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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SEINASI-KESI
Helena Nurramdhani Irmanda
UPN Veteran Jakarta
Computer Science & IT Engineering

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Penerapan Convolutional Neural Networks untuk Mesin Penerjemah Bahasa Daerah Minangkabau Berbasis Gambar Mayanda Mega Santoni; Nurul Chamidah; Desta Sandya Prasvita; Helena Nurramdhani Irmanda; Ria Astriratma; Reza Amarta Prayoga
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (407.35 KB) | DOI: 10.29207/resti.v5i6.3614

Abstract

One of efforts by the Indonesian people to defend the country is to preserve and to maintain the regional languages. The current era of modernity makes the regional language image become old-fashioned, so that most them are no longer spoken. If it is ignored, then there will be a cultural identity crisis that causes regional languages to be vulnerable to extinction. Technological developments can be used as a way to preserve regional languages. Digital image-based artificial intelligence technology using machine learning methods such as machine translation can be used to answer the problems. This research will use Deep Learning method, namely Convolutional Neural Networks (CNN). Data of this research were 1300 alphabetic images, 5000 text images and 200 vocabularies of Minangkabau regional language. Alphabetic image data is used for the formation of the CNN classification model. This model is used for text image recognition, the results of which will be translated into regional languages. The accuracy of the CNN model is 98.97%, while the accuracy for text image recognition (OCR) is 50.72%. This low accuracy is due to the failure of segmentation on the letters i and j. However, the translation accuracy increases after the implementation of the Leveinstan Distance algorithm which can correct text classification errors, with an accuracy value of 75.78%. Therefore, this research has succeeded in implementing the Convolutional Neural Networks (CNN) method in identifying text in text images and the Leveinstan Distance method in translating Indonesian text into regional language texts.
Sistem Informasi Arsip Surat Masuk Dan Surat Keluar di Kecamatan Cibodas Tania Suzanne Narissa; Helena Nurramdhani Irmanda
PROSIDING SEINASI-KESI Vol 1, No 1 (2022): SEMINAR NASIONAL INFORMATIKA, SISTEM INFORMASI, DAN KEAMANAN SIBER
Publisher : Fakultas Ilmu Komputer UPN Veteran Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penyimpanan arsip surat masuk dan surat keluar di suatu instansi sangat diperlukan sistem informasi yang dapat dengan efisien, tepat, dan cepat untuk menyortir surat-surat tersebut. Sistem ini sangat tepat digunakan oleh instansi yang masih menggunakan sistem manual sampai saat ini, salah satunya adalah Kecamatan Cibodas. Penyimpanan arsip surat yang masih dilakukan secara manual akan memakan waktu lebih banyak terutama dalam pencarian surat. Penelitian ini dilakukan dengan metode waterfall yaitu metode yang pengimplementasiannya dilakukan melalui pendekatan secara sistematis serta berurutan. Penggambaran rancangan sistem yang digunakan yaitu dengan Unified Modelling Language (UML). Sistem Informasi Arsip Surat ini merupakan sistem informasi berbasis website yang dibangun menggunakan PHP sebagai bahasa pemogramannya dan MySQL (phpMyAdmin) sebagai database penyimpanannya. Dibangunnya sistem informasi ini akan menjadikan penyimpanan arsip surat lebih efisien ketika digunakan.
Co-Authors Desta Sandya Prasvita Nurul Chamidah Reza Amarta Prayoga Reza Amarta Prayoga Ria Astriratma Santoni, Mayanda Mega Tania Suzanne Narissa