speech-impaired people, many systems have been created to translate gestures and signs in sign language into understandable words. Artificial intelligence and computer vision-based technologies, such as YOLOv9 offer solutions to recognize hand gestures more quickly, accurately, and efficiently. This research aims to develop a hand gesture detection system for alphabetic sign language using YOLOv9 architecture, with the aim of improving the accuracy and speed of hand gesture detection. The data used consists of 6500 sign language alphabet hand gesture images that have been labeled with bounding boxes and processed using image augmentation techniques. The model was trained on the Kaggle platform and evaluated using performance metrics such as Accuracy, Precision, Recall, F1-Score, and Intersection over Union (IoU). The results show that the YOLOv9 model achieves an average detection accuracy of 97%, with precision and recall above 90% for most classes. In addition, YOLOv9 shows advantages over other algorithms such as SSD MobileNet v2 and Faster RCNN, both in terms of speed and accuracy. In conclusion, YOLOv9 proved to be very effective in detecting sign language hand gestures, thereby speeding up and facilitating communication. This research is expected to contribute to the development of more inclusive technologies in various fields, such as education, public services, and employment opportunities, which support better communication between sign language users and the general public.
PERFORMANCE EVALUATION OF YOLOV8 IN REAL-TIME VEHICLE DETECTION IN VARIOUS ENVIRONMENTAL CONDITIONS Marcelleno, Derit Junio; Putra, Muhammad Pajar Kharisma
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.2025.6.1.3916

Abstract

This research focuses on assessing and developing a real-time detection system using the YOLOv8 algorithm. Accurate and fast vehicle detection is a big challenge in modern traffic management, especially in various environmental conditions such as bad weather, low lighting, and high traffic density. The aim of this study was to evaluate the performance of YOLOv8 under these conditions and identify potential improvements. The dataset used consists of 16,990 vehicle images with various variations and environmental conditions. After being trained, the model is evaluated using metrics such as precision, recall, and F1-score, as well as Intersection over Union (IoU) with a threshold of 0.8 on IoU. The results show that YOLOv8 is superior with a fairly high detection accuracy of 78%, with precision of 82% and recall above 90%, and is able to detect vehicles in real-time conditions. However, the challenge of detecting small objects or irregularly shaped vehicles such as tractors still needs to be optimized. This research also compared the performance of YOLOv8 with the SSD (Single Shot Detector) algorithm, where YOLOv8 was proven to be superior in terms of accuracy, precision, recall and F1-score. The research results obtained provide valuable insights for the development of traffic management systems based on deep learning technology. The main contribution of this research is to provide a more efficient and effective vehicle detection solution, which can be applied in modern traffic management systems. Thus, it is hoped that the results of this research can increase the efficiency of traffic management and have a positive impact on the development of intelligent transportation systems in the future.
YOLOv9 – BASED TRAFFIC SIGN DETECTION UNDER VARYING LIGHTING CONDITIONS Pangestu, Akbar; Putra, Muhammad Pajar Kharisma
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.2025.6.1.3917

Abstract

Traffic signs are an important element that functions as a guide, regulator and safety supervisor for road users. In Indonesia, there are various types of traffic signs, including recommendation, prohibition, warning, command, and direction signs, which use numbers, letters, symbols, or a combination of the three to convey clear information to drivers. Based on data from the Indonesian National Police, 148,575 cases of traffic accidents were recorded in 2023, which continues to increase every day due to human error, poor road conditions, and lack of clarity and completeness of signs. This research aims to develop traffic sign detection technology using the YOLOv9 algorithm, starting with collecting 7,980 images from the Roboflow platform, which are then labeled and trained, and evaluated using metrics such as Accuracy, Precision, Recall, F1-Score, and Intersection over Union (IoU ). Then the model was tested to detect traffic signs in various media, such as images and videos. The results of this research show that the YOLO v9 model has the best performance compared to SSD MobileNet v2 and Faster RCNN. The YOLOv9 model achieved an accuracy of 94%, while SSD MobileNet v2 only had an accuracy of 43%, and Faster RCNN had an accuracy of 57%. From the research, it can be concluded that the YOLOv9 model is optimal enough to detect traffic signs in various lighting conditions, because the model has the best performance compared to the other two models, especially in terms of accuracy and balance between precision and recall. This research is expected to support the development of safer autonomous vehicles and intelligent transportation systems through optimal traffic sign detection.
Co-Authors Adriansyah, Dedy Fatmawati, Indah Rani Handoyo, Tri Ferli Marcelleno, Derit Junio Nugroho, Nadiyan Syah Wahyu Pangestu, Akbar