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SMART-In English: Learn English Using Speech Recognition Saputra, Dhanar Intan Surya; Handani, Sitaresmi Wahyu; Indartono, Kuat; Wijanarko, Andik
Journal of Robotics and Control (JRC) Vol 1, No 4 (2020): July
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.1423

English is an international language and important to learn. For someone learning English sometimes is a difficulty, especially in pronunciation. Therefore, SMART-In is a prototype of Android App that uses Speech Recognition technology by utilizing services from the Cloud Speech API (Application Programming Interface). SMART-In English can be used as an alternative to English learning, especially in the pronunciation of a word. Using speech recognition can display the score of the pronunciation spoken by the user, recorded, show a level the pronunciation of the word and display the correct pronunciation.

IoT-Based Smart Air Conditioner as a Preventive in the Post-COVID-19 Era: A Review Saputra, Dhanar Intan Surya; Suarnatha, I Putu Dody; Mahardika, Fajar; Wijanarko, Andik; Handani, Sitaresmi Wahyu
Journal of Robotics and Control (JRC) Vol 4, No 1 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i1.17090

The Internet of Things (IoT) refers to physical objects with sensors, computing power, software, and other technologies that communicate and exchange data with other devices, platforms, and systems over the Internet or other communication networks. Remarkable developments in IoT have paved the way for new possibilities, enabling the creation and automation of innovative services and advanced applications and constituting a collection of crucial enabling technologies for smart homes. In this New-Normal Era, the concept of an IoT-based Smart Air Conditioner (AC) as a Preventive Effort against COVID-19 is a proposed innovation and application. The Smart AC is designed based on the analysis of existing problems and is equipped with literature obtained in the study. The purpose of this study is to review the research literature on IoT-enabled Smart AC to emphasize the main trends and open problems of integrating IoT technology to create sustainable and efficient Smart homes. The IoT-based Smart AC was designed and equipped with air quality filter features, human sensors, temperature control, voice control, Cloud Storage, and Solar Panel services that can be controlled via smartphone devices. From the framework and study results, the IoT offers many benefits. The IoT-based Smart AC concept is one step ahead of existing AC products.

Model Recurrent Neural Network-Gated Recurrent Unit untuk Membangun Mesin Penerjemah Bahasa Indonesia-Banyumasan Wijanarko, Andik; Haura, Adzkiyatun Nisa Al
Jurnal Eksplora Informatika Vol 13 No 2 (2024): Jurnal Eksplora Informatika
Publisher : Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v13i2.977

Bahasa Banyumasan berakar dari bahasa Jawa dengan dialek Banyumasan, dituturkan di bagian barat Jawa Tengah dan sebagian bagian timur provinsi Jawa Barat. Undang-undang mengungkapkan perlunya upaya pelestarian bahasa daerah. Salah satu upayanya adalah membangun mesin penerjemah bahasa Indonesia-Banyumasan. Model yang digunakan adalah Recurrent Neural Network yang digunakan untuk membangun mesin penerjemah beberapa bahasa daerah di Indonesia, tapi belum pernah digunakan untuk bahasa Indonesia-Banyumasan, khususnya Gated Reccurent Unit. Tujuan penelitian ini membangun mesin penerjemah bahasa Indonesia-Banyumasan dan mengukur kualitas terjemahannya. Metode yang digunakan adalah eksperimen mulai dari pembuatan korpus paralel yang dilanjutkan melakukan training korpus menggunakan, dan langkah terakhir adalah melakukan evaluasi menggunakan metrik Bilingual Evaluation Understudy. Korpus paralel yang digunakan berisi 1.302 kalimat dengan panjang kalimat rata-rata 20 kata perkalimat. Waktu training yang diperlukan adalah 72 jam. Skor metrik yang dihasilkan adalah 34.1 yang berarti model tersebut dan paralel korpus menghasilkan kualitas terjemahan yang masih dapat ditingkatkan.

Recurrent Neural Network-Gated Recurrent Unit for Indonesia-Sentani Papua Machine Translation Achmad, Rizkial; Tokoro, Yokelin; Haurissa, Jusuf; Wijanarko, Andik
Journal of Information System and Informatics Vol 5 No 4 (2023): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i4.597

The Papuan Sentani language is spoken in the city of Jayapura, Papua. The law states the need to preserve regional languages. One of them is by building an Indonesian-Sentani Papua translation machine. The problem is how to build a translation machine and what model to choose in doing so. The model chosen is Recurrent Neural Network – Gated Recurrent Units (RNN-GRU) which has been widely used to build regional languages in Indonesia. The method used is an experiment starting from creating a parallel corpus, followed by corpus training using the RNN-GRU model, and the final step is conducting an evaluation using Bilingual Evaluation Understudy (BLEU) to find out the score. The parallel corpus used contains 281 sentences, each sentence has an average length of 8 words. The training time required is 3 hours without using a GPU. The result of this research was that a fairly good BLEU score was obtained, namely 35.3, which means that the RNN-GRU model and parallel corpus produced sufficient translation quality and could still be improved.

cARica: enhancing travelling experiences in wonosobo through location-based mobile augmented reality Saputra, Dhanar Intan Surya; Murjiatiningsih, Lilis; Hermawan, Hellik; Handani, Sitaresmi Wahyu; Wijanarko, Andik
Journal of Soft Computing Exploration Vol. 4 No. 1 (2023): March 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i1.97

Wonosobo, as a Regency in Central Java Province, Indonesia, has attractions including the Dieng Plateau Theater Kalianget, and Menjer Lake. The research is intended to provide more experience for tourists who visit the tour through Location-Based Mobile Augmented Reality (MAR), an application we developed, cARica. This application includes experience travelling in Wonosobo and is aware of other information displayed through AR content. It was an alternative medium for tourism promotion to be easy, attractive, and inexpensive. It is a practical guide to attract tourists to visit tourist sites. In its development, we use the prototyping method so that each stage is carried out under the procedures that have been prepared. To get the point of Interest (PoI) points of tourist sites, use Global Positioning System (GPS) data taken through Google Maps to get the Latitude and Longitude of each object. The results of this study present that cARica is a Location-Based Mobile Augmented Reality service platform that can be accessed using an Android smartphone and has three-dimensional animated character content with the Wonosobo regency icon. cARica is a form of innovation in providing exceptional services and experiences for tourists and has the potential to be continuously developed.

Comparison of Conversational Corpus and News Corpus on Gender Bias in Indonesian-English Transformer Model Translation Wijanarko, Andik; Al Haura, Adzkiyatun Nisa; Puspitaningrum, Indar; Saputra, Dhanar Intan Surya
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.918

Gender bias in machine translation is a significant issue that affects text translation and gender perception, often leading to misunderstandings, such as the tendency to default to using male pronouns. For example, the word "dia" in Indonesian is often translated as "he" rather than "she," even when the context suggests otherwise, as seen in the case of President Megawati. Reducing this bias requires ongoing research, particularly in understanding how different corpora affect translation accuracy. Studies have shown that formal news corpora, which have less gender bias, produce different results compared to conversational corpora that are more informal and exhibit gender bias. This research uses a training dataset of the Indonesian-English conversational parallel corpus from Open Subtitles, which contains many gendered pronouns. Additionally, a news corpus from Tanzil, with fewer gendered words, was also used. These corpora were sourced from Opus, widely used by previous researchers. For the testing dataset, biographies of female presidents were used, which are often translated as masculine by popular machine translation systems by default. Each corpus was trained using a Transformer model, resulting in a translation model. Each sentence from the generated translations was then detected for gender, and compared with the gender of sentences from the test data to evaluate accuracy. The results showed that the accuracy of gender translation from the conversational corpus was 84%, while the news corpus achieved an accuracy of 8%.

Model Recurrent Neural Network-Gated Recurrent Unit untuk Membangun Mesin Penerjemah Bahasa Indonesia-Banyumasan Wijanarko, Andik; Haura, Adzkiyatun Nisa Al
Eksplora Informatika Vol 13 No 2 (2024): Jurnal Eksplora Informatika
Publisher : Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v13i2.977

Bahasa Banyumasan berakar dari bahasa Jawa dengan dialek Banyumasan, dituturkan di bagian barat Jawa Tengah dan sebagian bagian timur provinsi Jawa Barat. Undang-undang mengungkapkan perlunya upaya pelestarian bahasa daerah. Salah satu upayanya adalah membangun mesin penerjemah bahasa Indonesia-Banyumasan. Model yang digunakan adalah Recurrent Neural Network yang digunakan untuk membangun mesin penerjemah beberapa bahasa daerah di Indonesia, tapi belum pernah digunakan untuk bahasa Indonesia-Banyumasan, khususnya Gated Reccurent Unit. Tujuan penelitian ini membangun mesin penerjemah bahasa Indonesia-Banyumasan dan mengukur kualitas terjemahannya. Metode yang digunakan adalah eksperimen mulai dari pembuatan korpus paralel yang dilanjutkan melakukan training korpus menggunakan, dan langkah terakhir adalah melakukan evaluasi menggunakan metrik Bilingual Evaluation Understudy. Korpus paralel yang digunakan berisi 1.302 kalimat dengan panjang kalimat rata-rata 20 kata perkalimat. Waktu training yang diperlukan adalah 72 jam. Skor metrik yang dihasilkan adalah 34.1 yang berarti model tersebut dan paralel korpus menghasilkan kualitas terjemahan yang masih dapat ditingkatkan.

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