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All Journal Elkom: Jurnal Elektronika dan Komputer Jurnal Elementer (Elektro dan Mesin Terapan)
I Gusti Agung Made Yoga Mahaputra
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Education Electrical & Electronics Engineering Engineering Mechanical Engineering

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Rancang Bangun Penerjemah BISINDO Real-time Berbasis Kamera dan Deep Learning dengan Kendali Suara ESP32 WiFi I Gusti Agung Made Yoga Mahaputra; Putri Alit Widyastuti Santiary; I Ketut Swardika
Jurnal ELEMENTER (Elektro dan Mesin Terapan) Vol 11 No 1 (2025): Jurnal Elektro dan Mesin Terapan (ELEMENTER)
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/elementer.v11i1.6578

Abstract

Indonesian Sign Language (BISINDO) serves as the primary means of communication for the deaf community. However, limited public understanding and the lack of practical real-time translation technology remain significant barriers to effective two-way communication. Most prior research has focused on foreign sign languages or relied on sensor-based gloves, which are less flexible for everyday use. This study proposes a real-time BISINDO translation system that converts hand gestures into speech using a camera and an ESP32 microcontroller. The system employs a CNN-LSTM deep learning model implemented in Python to classify gestures representing letters A to J, then wirelessly transmits the classification results to the ESP32, which triggers the corresponding audio output. A custom gesture dataset was collected and enhanced through preprocessing and data augmentation to support model training. Evaluation results demonstrate a classification accuracy of 91.4%, with a precision of 89.7%, recall of 90.5%, and F1-score of 89.9%. The average communication latency was recorded at 3.1 seconds, and the speech output success rate reached 86.7%. The system has proven reliable for real-time automatic gesture-to-speech translation and holds potential for further development as an inclusive communication aid for individuals with hearing impairments in Indonesia. This study serves as an initial foundation for future advancements in assistive communication technologies.
Analisis Temporal Gerakan Kata BISINDO Menggunakan Landmark Tangan dan LSTM dengan Keluaran Suara Berbasis ESP32 Secara Real-time I Gusti Agung Made Yoga Mahaputra; Putri Alit Widyastuti Santiary; I Ketut Swardika
Elkom: Jurnal Elektronika dan Komputer Vol. 18 No. 2 (2025): Desember : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v18i2.3258

Abstract

Indonesian Sign Language (BISINDO) serves as a primary communication medium for the deaf community; however, limited public understanding often creates barriers during daily interactions. This study aims to develop a real-time BISINDO word-level translation system using hand landmark extraction and temporal modeling with Long Short-Term Memory (LSTM). The system employs MediaPipe Hands to detect 21 hand landmarks per frame, which are then processed as sequential motion patterns to classify five BISINDO words: saya, terima kasih, maaf, nama, and kamu. A total of 250 gesture samples were recorded under controlled lighting conditions as the primary dataset. The processed sequences were used to train the LSTM model, which was subsequently integrated with an ESP32 microcontroller and a DFPlayer Mini module to produce direct audio output. Experimental results show that the model achieved an average accuracy of 86%, with precision and recall values ranging from 0.81 to 0.94. The confusion matrix analysis indicates that most gestures were correctly classified, although some errors occurred in gestures with similar initial motion trajectories. Integration testing demonstrated an average system latency of 3.8 seconds and an audio output success rate of 85%. These findings indicate that the proposed system is capable of translating BISINDO word-level gestures accurately, responsively, and consistently in real-time conditions. This study provides a strong foundation for the broader development of sign language translation systems, with potential enhancements in vocabulary expansion, multi-user datasets, and hardware optimization for deployment in real-world environments.
Co-Authors I Ketut Swardika Ida Bagus Alit Swamadika Lie Jasa Putri Alit Widyastuti Santiary Rukmi Sari Hartati