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Hitipeuw, Emanuel
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Hand image reading approach method to Indonesian Language Signing System (SIBI) using neural network and multi layer perseptron Bagaskoro, Muhammad Cahyo; Prasojo, Fadillah; Handayani, Anik Nur; Hitipeuw, Emanuel; Wibawa, Aji Prasetya; Liang, Yoeh Wen
Science in Information Technology Letters Vol 4, No 2 (2023): November 2023
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v4i2.1362

Abstract

Classification complexity is the main challenge in recognizing sign language through the use of computer vision to classify Indonesian Sign Language (SIBI) images automatically. It aims to facilitate communication between deaf or mute and non-deaf individuals, with the potential to increase social inclusion and accessibility for the disabled community. The comparison of algorithm performance in this research is between the neural network algorithm and multi-layer perceptron classification in letter recognition. This research uses two methods, namely a neural network and a multi-layer perceptron, to measure accuracy and precision in letter pattern recognition, which is expected to provide a foundation for the development of better sign language recognition technology in the future. The dataset used consists of 32,850 digital images of SIBI letters converted into alphabetic sign language parameters, which represent active signs. The developed system produces alphabet class labels and probabilities, which can be used as a reference for the development of more sophisticated sign language recognition models. In testing using the neural network method, good discrimination results were obtained with precision, recall and accuracy of around ±81%, while in testing using the multi-layer perceptron method around ±86%, showing the applicative potential of both methods in the context of sign language recognition. Testing of the two normalization methods was carried out four times with comparison of the normalized data, which can provide further insight into the effectiveness and reliability of the normalization technique in improving the performance of sign language recognition systems.
Decision tree based algorithms for Indonesian Language Sign System (SIBI) recognition Nugraha, Agil Zaidan; Salsabila, Reni Fatrisna; Handayani, Anik Nur; Wibawa, Aji Prasetya; Hitipeuw, Emanuel; Arai, Kohei
Applied Engineering and Technology Vol 3, No 2 (2024): August 2024
Publisher : ASCEE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/aet.v3i2.1536

Abstract

Indonesian Sign Language System (SIBI) recognition plays a crucial role in improving effective communication for individuals with hearing loss in Indonesia. To support automatic SIBI recognition, this research presents a performance analysis of two main algorithms, namely Decision Tree and C4.5, in the context of the SIBI recognition task. This research utilizes a rich SIBI dataset that includes a variety of SIBI signs used in everyday communication. Data pre-processing, model construction with both algorithms, and model performance evaluation using accuracy, precision, recall, and F1-score metrics are all part of the study. Regarding SIBI recognition accuracy, the experimental results demonstrate that the Decision Tree performs better than Decision Tree. The Decision Tree also makes models that are easier to understand, which is important for making communication systems based on SIBI.
Co-Authors Aji Prasetya Wibawa Anik Nur Handayani Bagaskoro, Muhammad Cahyo Kohei Arai Liang, Yoeh Wen Nugraha, Agil Zaidan Prasojo, Fadillah Salsabila, Reni Fatrisna