Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Journal of Electrical, Electronic, Information, and Communication Technology (JEEICT)
Ananda Putra Kanieza
Sebelas Maret University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Development of Sign Language Interpreter Glove for Speech-Impaired Deaf Individuals with Levenshtein Algorithm as an Autotext Correction System. Dea Muthia Febry; Ananda Putra Kanieza; Gilang Fajar Ramadhan; Velisa Nur Aini; Faisal Rahutomo
Journal of Electrical, Electronic, Information, and Communication Technology Vol 6, No 1 (2024): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jeeict.6.1.79652

Abstract

Assistive technology is technology developed in the form of aids, adaptive tools, and rehabilitation for individuals with disabilities to compensate for their lacking abilities. With assistive technology available to people with special needs, it can help them enhance their independent living skills and reduce their dependence on others, including in communication activities. This research aims to assess the feasibility of developing assistive technology for the speech-impaired, specifically a glove that can translate the SIBI letter sequence into words displayed on a 6 x 12 LCD screen and also produce sound output from the sign language hand movements of speech-impaired individuals when using the glove. The research method used in this study is Research and Development (R&D). The calibration test phase indicated that the prototype was in good condition, as evidenced by an accuracy rate above 90% for voltage at 0◦ and 90◦ angles produced by each finger. Consequently, the next calibration phase, which involves translating sensor readings into SIBI letters through digital data values, can be carried out by taking the ADC values of each finger. Subsequently, the glove was tested to read 7 out of 20 alphabets and achieved a success rate of ≥ 90% for 5 alphabets. The lowest success rate was 70% for the letter E. The average success rate for the 7 alphabet experiments was 91.4%. In the field test phase, the glove was tested on a deaf-mute student to form several words, and the output text displayed on the LCD and audio output matched the readings corrected by the auto-text correction system.
Real-time facial and body pose emotion recognition for children with autism based on YOLOv8 and LSTM Siti Nurohmahwati; Ananda Putra Kanieza; Ade Rifky Setiawan; Ahmad Fadlan
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1899-1912

Abstract

Children with autism spectrum disorder (ASD) often face challenges in recognizing and expressing emotions, which can affect their behavior and participation in inclusive classroom environments. This study proposes a real-time multimodal emotion recognition system integrating deep learning and Internet of Things (IoT) technologies to support early emotional monitoring in children with ASD. The framework combines YOLOv8 for facial expression detection and YOLOv8-based pose estimation for body movement analysis, along with a long short-term memory (LSTM) network for temporal emotion classification. At the facial level, the system recognizes five emotional states: sad, happy, neutral, boredom, and tantrum. At the temporal level, the LSTM model classifies behavioral sequences into three categories: neutral/bored, happy, and tantrum, enabling hierarchical emotion interpretation from instantaneous expressions to temporal patterns. Experimental results show that the facial expression model achieves 92% precision, while the LSTM-based classifier reaches 95% peak validation accuracy and 93.33% final test accuracy. The system is deployed on a web- based monitoring platform with real-time notifications for educators and parents. The proposed approach demonstrates effectiveness in providing timely emotional insights to support early intervention and improve inclusive education for children with ASD.
Co-Authors Ade Rifky Setiawan Ahmad Fadlan Dea Muthia Febry Faisal Rahutomo Gilang Fajar Ramadhan Siti Nurohmahwati Velisa Nur Aini