PENERAPAN ALGORITMA BACKPROPAGATION UNTUK MENGENALI POLA TULISAN ANGKA DENGAN FUNGSI PELATIHAN GRADIENT DESCENT WITH MOMENTUM ADAPTIVE LR

Putrama Alkhairi(1*), B. Herawan Hayadi(2),

(1) Universitas Potensi Utama
(2) Universitas Potensi Utama
(*) Corresponding Author

Abstract


This system can be used for the main capability of the system is number plate recognition. In this study, the Momentum Backpropagation Neural Network method will be used to recognize the character of an image of a vehicle number plate in Indonesia. But before that, the number plate image will be converted into a binary image. The binary image is then segmented to isolate the characters to be recognized. Finally, the dimensions of the segmented image will be reduced using Haar Wavelet. One of the weaknesses of computers compared to humans is recognizing character patterns if they do not use support methods. Artificial Neural Network (ANN) is a method or concept that takes the human nervous system. In the ANN system, there are several methods used to conduct training on computers that are made, training is used to increase the accuracy or ability of computers to recognize patterns. One of the ANN algorithms used to train system data is backpropagation. With the Artificial Neural Network (ANN) method, the algorithm can produce a system that can recognize handwritten numeric character patterns that can make it easier for humans to do pattern recognition. The results of the testing process using the Backpropagation algorithm reached 95% with a total of 40 trained data. The test results from the test data reached 90% of the 40 test data.

Full Text:

PDF

References


R. A. Purba, S. Samsir, M. Siddik, S. Sondang, And M. F. Nasir, “The Optimalization Of Backpropagation Neural Networks To Simplify Decision Making,” Iop Conf. Ser. Mater. Sci. Eng., Vol. 830, No. 2, 2020, Doi: 10.1088/1757-899x/830/2/022091.

F. A. Ruslan, A. M. Samad, And R. Adnan, “4 Hours Nnarx Flood Prediction Model Using ‘Traingd’ And ‘Trainoss’ Training Function: A Comparative Study,” Proc. - 2018 Ieee 14th Int. Colloq. Signal Process. Its Appl. Cspa 2018, No. March, Pp. 77–81, 2018, Doi: 10.1109/Cspa.2018.8368689.

W. Budiharto And D. Suhartono, Artificial Intelligence Konsep Dan Penerapannya, Andi Yogya. Yogyakarta, 2014.

Y. Nuraeni, “Penerapan Jaringan Syaraf Tiruan Untuk Mengukur Tingkat Korelasi Antara Nem Dengan Ipk Kelulusan Mahasiswa,” Telkomnika (Telecommunication Comput. Electron. Control., Vol. 7, No. 3, P. 195, 2009, Doi: 10.12928/Telkomnika.V7i3.594.

T. Dalgleish Et Al., Jaringan Syaraf Tiruan (Neural Network), Vol. 136, No. 1. 2014.

R. N. Putri, D. Setiawan, And E. Mufadli, “Analisa Korelasi Peningkatan Jumlah Penelitian Dengan Tingkat Beban Kerja Dosen Menggunakan Metode Backpropagation,” 2016.

A. T. Solikhun, M. Safii, “Jaringan Saraf Tiruan Untuk Memprediksi Tingkat Pemahaman Sisiwa Terhadap Matapelajaran Dengan Menggunakan Algoritma Backpropagation Solikhun,” J. Sains Komput. Inform., Vol. 1, No. 1, Pp. 24–36, 2017.

X. Yan, Y. Xu, X. Xing, B. Cui, Z. Guo, And T. Guo, “Trustworthy Network Anomaly Detection Based On An Adaptive Learning Rate And Momentum In Iiot,” Ieee Trans. Ind. Informatics, Vol. 16, No. 9, Pp. 6182–6192, 2020, Doi: 10.1109/Tii.2020.2975227.

Z. Xu, A. M. Dai, J. Kemp, And L. Metz, “Learning An Adaptive Learning Rate Schedule,” Arxiv, Vol. 1909.09712, 2019.

Agus Perdana Windarto, “Implementasi Jst Dalam Menentukan Kelayakan Nasabah Pinjaman Kur Pada Bank Mandiri Mikro Serbelawan Dengan Metode Backpropogation,” J-Sakti (Jurnal Sains Komput. Dan Inform., Vol. 1, No. 1, Pp. 12–23, 2017.

A. P. Windarto, L. S. Dewi, And D. Hartama, “Implementation Of Artificial Intelligence In Predicting The Value Of Indonesian Oil And Gas Exports With Bp Algorithm,” Int. J. Recent Trends Eng. Res., Vol. 3, No. 10, Pp. 1–12, 2017, Doi: 10.23883/Ijrter.2017.3482.J5bbs.

Z. A. Matodang, “Jaringan Syaraf Tiruan Dengan Algoritma Backpropagtion Untuk Penentuan Kelulusan Sidang Skripsi,” Pelita Inform. Budi Darma, Vol. 4, No. 1, Pp. 84–93, 2013.

Y. A. Lesnussa, S. Latuconsina, And E. R. Persulessy, “Aplikasi Jaringan Saraf Tiruan Backpropagation Untuk Memprediksi Prestasi Siswa Sma ( Studi Kasus : Prediksi Prestasi Siswa Sman 4 Ambon ),” J. Mat. Integr., Vol. 11, No. 2, Pp. 149–160, 2015.

N. M. Sukarno, P. W. Wirawan, And S. Adhy, “Perancangan Dan Implementasi Jaringan Saraf Tiruan,” Vol. 5, Pp. 9–18, 2013.

L. Handayani And M. Adri, “Penerapan Jst ( Backpropagation ) Untuk Prediksi Curah Hujan ( Studi Kasus : Kota Pekanbaru ),” No. November, Pp. 238–247, 2015.


Refbacks

  • There are currently no refbacks.


 

SmartAI: Buletin artificial intelligence
Online ISSN: 2828-1144
Organized by Yayasan Adwitiya Basurata Inovasi
Published by Yayasan Adwitiya Basurata Inovasi
W: https://ejournal.abivasi.id/index.php/SmartAI

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0

Published Papers Indexed/Abstracted By: