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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Teknologi Infomasi, Komunikasi dan Elektronika (JTIKE)
Aini, Lyla Ruslana
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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.
Expertise Retrieval Using Adjusted TF-IDF and Keyword Mapping to ACM Classification Terms Aini, Lyla Ruslana; Evi Yulianti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6397

Abstract

In an era of collaboration, knowing someone's expertise is becoming increasingly necessary. Recognizing individuals' proficiency can be challenging because it requires considerable manual time. This study explores the expertise of lecturers from the Computer Science Department, Universitas Indonesia (Fasilkom UI), based on scientific publications. The data were obtained from the Sinta journal website’s scrapping process, which includes Scopus, Garuda, and Google Scholar data sources. The approach used was keyword extraction using the adjusted TF-IDF. The resulting keywords were then mapped to the ACM classification class using cosine similarity calculations with various embedding models, including BERT, BERT multilingual, FastText, XLM Roberta, and SBERT. The experimental results highlighted that combining the adjusted TF-IDF with mapping to the ACM classes using SBERT is a promising approach for gaining the best expertise. The use of abstract data has proved to be better than that of full-text data. Using title-abstract-EN data achieved a score of 0.49 for both the P@1 and NDCG@1 metrics, whereas the title-abstract-ENID data attained a score of 0.75 for both metrics P@1 and NDCG@1.
Co-Authors Ayuningtyas, Fara Elvira Nurfadhilah EVI YULIANTI GUNARSO GUNARSO Gunawan, Made Jarin, Asril Laksono Trisnantoro Latief, Andi Djalal Prafitia, Harnum Annisa Puspita, Gita Citra Santosa, Agung Uliniansyah, Mohammad Teduh