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All Journal Jurnal Informatika Journal of Information Systems and Informatics Journal of Robotics and Control (JRC)
Andik Wijanarko
Universitas Amikom Purwokerto
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Penerapan Model View Controller untuk Membangun Aplikasi Data Dasar Keluarga Desa Somagede Andik Wijanarko; Adam Prayogo Kuncoro; Naufal Dzaki Wuryantoro
Jurnal Informatika Vol 8, No 1 (2021): April 2021
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (322.409 KB) | DOI: 10.31294/ji.v8i1.8662

Abstract

Saat ini teknologi sudah banyak digunakan di berbagai sektor kehidupan, baik itu dibidang ekonomi, pendidikan, dan pemerintahan. Dengan perkembangan teknologi yang sangat pesat, instansi-instansi pemerintah dituntut untuk menerapkan sebuah pengolahan data yang terkompeterisasi sehingga dapat memberikan informasi yang tepat dan akurat. Salah satu contoh instansi pemerintahan adalah pemerintah desa. Pemerintahan Desa adalah penyelenggaraan urusan pemerintahan dan kepentingan masyarakat setempat dalam sistem pemerintahan Negara Kesatuan Republik Indonesia. Setiap desa memiliki catatan penduduk dan juga data dasar keluarga. Balai desa Somagede sudah memiliki sistem informasi pendataan data dasar keluarga desa Somagede. Pada saat petugas menggunakan sistem informasi pendataan data dasar keluarga Somagede, petugas mempunyai beberapa kendala yaitu, jaringan wifi di kantor desa tidak selalu ada, yang menyebabkan petugas merasa kesulitan untuk menginputkan data dasar keluarga Somagede dan menyajikan data dasar keluarga desa Somagede, kepada orang yang membutuhkan data dasar keluarga desa Somagede. Tujuan dari penelitian ini adalah untuk mengembangkan aplikasi pendataan data data dasar keluarga Somagede yang dapat diakses mempermudah petugas desa untuk melakukan pendataan data data dasar keluarga desa Somagede, tujuan lainnya adalah mengembangkan database aplikasi pendataan data dasar keluarga Somagede dari yang sederhana menjadi kompleks. Metode pengembangan perangkat lunak yang digunakan pada penelitian ini adalah extreme programming dengan pengujian menggunakan blackbox testing. Hasil dari aplikasi pendataan data dasar keluarga berbasis website ini adalah dapat memudahkan petugas dalam melakukan pendataan data dasar keluarga Somagede.
SMART-In English: Learn English Using Speech Recognition Dhanar Intan Surya Saputra; Sitaresmi Wahyu Handani; Kuat Indartono; Andik Wijanarko
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

Abstract

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

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

Abstract

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

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

Abstract

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%.
Co-Authors Adam Prayogo Kuncoro Adzkiyatun Nisa Al Haura Dhanar Intan Surya Saputra Indar Puspitaningrum Jusuf Haurissa Kuat Indartono Naufal Dzaki Wuryantoro Rizkial Achmad Sitaresmi Wahyu Handani Yokelin Tokoro