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All Journal IJoICT (International Journal on Information and Communication Technology) JRSI (Jurnal Rekayasa Sistem dan Industri) Scientific Journal of Informatics
Aradea
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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The The Recognition of American Sign Language Using CNN with Hand Keypoint Muhamad Asep Ridwan; Aradea; Husni Mubarok
International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023
Publisher : School of Computing, Telkom University

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

Abstract

Sign Language is a method used by the deaf community for their communication. In line with the advances of deep learning, researchers have widely interpreted neural networks for language recognition in recent years. Many models and hardware have been developed to help get high accuracy in language recognition, but generally, the problem of accuracy is still a concern of researchers, even the accuracy problem related to American language or American sign language (ASL) still requires further research to solve. This paper discusses a method to improve ASL recognition accuracy using Convolutional Neural Network (CNN) with hand keypoint. Pre-trained Keypoint detector is used to generate hand keypoints on the massey dataset as an input for classification in the CNN model. The results show that the accuracy of the proposed method is better than the previous studies, obtaining an accuracy of 99.1% in recognizing the 26 statistical signs of the ASL alphabet.
SOCA-YOLO: Smart Optic with Coordinate Attention Model for Vision System-Based Eye Disease Detection Rianto, Rianto; Purwayoga, Vega; Aradea; Mikail, Ali Astra; Yumna, Irsalina
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.29293

Abstract

Purpose: The purpose of this research is to identify eye diseases using a modified YOLOv9. In particular, we modified YOLOv9 with the addition of Coordinate Attention (CA) for better eye disease detection performance, the use of Programmable Gradient Information (PGI), and Generalized Efficient Layer Aggregation Network (GELAN) for higher computational efficiency and accuracy. Methods: This study consists of several stages, including the acquisition of eye disease data obtained from the Roboflow website, data annotation, image augmentation, modeling using a modified YOLOv9, and model evaluation. Result: SOCA-YOLO model achieved an F1 score of 87,2% and mAP50 of 92,9%, outperforming YOLOv9-e by 1,7%. It also surpassed YOLOv6-L6 by 11,1%, YOLOv10-X by 0,8% in mAP50, and YOLOv8-X by 1,1% in recall, showcasing its superior detection accuracy and recall performance. Novelty: This research contributes by introducing the SOCA-YOLO model in improving the performance of the YOLOv9 by modifying the addition of Coordinate Attention (CA) for better eye disease detection performance, alongside Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (GELAN) for better computational efficiency and accuracy.
The The Recognition of American Sign Language Using CNN with Hand Keypoint Ridwan, Muhamad Asep; Aradea; Mubarok, Husni
International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023
Publisher : School of Computing, Telkom University

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

Abstract

Sign Language is a method used by the deaf community for their communication. In line with the advances of deep learning, researchers have widely interpreted neural networks for language recognition in recent years. Many models and hardware have been developed to help get high accuracy in language recognition, but generally, the problem of accuracy is still a concern of researchers, even the accuracy problem related to American language or American sign language (ASL) still requires further research to solve. This paper discusses a method to improve ASL recognition accuracy using Convolutional Neural Network (CNN) with hand keypoint. Pre-trained Keypoint detector is used to generate hand keypoints on the massey dataset as an input for classification in the CNN model. The results show that the accuracy of the proposed method is better than the previous studies, obtaining an accuracy of 99.1% in recognizing the 26 statistical signs of the ASL alphabet.
Recognition of Organic Waste Objects Based on Vision Systems Using Attention Convolutional Neural Networks Models Aradea; Rianto; Mubarok, Husni
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.6494

Abstract

Purpose: High population growth and increasing consumption patterns have resulted in significant organic waste production. The public often does not understand the correct way to deal with the problem of organic waste, including public awareness regarding the need for its management. Therefore, a system is needed to recognize waste objects based on various types. Currently, much research in this field has been studying object recognition, for example, the implementation of the Convolutional Neural Networks (CNN) model. However, there are still various challenges that must be addressed, including objects with diverse visual characteristics such as form, size, color, and physical condition. This research focuses on developing a system that enhances object recognition of waste, specifically organic waste, using an Attention Convolutional Neural Network (ACNN). By integrating attention mechanisms into the CNN model, this study addresses the challenges of recognizing waste objects with diverse visual characteristics. The proposed system seeks to improve the accuracy and efficiency of organic waste identification, which is crucial for advancing waste management practices and reducing environmental impact. Methods: This research combines a CNN architecture with an attention mechanism to create a better object detection environment called Attention-CNN (ACNN). The ACNN architecture employed consists of one layer input, three convoluted layers, three max-pooling layers, one attention layer, one flattened layer, four dropout layers, and two dense layers arranged in a certain way. Result: The research result shows that the model CNN with attention mechanism (ACNN) was slightly better at 86.93% than the standard model of CNN, which accounted for 86.70% in accuracy. Novelty: In general, the current use of CNN architecture to address waste object recognition problems typically employs standard architectures, resulting in lower accuracy for complex waste objects. In contrast, our research integrates attention mechanisms into the CNN architecture (ACNN), enhancing the model's ability to focus on relevant features of waste objects. This leads to improved recognition accuracy and robustness against visual variability. This distinction is important as it overcomes the limitations of standard CNN models in handling visually diverse and complex waste objects, thereby highlighting the novelty and contribution of our research.
The Application of Template Matching Algorithm to Improve Accuracy of Plate Number Recognition R Moch Dysa Satria Anggara; Aradea; Rianto
Jurnal Rekayasa Sistem & Industri Vol 9 No 01 (2022): Jurnal Rekayasa Sistem & Industri
Publisher : School of Industrial and System Engineering, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jrsi.v9i01.514

Abstract

Pattern Recognition is a part research of Image Processing. In term implementation of Pattern Recognitionused for variety research needs such as Identify Transportation License Plate Number. Identifying licenseplate number is one process to reduce crime, accident, and support related agencies to process informationtraffic data. Taking picture of license plate number by camera create image that can be processed by OpticalCharacter Recognition (OCR). OCR is conversion process of picture that contain alphabet and numericobject into ASCII character. In creating of OCR, there’s many kind of algorithm, we used TemplateMatching algorithm as main research. Process of Template Matching algorithm is fitting all part of picturetaken by camera with image that has been processed into Template Data. However like another algorithm,this algorithm had a deficiency about accuracy and time to recognition process. To create and enhance ofaccuracy in Template Matching algorithm, we create 3 scenarios to take a picture for compare the result ofaccuracy and time to OCR process also comparing Template Matching algorithm with Learning VectorQuantization (LVQ) as accuracy comparison method. Based on result of Template Matching algorithm had84% accuracy and 0,942 Milliseconds to process OCR.
Co-Authors Husni Mubarok Husni Mubarok Mikail, Ali Astra Muhamad Asep Ridwan Purwayoga, Vega R Moch Dysa Satria Anggara Rianto Rianto, Rianto Ridwan, Muhamad Asep Yumna, Irsalina