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All Journal Jurnal Teknologi dan Manajemen Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) Technomedia Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Capture : Jurnal Seni Media Rekam Building of Informatics, Technology and Science TEPIAN Jurnal Teknik Informatika (JUTIF) Business Economic, Communication, and Social Sciences Journal (BECOSS) Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Jurnal Pendidikan dan Teknologi Indonesia Jurnal Locus Penelitian dan Pengabdian Jurnal Indonesia : Manajemen Informatika dan Komunikasi The Indonesian Journal of Computer Science
Santoso, Handri
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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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LSTM-Based Hand Gesture Recognition for Indonesian Sign Language System (SIBI) on Affix, Alphabet, Number, and Word Ho, Patricia; Santoso, Handri
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9607

Abstract

Sign language plays a critical role in enabling communication for the Deaf and hard-of-hearing community in Indonesia, yet there remains a significant gap in technological support for recognizing the official Indonesian sign language, Sistem Isyarat Bahasa Indonesia (SIBI). This study presents a deep learning-based hand gesture recognition system for SIBI, focusing on four primary gesture categories: affix, alphabet, number, and word. A large and diverse dataset of 21,351 videos was collected, covering 18 affix, 26 alphabet, 35 number, and 29 word classes. Hand keypoints were extracted using MediaPipe Holistic, and a bidirectional long short-term memory (BiLSTM) model was trained using 5-fold stratified cross-validation. The model achieved high recognition performance in the alphabet, number, and word categories, with mean test accuracies of 93.94%, 91.48%, and 92.41%, respectively, and slightly lower performance in the affix category at 68.17%. The affix category posed particular challenges due to subtle hand shape differences and high variability between signers, while the alphabet category consistently showed the highest accuracy due to its distinct and static handshapes. Evaluation metrics, including precision, recall, F1-score, and confusion matrix analysis, provided further insights into model strengths and limitations. Overall, the study demonstrates the effectiveness of LSTM models for sequential hand gesture recognition in SIBI and highlights areas for future improvement, such as handling non-manual features and improving generalization across signers.
Co-Authors Akbar, Achmad Fathurrazi Akbariani, Nurhaliza Vania Amanda, Djaja Andrew Andrew, Andrew Aprilia, Novia Pramesti Dazki, Erick Devanda, Faustin Eko Hadianto, Eko Fransisca, Viviana Harlis Setiyowati HARYONO Heni Jusuf Hindarto, Djarot Ho, Patricia Indrajit, Eko ISWAHYUDI ISWAHYUDI Karjadi, Daniel Avian Karjadi, Markus Laurenso, Justin Lijaya, Hendra Lucia Sri Istiyowati Lupitha, Mariska Mareta, Arvin Maria Magdalena Mohammad, Saiful Azhari Perdana, Nanda Praharto, Suwandhy Prawira, Khairul Thamrin Priswanto, Budi Purwadi Purwadi Putri, Gladys Rahman, Ben Ramadhoni, Moehammad Richardus Eko Indrajit Rochadiani, Theresia Herlina Ruddin, Isra Sakti, Irwin Sandjaya, Julio Santoso, Billy Cahyo Saputra, Adi Dwifana Sari, Muh Masri Sentosa, Steve Suhandi, Vartin Suherman, Endang Sumargo, Ruly Sze, Edward Wedha, Alessandro Enriqco Putra Bayu Wedha, bayu Yasa Widjaja, Herman