Identifikasi Daging Ayam Kampung Segar dan Basi Menggunakan Metode Learning Vector Quantization

Authors

  • Dennis Feliawan Aji Universitas Mercu Buana Yogyakarta
  • Indah Susilawati Universitas Mercu Buana Yogyakarta

Keywords:

citra daging ayam kampung, Learning Vector Quantization, matriks co-occurrence

Abstract

Kampong chicken meat is meat obtained from kampong chicken. kampong chicken meat is considered expensive because they take longer time to grow up, a lot of people cheat by selling stale kampong chicken meat. The characteristics used to identify the meat’s image are homogeneity, contrast, average and variants. The number of data used in this research consists of two classes, each class has 30 image data, the total data is 60 training data. Whereas for test data, each class used 20 test data with a total of 40 test data. During the training process using LVQ parameters, there were 2 best percentages of 90%, namely on alpha 0.001 with a dec alpha of 0.2 and alpha 0.01 with a dec alpha of 0.9. The identification performed using the final weight from alpha 0.01 and dec alpha 0.9 had an 90% accuracy level with 4 iterations. The best performance from 40 test data using this software was with alpha 0.01 and dec alpha 0.9, which reached 90%.

Downloads

Download data is not yet available.

References

Afriandi, E., & Sutikno. (2016). Identifikasi Telapak Tangan Menggunakan Jaringan Syaraf Tiruan Learning Vector Quantization (LVQ). Jurnal Infotel, Vol. 8, No.2, ISSN : 2085-3688, Hal. 107-114.
Arifin, J., & Naf’an, M. Z. (2017). Verifikasi Tanda Tangan Asli Atau Palsu Berdasarkan Sifat Keacakan (Entropi). Jurnal Infotel, Vol.9, No.1, ISSN : 2085-3688, Hal. 130-135.
Health, R. b. (2004). Havelarr & Zweitering .
Hermawan. (2006). Jaringan Syaraf Tiruan Teori dan Aplikasi. Yogyakarta.
Jasril, Cahyana, M. S., Handayani, L., & Budianita, E. (2015). Implementasi Learning Vektor Quantization (LVQ) dalam Mengidentifikasi Citra Daging Babi dan Daging Sapi. Seminar Nasional Teknologi Informasi, Komunikasi dan Industri (SNTIKI) 7, ISSN : 2085-9902, Hal. 176-184.
Jasril, Handayani, L., Budianita, E., & Amri, F. U. (2017). Implementasi Metode Segmentasi dan LVQ Untuk Identifikasi Citra Daging Sapi dan Babi. Seminar Nasional Teknologi Informasi, Komunikasi dan Industri (SNTIKI) 9(2579-7271), 283-292.
Kiswanto, K., Sediyono, E., & Suhartono, S. (2014, Januari). Identifikasi Citra Untuk Mengidentifikasi Jenis Daging Sapi Menggunakan Transformasi Wavelet Haar. JSINBIS (Jurnal Sistem Informasi Bisnis), 1, 73-79.
Kusumadewi, S. (2003). Artificial Intelligence : Teknik dan Aplikasinya. Yogyakarta.
Lihayati, N., Pawening, R. E., & Furqan, M. (2016). Klasifikasi Jenis Daging Berdasarkan Tekstur Menggunakan Metode Gray Level Coocurent Matrix. 8(2085-2347), A-305 - A-310.
Purnama, A. (2016). Jaringan Syaraf Tiruan (Neural Network).
Puspaningrum, D. (2006). Pengantar Jaringan Syaraf Tiruan. Yogyakarta.
Qur’ani, D. Y., & Rosmalinda, S. (2010). Jaringan Syaraf Tiruan Learning Vector Quantization Untuk Aplikasi Pengenalan Tanda Tangan. Seminar Nasional Aplikasi Teknologi Informasi 2010, ISSN: 1907-5022, Hal. 6-10.
Sing, J. J. (2004). Jaringan Syaraf Tiruan Pemrogramannya Menggunakan MATLAB.
Sutoyo, T. (2009). Teori Pengolahan Citra Digital.

Published

2020-08-31