Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Komputasi
Renada Dhea Armelia
Universitas Lampung
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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

Found 1 Documents
Search

 1 
Perbandingan Kinerja Backpropagation dan Convolutional Neural Network untuk Klasifikasi Citra Batik Lampung Renada Dhea Armelia; Rico Andrian; Akmal Junaidi
Jurnal Komputasi Vol. 12 No. 1 (2024): Jurnal Komputasi
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v12i1.248

Abstract

one of Indonesia's cultures on October 2, 2009. Lampung initially did not have a batik tradition but there is a legacy that is referred to as the first batik worn by Lampung people, namely sembagi cloth. Batik Siger is a business that produces batik typical of Lampung which originated from a course and training institution and was established in 2008. LKP Batik Siger provides services to the community in the field of written batik. This research discusses the performance of backpropagation and convolutional neural networks that will be used for the classification of Lampung batik image patterns. The Lampung batik motifs used are sembagi, pakjimo, granitan, soga, siger tangkup betik, jung agung, kembang cengkih and siger ratu agung. The stages that will be carried out are scaling, grayscale, thresholding and classification. The comparison of training data, testing data and validation used is 70:20:10 with the needs of backpropagation and convolutional neural network, namely epoch = 100, learning rate = 0.01. Backpropagation classification resulted in an accuracy of 96.25% and a classification error of 3.75%. The convolutional neural network classification resulted in an accuracy of 99.37% and a classification error of 0.63%. The performance of the CNN method has 3.12% higher accuracy compared to the performance of convolutional neural network.
Co-Authors Akmal Junaidi Andrian, Rico