Detection of fake shallots using website-based haar-like features algorithm

Abstract

Shallots is commonly used as essential cooking spices or complement seasoning. The high market demand for this commodity has triggered some people to counterfeit it. They mix the shallots with defective products of onions to get more benefits. It urges to provide a system that can help people to distinguish whether the shallot is original or fake. This research aims to provides an object recognition system for fake shallots utilizing the Haar-Like Feature algorithm. It used the cascade training data set of 59 positive images and 150 negative images with 50 comparison images. The identification process of the shallots was through the haar-cascade process, integrated image, adaptive boosting, cascade classifier, and local binary pattern histogram. This system was made based on the Django website using the python programming language. The test was conducted 30 times on Brebes shallots mixed with Mumbai's mini onions in a single and mixture test method. The test obtained an average percentage of 69.2% for the object recognition of Mumbai's mini onions.

Keywords

Fake shallots; Fake shallots detection; Haar –like feature; Object recognition

Full Text:

PDF

References

H. B. Pratomo, “Pemerintah beri tips kenali bawang merah palsu.,” https://www.merdeka.com, Jun. 22, 2018. [Online]. Available: https://www.merdeka.com/uang/pemerintah-beri-tips-kenali-bawang-merah-palsu.html

T Sutoyo dkk, TEORI PENGOLAHAN CITRA DIGITAL. Yogyakarta: Andi Offset, 2009.

A. Agusriandi, “IdentifikasiBawang Merah dan Bombay dengan PendekatanRadial Basis Function Neural Network (RBFNN),” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. Vol 4 No 4, pp. 1043–1050, Oktober 2020, doi: 10.30865/mib.v4i4.2334.

S. Abidin, “Deteksi Wajah Menggunakan Metode Haar Cascade Classifier Berbasis Webcam Pada Matlab,” ELEKTERIKA, vol. 15, no. 1, p. 21, May 2018, doi: 10.31963/elekterika.v15i1.2102.

N. D. Mega Anjani, F. Farida, and M. Kurniawan, “ANALISIS FITUR HAAR MENGGUNAKAN ALGORITMA HAAR-LIKE FEATURE PADA CITRA KENDARAAN BERMOTOR,” NERO, vol. 5, no. 2, p. 124, Dec. 2020, doi: 10.21107/nero.v5i2.187.

M. Zulfikri, E. Yudaningtyas, and R. Rahmadwati, “Sistem Penegakan Speed Bump Berdasarkan Kecepatan Kendaraan yang Diklasifikasikan Haar Cascade Classifier,” Jurnal Teknologi dan Sistem Komputer, vol. 7, no. 1, pp. 12–18, Jan. 2019, doi: 10.14710/jtsiskom.7.1.2019.12-18.

S. Al-Aidid and D. Pamungkas, “Sistem Pengenalan Wajah dengan Algoritma Haar Cascade dan Local Binary Pattern Histogram,” JRE, vol. 14, no. 1, pp. 62–67, Apr. 2018, doi: 10.17529/jre.v14i1.9799.

S. Chau, J. Banjarnahor, D. Irfansyah, and S. Kumala, “Analisis Pendeteksian Pola Wajah Menggunakan Metode Haar-Like Feature,” JITE, vol. 2, no. 2, p. 69, Jan. 2019, doi: 10.31289/jite.v2i2.2133.

R. E. Putri, T. Matulatan, and N. Hayaty, “Sistem Deteksi Wajah Pada Kamera Realtime dengan menggunakan Metode Viola Jones,” sustainable, vol. 8, no. 1, pp. 30–37, May 2019, doi: 10.31629/sustainable.v8i1.526.

D. A. Ayubi, D. A. Prasetya, and I. Mujahidin, “Pendeteksi Wajah Secara Real Time pada 2 Degree of Freedom (DOF) Kepala Robot Menggunakan Deep Integral Image Cascade,” CYCLOTRON, vol. 3, no. 1, Feb. 2020, doi: 10.30651/cl.v3i1.4306.

R. E. Schapire and Y. Freund, Boosting: foundations and algorithms. Cambridge, MA: MIT Press, 2012.

R. Laganière, OpenCV 3 computer vision application programming cookbook: recipes to help you build computer vision applications that make the most of the popular C++ library Open CV 3, Third edition. Birmingham Mumbai: Packt, 2017.

http://dx.doi.org/10.28989/compiler.v10i2.978

Article Metrics

Abstract view: 455 times
Download     : 208   times

This work is licensed under a Creative Commons Attribution 4.0 International License.

Refbacks

• There are currently no refbacks.