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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan Teknologi dan Kejuruan: Jurnal teknologi, Kejuruan dan Pengajarannya Jurnal Pendidikan Sains Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan JOIV : International Journal on Informatics Visualization VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro PEMBELAJAR: Jurnal Ilmu Pendidikan, Keguruan, dan Pembelajaran Pendas : Jurnah Ilmiah Pendidikan Dasar ILKOM Jurnal Ilmiah SENTIA 2016 Buletin Ilmiah Sarjana Teknik Elektro Letters in Information Technology Education (LITE) Jurnal Graha Pengabdian Ideguru: Jurnal Karya Ilmiah Guru Science in Information Technology Letters International Journal of Engineering, Science and Information Technology Bulletin of Community Engagement Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Integrasi dan Harmoni Inovatif Ilmu-ilmu Sosial Jurnal Inovasi Teknologi dan Edukasi Teknik ABDI UNISAP: Jurnal Pengabdian Kepada Masyarakat
Wahyu Sakti Gunawan Irianto
Universitas Negeri Malang
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Journal : Buletin Ilmiah Sarjana Teknik Elektro

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Improving Indonesian Sign Alphabet Recognition for Assistive Learning Robots Using Gamma-Corrected MobileNetV2 Hayati, Lilis Nur; Handayani, Anik Nur; Irianto, Wahyu Sakti Gunawan; Asmara, Rosa Andrie; Indra, Dolly; Damanhuri, Nor Salwa
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13300

Abstract

Sign language recognition plays a critical role in promoting inclusive education, particularly for deaf children in Indonesia. However, many existing systems struggle with real-time performance and sensitivity to lighting variations, limiting their applicability in real-world settings. This study addresses these issues by optimizing a BISINDO (Bahasa Isyarat Indonesia) alphabet recognition system using the SSD MobileNetV2 architecture, enhanced with gamma correction as a luminance normalization technique. The research contribution is the integration of gamma correction preprocessing with SSD MobileNetV2, tailored for BISINDO and implemented on a low-cost assistive robot platform. This approach aims to improve robustness under diverse lighting conditions while maintaining real-time capability without the use of specialized sensors or wearables. The proposed method involves data collection, image augmentation, gamma correction (γ = 1.2, 1.5, and 2.0), and training using the SSD MobileNetV2 FPNLite 320x320 model. The dataset consists of 1,820 original images expanded to 5,096 via augmentation, with 26 BISINDO alphabet classes. The system was evaluated under indoor and outdoor conditions. Experimental results showed significant improvements with gamma correction. Indoor accuracy increased from 94.47% to 97.33%, precision from 91.30% to 95.23%, and recall from 97.87% to 99.57%. Outdoor accuracy improved from 93.80% to 97.30%, with precision rising from 90.33% to 94.73%, and recall reaching 100%. In conclusion, the proposed system offers a reliable, real-time solution for BISINDO recognition in low-resource educational environments. Future work includes the recognition of two-handed gestures and integration with natural language processing for enhanced contextual understanding.
Bi-LSTM and Attention-based Approach for Lip-To-Speech Synthesis in Low-Resource Languages: A Case Study on Bahasa Indonesia Setyaningsih, Eka Rahayu; Handayani, Anik Nur; Irianto, Wahyu Sakti Gunawan; Kristian, Yosi
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14310

Abstract

Lip-to-speech synthesis enables the transformation of visual information, particularly lip movements, into intelligible speech. This technology has gained increasing attention due to its potential in assistive communication for individuals with speech impairments, audio restoration in cases of missing or corrupted speech signals, and enhancement of communication quality in noisy or bandwidth-limited environments. However, research on low-resource languages, such as Bahasa Indonesia, remains limited, primarily due to the absence of suitable corpora and the unique phonetic structures of the language. To address this challenge, this study employs the LUMINA dataset, a purpose-built Indonesian audio-visual corpus comprising 14 speakers with diverse syllabic coverage. The main contribution of this work is the design and evaluation of an Attention-Augmented Bi-LSTM Multimodal Autoencoder, implemented as a two-stage parallel pipeline: (1) an audio autoencoder trained to learn compact latent representations from Mel-spectrograms, and (2) a visual encoder based on EfficientNetV2-S integrated with Bi-LSTM and multi-head attention to predict these latent features from silent video sequences. The experimental evaluation yields promising yet constrained results. Objective metrics yielded maximum scores of PESQ 1.465, STOI 0.7445, and ESTOI 0.5099, which are considerably lower than those of state-of-the-art English systems (PESQ > 2.5, STOI > 0.85), indicating that intelligibility remains a challenge. However, subjective evaluation using Mean Opinion Score (MOS) demonstrates consistent improvements: while baseline LSTM models achieve only 1.7–2.5, the Bi-LSTM with 8-head attention attains 3.3–4.0, with the highest ratings observed in female multi-speaker scenarios. These findings confirm that Bi-LSTM with attention improves over conventional baselines and generalizes better in multi-speaker contexts. The study establishes a first baseline for lip-to-speech synthesis in Bahasa Indonesia and underscores the importance of larger datasets and advanced modeling strategies to further enhance intelligibility and robustness in low-resource language settings.
Co-Authors Abbar, Habib Muhammad Abdul Hadi, Afif Agro Lukman Putra Aguwin Ardi Pranata Ahmad Fuadi Ahsan, Muhammad Zamani Aji Prasetya Wibawa Al-Jabbar, Habib Muhammad Amrullah, Muhammad Shalahuddin Ananda Putri Syaviri Anik Nur Handayani Anusua Ghosh, Anusua Artina Tri Wistiawati Ashshiddiqi, Dimas Jundan Asmoro, Achmad Shoddiq Bayu Budi Wibowotomo Damanhuri, Nor Salwa Didik Dwi Prasetya Dila Umnia Soraya Dolly Indra Eka Rahayu Setyaningsih Erna Daniati Evania Kurniawati Fahreza, M. Dimas Aviv Falah, Moh. Zainul Fitri, Anisa Hilya Hakkun Elmunsyah Haqiqi, Ahmad Faiz Risvan Hasriani Hermansyah Heru Wahyu Herwanto I Made Wirawan Isyatul Karimah Joumil Aidil Saifuddin Khamdan, Candra Wahyu Nur Kirana, Karika Candra Krisnawan, Shandy Leonel Hernandez, Leonel lilis nurhayati M. Rodhi Faiz, M. Rodhi Milenia, Herpri Mohammad Husein An Nabawi Muhammad Afnan Habibi Muhammad Jauharul Fuady Mungalim, Rifqy Nugroho, Andhik Catur Nugroho, Bagas Putra, Adhi Pramana Estiawanda Rina Dewi Indahsari Rizka Afdalia Rosa Andrie Asmara Saifuddin, Farikh Saputra, Ismed Eko Hadi Sari, Gilang Rafiqa Setiadi Cahyono Putro Setyaningsih, Eka Rahayu Siti Sendari Slamet Wibawanto Sujito Sujito Syaad Patmanthara Syah, Abdullah Iskandar Triyanna Widiyaningtyas Ulum, Khoirul Utomo Pujianto Veithzal Rivai Zainal Wardhana, Nyoman Dedi Kusuma Widayanti, Erma Wistiawati, Artina Tri Yosi Kristian Yuni Rahmawati