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Journal : Jurnal Teknik Informatika (JUTIF)

Deep Learning-Based Recognition of Indonesian Sign Language (BISINDO) Alphabetic Gestures Using Skeletal Feature Extraction and LSTM Afwan, Teuku M Arief; Gernowo, Rahmat; Wibawa, Helmie Arif
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5337

Abstract

Communication is a fundamental aspect of human life, and for the deaf community, sign language serves as the primary medium of interaction. In Indonesia, the Indonesian Sign Language (BISINDO) is widely used, however, research on automatic BISINDO recognition remains limited due to the scarcity of representative datasets. This study presents the development of a BISINDO recognition system based on deep learning by integrating the Long Short-Term Memory (LSTM) architecture with the MediaPipe Holistic framework. To address data limitations, a custom dataset comprising 866 BISINDO alphabetic gesture videos was collected, involving recordings from both expert and non-expert signers to capture stylistic variations. Extracted skeletal landmark features were processed through a three-layer LSTM network followed by dense layers for sequential modeling and classification. Experimental results show that the proposed model achieved a validation accuracy of approximately 93%, outperforming static image–based methods and demonstrating the effectiveness of skeletal features in representing dynamic gestures. The model also exhibited real-time applicability with promising performance, although challenges such as misclassification of visually similar gestures and dataset imbalance remain. This study contributes to the underexplored field of BISINDO recognition by providing a baseline system and dataset, and further advances the domains of computer vision and human–computer interaction within informatics through an inclusive, data-driven framework for Indonesian Sign Language recognition and future AI-assisted accessibility technologies.
Co-Authors Adi Wibowo Adiyono, Soni Afwan, Teuku M Arief Agus Setyawan Agus Sutejo Agusta Praba Ristadi Pinem Ahmad Lubis Ghozali Aldi Setiawan, Aldi Andryani, Ria Annisa Luthfianti Panular Ardima, Muhammad Basyier Arfriandi, Arief Ari Bawono Putranto Aria Hendrawan, Aria Aries Dwi Indriyanti, Aries Dwi Aris Sugiharto Atik Zilziana Muflihati Noor Bayong Tjasyono H. Kasih Bayu Surarso Beta Noranita Budi Prasetiyo, Budi Budi Warsito Budi Warsito Catur Edi Widodo Cholil, Saifur Rohman Christine Dewi D Febrianty Dafiz Adi Nugroho Dedy Kurniadi Edi Surya Negara Eko Nur Hidayat Eko Sediyono F M Arif Faliha Muthmainah Fauzan Ishlakhuddin Frysca Putti Muviana Ghufron Ghufron Gumay, Naretha Kawadha Pasemah Helmie Arif Wibawa Hengki Hengki Heri Mulyanti Hidayat, Agung Rahmad I. Istadi Ikhthison Mekongga Iryanto Iryanto Ismi Dian Kusumawardhani Isnain Gunadi Istadi I’tishom Al Khoiry Khusnah, Miftakhul Koesuma, Sorja Kuresih, Kuresih Kurnia Adi Cahyanto Kusworo Adi M. Solehuddin Mahrus Ali Michael Andreas Purwoadi Moh Ali Fikri Muchammad A Rofik Mulyani, Esti Munengsih Sari Bunga Munji Hanafi Nabiel Putra Adam, Nabiel Putra Novita Mariana Nuriyana Muthia Sani Nuriyana Muthia Sani Nursamsiah Nursamsiah Oky Dwi Nurhayati Prayitno R. Rizal Isnanto Radini Sinta, Radini Ratih Rundri Utami Rosyalia, Syofi Sakhina, Friska Ayu Setiabudi, Nur Andi Shahmirul Hafizullah Imanuddin Siti Yuniar Pangestu Slamet, Vincencius Gunawan Suryono Suryono Syibli, Mohammad Tri Mulyono Triyono, Liliek Victor Gayuh Utomo Wahyu Jatmiko Wahyul Amien Syafei Wicaksana, Hilman Singgih Widagdo, Krisan Aprian Widiyatmoko, Carolus Borromeus Wulandari, Rosita Ayu Yenny Ernitawati Zaenal Arifin