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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu Pertanian Indonesia Jurnal Teknologi Infomasi, Komunikasi dan Elektronika (JTIKE) Jurnal Linguistik Komputasional
Elvira Nurfadhilah
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Agriculture, Biological Sciences & Forestry 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)

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.
Uji Coba Korpus Data Wicara BPPT sebagai Data Latih Sistem Pengenalan Wicara Bahasa Indonesia Made Gunawan; Elvira Nurfadhilah; Lyla Ruslana Aini; M. Teduh Uliniansyah; Gunarso -; Agung Santosa; Juliati Junde
Jurnal Linguistik Komputasional Vol 1 No 2 (2018): Vol. 1, No. 2
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (555.048 KB) | DOI: 10.26418/jlk.v1i2.8

Abstract

Kami menyajikan hasil uji coba pengenalan wicara menggunakan Korpus Data Wicara BPPT yang dikembangkan tahun 2013 (KDW-BPPT-2013) dengan menggunakan anggaran DIPA tahun 2013. Korpus ini digunakan sebagai data latih dan data uji. Korpus ini berisi ujaran dari 200 pembicara yang terdiri dari 50 laki-laki dewasa, 50 laki-laki remaja, 50 perempuan dewasa, dan 50 perempuan remaja dengan masing-masing mengucapkan 250 kalimat. Total lama ujaran data wicara ini sekitar 92 jam. Uji coba dilakukan dengan menggunakan Kaldi dan menghasilkan Word Error Rate (WER) GMM 2,52 % dan DNN 1,64%.
Pemanfaatan Teknologi Tepat Guna Identifikasi Tumbuhan Obat Berbasis Citra Yeni Herdiyeni; Julio Adisantoso; Ellyn K Damayanti; Ervizal AM Zuhud; Elvira Nurfadhila; Kristina Paskianti
Jurnal Ilmu Pertanian Indonesia Vol. 18 No. 2 (2013): Jurnal Ilmu Pertanian Indonesia
Publisher : Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (575.323 KB)

Abstract

Indonesia is a mega biodiversity country including many kind medicinal plants. It is not easy to identify the various kinds of the medicinal plants especially for common people. Therefore, we need a computer-based automatic system as a tool to identify these various types of the medicinal plants. Developing of computer-based automatic system for medicinal plant identification has been done based on leaf image. There are 30 species of medicinal plants used in this study. There are 3 features for identification, i.e. morphology, texture, and shape. To improve the accuracy of identification we applied probabilistic neural network to classify the species of medicinal plant. The experiment results showed that the accuracy of identification increase to 74.67%. Developing of search engine has been done as well. We used 32 species of medicinal plant. The number of document was 132 documents. The document consists of name, family, description, diseases, and chemical substances. To improve the accuracy of searching, we applied KNN Fuzzy to classify document into 2 categories, i.e., family and diseases. The experiment results showed that the accuracy of average of precision is 96% for only word of length query and 89% for two words of length query. The system is very beneficial for people in society because it can be used to identify medicinal plants easily and the relevant communitis become independent in maintaining family health and giving opportunities as well as income of the people. Hence, the system is promising for leaf identification and supporting plant biodiversity in Indonesia.
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.
Co-Authors Agung Santosa Aini, Lyla Ruslana Aini, Lyla Ruslana Ayuningtyas, Fara Ellyn K Damayanti Ervizal AM Zuhud Gunarso - GUNARSO GUNARSO Gunawan, Made Jarin, Asril Jarin, Asril Juliati Junde Julio Adisantoso Kristina Paskianti Latief, Andi Djalal Lyla Ruslana Aini M. Teduh Uliniansyah Made Gunawan Prafitia, Harnum Annisa Puspita, Gita Citra Santosa, Agung Santosa, Agung Uliniansyah, Mohammad Teduh Uliniansyah, Mohammad Teduh