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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknologi Infomasi, Komunikasi dan Elektronika (JTIKE) Aiti: Jurnal Teknologi Informasi
Uliniansyah, Mohammad Teduh
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Computer Science & IT Engineering

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RANCANGAN APLIKASI ANDROID PENERJEMAH WICARA KE WICARA DENGAN KOMUNIKASI DUA ARAH Santosa, Agung; Jarin, Asril; Aini, Lyla Ruslana; Ayuningtyas, Fara; Gunarso, Gunarso; Gunawan, Made; Uliniansyah, Mohammad Teduh; Latief, Andi Djalal; Puspita, Gita Citra; Nurfadhilah, Elvira; Prafitia, Harnum Annisa
Jurnal Teknologi Infomasi, Komunikasi dan Elektronika (JTIKE) Vol 1, No 1 (2018)
Publisher : Badan Pengkajian dan Penerapan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (915.273 KB) | DOI: 10.29122/jtike.v1i1.3282

Abstract

Dengan ketersediaan sumber daya kebahasaan dan sistem Pengolahan Bahasa Alami yang sudah dikembangkan sebelumnya, kegiatan-kegiatan kerekayasaan Teknologi Bahasa BPPT melakukan pengembangan sebuah aplikasi penerjemah wicara-ke-wicara untuk dua Bahasa (Bahasa Indonesia dan Bahasa Inggris) yang memanfaatkan layanan dari server pengenal wicara, mesin penerjemah, dan sintesis wicara. Aplikasi ini dikenal sebagai speech-to-speech translation (S2ST). Di makalah ini, kami deskripsikan rancangan aplikasi S2ST tersebut dengan fokus pengembangan pada aplikasi mobile android yang dapat melayani percakapan antara dua pengguna. Teknik-teknik yang diterapkan antara lain adalah WebSocket, RESTful service, JSON, dan OkHttp3.Keywords:  Penerjemah wicara ke wicara, S2ST, NLP, ASR, MT, TTS, WebSocket, RESTful Service.
Automatic speech recognition for Indonesian medical dictation in cloud environment Jarin, Asril; Santosa, Agung; Uliniansyah, Mohammad Teduh; Aini, Lyla Ruslana; Nurfadhilah, Elvira; Gunarso, Gunarso
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1762-1772

Abstract

This paper introduces SPWPM, an automatic speech recognition (ASR) system designed specifically for Indonesian medical dictation. The main objective of SPWPM is to assist medical professionals in producing medical reports and diagnosing patients. Deployed within a cloud computing service architecture, SPWPM strives to achieve a minimum speech recognition accuracy of 95%. The ASR model of SPWPM is developed using Kaldi and PyChain technologies—creating a comprehensive training dataset involving collaboration with PT Dua-Empat-Tujuh and Harapan Kita Hospital. Several optimization techniques were applied, including language modeling with smoothing, lexicon generation using the Grapheme-to-Phoneme Converter, and data augmentation. The readiness of this technology to assist hospital users was assessed through two evaluations: the SPWPM architecture test and the SPWPM speech recognition test. The results demonstrate the system's preparedness in accurately transcribing medical dictation, showcasing its potential to enhance medical reporting for healthcare professionals in hospital environments.
Pengembangan model akustik dengan deep neural network untuk sistem pengenalan wicara bahasa Indonesia Gunarso, Gunarso; Buono, Agus; Mushthofa, Mushthofa; Uliniansyah, Mohammad Teduh
AITI Vol 22 No 1 (2025)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v22i1.84-100

Abstract

The Deep Neural Network (DNN)-based approach offers significantly higher accuracy compared to traditional methods such as Hidden Markov Model (HMM)-Gaussian Mixture Model (GMM) in acoustic model development. In this research, three popular DNN variants were evaluated: Time-Delay Neural Network (TDNN), Long Short-Term Memory (LSTM), and a hybrid combination of TDNN-LSTM for acoustic model development in Indonesian speech recognition. Using the KDW-BPPT-50K-ASR1 speech data for over 92 hours, acoustic models were trained, and experiments were conducted to analyze their performance. Research results show that the hybrid TDNN-LSTM model achieved the best performance with a Word Error Rate (WER) of 9.67%, outperforming TDNN with a WER of 12.16% and LSTM with a WER of 10.6%. This finding confirms that the hybrid model is able to improve the accuracy of Indonesian speech recognition compared to using TDNN or LSTM separately. These results provide a significant contribution to the development of more accurate and efficient speech recognition systems.
Modeling sentiment analysis of Indonesian biodiversity policy Tweets using IndoBERTweet Uliniansyah, Mohammad Teduh; Jarin, Asril; Santosa, Agung; Gunarso, Gunarso
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2389-2401

Abstract

This study develops and evaluates a sentiment analysis model using IndoBERTweet to analyze Twitter data on Indonesia’s biodiversity policy. Twitter data focusing on topics such as food security, health, and environmental management were collected, with a representative subset of 13,435 tweets annotated from a larger dataset of 500,000 to ensure reliable sentiment labels through majority voting. IndoBERTweet was compared to seven traditional machine-learning classifiers using TF-IDF and BERT embeddings for feature extraction. Model performance was assessed using mean accuracy, mean F1 score, and statistical significance (p-values). Additionally, sentiment analysis included word attribution techniques with BERT embeddings, enhancing relevance, interpretability, and consistent attribution to deliver accurate insights. IndoBERTweet models consistently outperformed traditional methods in both accuracy and F1 score. While BERT embeddings boosted performance for conventional models, IndoBERTweet delivered superior results, with p-values below 0.05 confirming statistical significance. This approach demonstrates that the model’s outputs are explainable and align with human understanding. Findings underscore IndoBERTweet’s substantial impact on advancing sentiment analysis technology, showcasing its potential to drive innovation and elevate practices in the field.
Co-Authors Agus Buono Aini, Lyla Ruslana Ayuningtyas, Fara Elvira Nurfadhilah GUNARSO GUNARSO Gunawan, Made Jarin, Asril Laksono Trisnantoro Latief, Andi Djalal Prafitia, Harnum Annisa Puspita, Gita Citra RR. Ella Evrita Hestiandari Santosa, Agung