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Mirza, A Haidar
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Computer Science & IT

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PENGEMBANGAN KONSEP E-GOVERNMENT BERBASIS CLOUD COMPUTING PADA KABUPATEN BANYUASIN Saputra, Benny Wilson; Antoni, Darius; Firdaus, Firdaus; Mirza, A Haidar
POSITIF : Jurnal Sistem dan Teknologi Informasi Vol 6 No 2 (2020): Positif : Jurnal Sistem dan Teknologi Informasi
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/positif.v6i2.901

Abstract

Layanan yang berkualitas kepada masyarakat merupakan keharusan yang harus dipenuhi oleh pemerintahan saat ini. Terbukti dengan semakin banyaknya program e-government yang sudah jalankan oleh pemerintah Indonesia. Tetapi ini bukan merupakan hal yang mudah dilakukan oleh pemerintahan daerah untuk melaksanakan e-government sepenuhnya. Seperti pada pemerintah Kabupaten Banyuasin yang dalam mengembangkan layanan e-government masih banyak menghadapi tantangan, diantaranya adalah keterbatasan sumber daya manusia, infrastruktur yang belum memadai, kesulitan dalam migrasi pelayanan, integrasi, manajemen sofware dan hardware serta hal-hal lain yang sering menyebabkan kegagalan dalam pengembangan e-government yang berkualitas. Salah Satu Solusi yang dapat dilakukan untuk mengatasi masalah tersebut adalah dengan menerapkan Teknologi Cloud Computing yang dapat mendukung layanan e-government Kabupaten Banyuasin. Kata Kunci : e-Government, Cloud Computing, Kabupaten Banyuasin
Implementasi Deep Learning CNN untuk Menerjemahkan Sistem Isyarat Bahasa Indonesia (SIBI) ke Teks Pramuda, Tintou; Mirza, A Haidar
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10371

Abstract

Communication is a fundamental aspect of human life. However, individuals with hearing and speech impairments often face barriers in communicating with the general public. The Indonesian Sign System (SIBI) serves as a communication solution for the deaf and speech-impaired community in Indonesia, yet public understanding of SIBI remains limited. To address this issue, this study aims to develop an automatic translation model from SIBI sign language into Indonesian text by utilizing Deep Learning technology, specifically the Convolutional Neural Network (CNN) algorithm. CNN was chosen for its ability to effectively recognize visual patterns, making it suitable for processing hand gesture images in sign language. This research involved collecting and classifying a dataset of hand images based on the alphabet or words in SIBI, which were then used to train the CNN model. The designed CNN model was built to accurately classify hand signs and translate them into Indonesian text. The results of this study have the potential to serve as a supportive solution for inclusive communication between the deaf community and the wider public, and can be further developed for contextual sentence translation. Keywords: Indonesian Sign System (SIBI), CNN, Deep Learning, Automatic Translation, Inclusive Communication
Co-Authors Antoni, Darius Firdaus Firdaus Pramuda, Tintou Saputra, Benny Wilson