Article Per Year (5 Year)
p-Index From 2019 - 2024
1.857
P-Index
This Author published in this journals
All Journal International Journal of Evaluation and Research in Education (IJERE) JIPFRI (Jurnal Inovasi Pendidikan Fisika dan Riset Ilmiah) JIPF (Jurnal Ilmu Pendidikan Fisika) STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Repositor Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) JRKT (Jurnal Rekayasa Komputasi Terapan) Kapas: Kumpulan Artikel Pengabdian Masyarakat
Prasetya, Rendi
Universitas Indraprasta PGRI
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Mathematics Physics Social Sciences Other

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : STRING (Satuan Tulisan Riset dan Inovasi Teknologi)

 1 
Implementasi Jaringan Syaraf Tiruan Backpropagation untuk Memprediksi Ketinggian Air (Studi Kasus: Sungai Ciliwung) Rendi Prasetya
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 1, No 3 (2017)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (827.21 KB) | DOI: 10.30998/string.v1i3.1558

Abstract

Prediction water level is still done using statistical methods. However, problems will arise if the research done on dynamic systems, as well as the water level prediction system. Artificial neural network technology can properly identify data patterns of a dynamic system. This study was conducted to predict the height of water in Bogor using rainfall data, evaporation and water level, observations in 2009-2010. The design of the prediction model using neural networks backpropagation with MATLAB software. Characteristics of artificial neural networks used are: 1 input layer with two neurons (precipitation and evaporation), one hidden layer and one output layer (water level), the value of the learning rate of 0.9; momentum of 0.1; 3 hidden neurons, error tolerance of 0.0001 and a maximum of 10000 epoch The experimental results by looping earned 10 times the average error was 10.93%, and it can be concluded that the system can properly predict the height of the water
Co-Authors Ariando Damanik Dian Nazelliana Diana Diana Fajar Eri Irianto Fery Rahmawan Asma Herlinda Herlinda Irnin Agustina Dwi Astuti Irnin Agustina Dwi Astuti Isnaini Muthiah Norma Pravitasari Retna Ningsih Rismawati, Nofita Rudi Prasetya Siti Marti'ah Susanah Susanah VH Valentino Wildan Wildan Yoga Budi Bhakti Yulika Rahmawati Za'imatun Niswati Zulherman