Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Gusti Reza Maulana
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Pembelajaran Mesin untuk Pembuatan Dokumen Karya Sastra Indonesia Secara Otomatis menggunakan Metode Modified Long Short Term Memory (LSTM) berbasis Extreme Learning Machine (ELM) Gusti Reza Maulana; Imam Cholissodin; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 12 (2022): Desember 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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

Abstract

Documents as important and make it easy for human work. one type of document is a literary work. Literary work is a series of writings or an idea and thought conveyed with aesthetic purposes. The literary works used in this research are words of wisdom, poetry and rhymes. All this time making literary works are written manually. This is why many of these literary works took too long to produce. The focus of this research is to apply a literary work made from the program so that it does not take a long time to make it. This study uses a Modified Long Short Term Memory algorithm based on Extreme Learning Machine for the creation of the literary work. The process of making the literary work is preprocessing, dividing training data and test data, training phase, testing phase, calculating MAPE and calculating the accuracy of the BLEU Score. In testing the number of context neurons and hidden neurons, optimal results were obtained, namely as many as 5 and 800 for the three literary works. Calculation using the BLEU Score with a value of N = 4 produces an average accuracy for words of wisdom 74.66%, poetry of 14.49% and rhymes of 73.76%.
Co-Authors Bayu Rahayudi Imam Cholissodin