IMAGERY IDENTIFICATION OF TOMATOES WHICH CONTAIN PESTICIDES USING LEARNING VECTOR QUANTIZATION

  • Ade Sumarsono Informatika, Fakultas Teknologi Informasi, Universitas Mercu Buana Yogyakarta, Indonesia
  • Supatman Informatika, Fakultas Teknologi Informasi, Universitas Mercu Buana Yogyakarta, Indonesia
Keywords: histogram, learning vector quantization (LVQ), tomatoes

Abstract

Tomatoes have a risk of carrying pesticides above the maximum residue limit (MRL) because the fruit is directly sprayed with pesticides during its production process. Pesticide residue in farmers’ produce pose indirect effects to the consumers, but in the long run, it may cause health problems such as neural disorders as well as enzyme metabolism. This research identifies the image of tomatoes containing pesticides by using two types of tomatoes were used as samples, namely tomatoes which contain pesticides, and those which do not contain pesticides. This research aims to develop an algorithm to identify tomatoes that contain pesticides and those which do not contain pesticides using Learning Vector Quantization (LVQ). The characteristics used to identify tomato images are average, variant, and standard deviation. This research consisted of two classes and used 40 training image data and 40 test image data for each class. During the training process using LVQ parameters, there were 98.75% best percentage at alpha 0.001 and decalpha 0.9 with the lowest iteration of 3. The final weight obtained from the parameters was then used to perform test data identification. In terms of the best performance on the test data, it was with alpha 0.001 and decalpha 0.9, which reached 97.5%.

Downloads

Download data is not yet available.

References

P. Utomo, “Sistem Klasifikasi Jenis Beras Menggunakan Metode Learning Vector Quantization,” Jurnal Ilmiah Ilmu - ilmu Teknik, vol. I, pp 61-67, 2016.

D. Harjunowibowo, “Perangkat Lunak Deteksi Uang Palsu Berbasis LVQ Memanfaatkan Ultraviolet”, Seminar Nasional Pendidikan Biologi FKIP UNS, 2010, pp. 342-352.

Jasril, L. Handayani, E. Budianita, & F. U. Amri, “Implementasi Metode Segmentasi dan LVQ untuk Identifikasi Citra Daging Sapi dan Babi,” Seminar Nasional Teknologi Informasi, Komunikasi dan Industri (SNTIKI), 2017, pp. 283-292.

I. Fathurrahman, A. M. Nur, & Fathurrahman. “Identifikasi Kematangan Buah Mentimun Berbasis Citra Digital Menggunakan Jaringan Syaraf Tiruan Backpropagation,” Infotek : Jurnal Informatika dan Teknologi, pp. 27-33, 2019.

M. Effendi, Fitriyah, & U. Effendi, “Identifikasi Jenis Dan Mutu Teh Menggunakan Pengolahan Citra Digital dengan Metode Jaringan Syaraf Tiruan,” Jurnal Teknotan, vol. 11, no. 2, pp. 67-76, 2017.

A. M. Arymurthy, Pengantar Pengolahan Citra. Jakarta: Elex Media Komputindo. 1991.

R. Munir, Pengolahan Citra Digital Dengan Pendekatan Algoritmik. Bandung: Informatika, 2004.

T. Sutoyo, Teori Pengolahan Citra Digital. Yogyakarta: Andi Publisher, 2009.

E. Kartika, R. Yusuf, & A. Syakur, “Pertumbuhan dan Hasil Tanaman Tomat (Lycopersicum esculentum Mill.) Pada Berbagai Persentase Naungan,” e-J. Agrotekbis, pp. 717-724, 2015.

U. Yuliana, R. N. Whidhiasih, & Maimunah, “Identifikasi Rasa Buah Mangga Gedong Gincu Cirebon Berdasarkan Citra Red-Green-Blue Menggunakan Jaringan Syaraf Tiruan,” Jurnal Penelitian Ilmu Komputer, System Embedded & Logic, pp. 61-68, 2016.

R. Hamidi, M. T. Furqon, & B. Rahayudi, “Implementasi Learning Vector Quantization (LVQ) untuk Klasifikasi Kualitas Air Sungai,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. I, pp. 1758-1763, 2017.

R. R. Jordy, R. Magdalena, & L. Novamizanti, “Klasifikasi Motif Batik Solo Menggunakan Histogram of Oriented Gradient Dan Learning Vector Quantization,” e-Proceeding of Engineering, 2018, V, pp. 5079-5085.

D. Y. Qur’ani, & S. Rosmalinda, “Jaringan syaraf tiruan learning vector quantization untuk aplikasi pengenalan tanda tangan,” Seminar Nasional Aplikasi Teknologi Informasi, 2010, pp. 6-10.

A. S. Romadhon, & V. T. Widyaningrum, “Klasifikasi Mutu Jeruk Nipis dengan Metode Learning Vector Quantization (LVQ),” Jurnal Ilmiah Rekayasa, pp. 121-128, 2015.

U. Sudibyo, D. P. Kusumaningrum, E. H. Rachmawanto, & C. A. Sari, “Optimasi Algoritma Learning Vector Quantization (LVQ) Dalam Pengklasifikasian Citra Daging Sapi dan Daging Babi Berbasis GLCM dan HSV,” Jurnal SIMETRIS, vol. IX, pp. 1-10, 2018.

N. D. Miranda, L. Novamizanti, & S. Rizal, "Convolutional Neural Network Pada Klasifikasi Sidik Jari Menggunakan Resnet-50", Jurnal Teknik Informatika (Jutif), vol. 1, no. 2, 2020.

Published
2021-01-13
How to Cite
[1]
A. Sumarsono and S. Supatman, “IMAGERY IDENTIFICATION OF TOMATOES WHICH CONTAIN PESTICIDES USING LEARNING VECTOR QUANTIZATION”, J. Tek. Inform. (JUTIF), vol. 2, no. 1, pp. 9-16, Jan. 2021.