Klasifikasi Bobot Telur Ayam Ras menggunakan Visi Komputer dan Segmentasi Citra

Supiyandi Supiyandi; Warda Hamidah; Nazwa Alya Faradita; Arizka Anggraini; Adisty Maysandra

doi:10.61132/neptunus.v3i1.586

Authors

Supiyandi Supiyandi Universitas Panca Budi
Warda Hamidah UIN Sumatera Utara
Nazwa Alya Faradita UIN Sumatera Utara
Arizka Anggraini UIN Sumatera Utara
Adisty Maysandra UIN Sumatera Utara

DOI:

https://doi.org/10.61132/neptunus.v3i1.586

Keywords:

Computer Vision, Segmentation, Chicken Eggs, Classification, RGB

Abstract

This study aims to classify chicken eggs based on their physical size using the concept of computer vision and image segmentation techniques. Compared to the standard methods that have been used so far, this alternative technology is expected to help standardize measurements, cost efficiency, and work effectiveness. In this study, the classification of chicken eggs was carried out using image segmentation and regression analysis. Thus, it is expected that the classification of chicken eggs will have increasingly accurate values. After the image is taken using a webcam, the image segmentation process is used to divide the image into homogeneous areas based on the RGB (true color) color intensity similarity standard. Regression analysis is used to study and measure the relationship between the number of pixels and the weight of the object. The number of pixels indicating the area of the object is the result of image segmentation, which will be entered into the regression equation to calculate the weight (grams). The results showed that the color characteristics of chicken eggs have a normalization of R at least 0.41 and a normalization of G at least 0.3. In addition, the classification test has an accuracy of 100% (36/36) and a weight estimation accuracy of 42 percent (15/36).

References

Ahmad, U. (2019). Pengolahan citra digital & teknik pemrogramannya (Ed. 1). Graha Ilmu.

Andono, P. N. (2021). Konsep pengolahan citra digital (Ed. 1). Andi.

Badan Standarisasi Nasional. (2022). Standar Nasional Indonesia nomor 3926-2008 tentang telur ayam konsumsi. Badan Standarisasi Nasional.

Basuki, A., Palandi, J. F., & Fatchurrochman. (2022). Pengolahan citra menggunakan Visual Basic. Graha Ilmu.

Darmawan, M. W. (2019). Identifikasi mutu buah mangga Arum Manis berdasarkan warna menggunakan image processing dan JST. Jurusan Teknik Elektro Universitas Gadjah Mada.

Hadi, S. (2019). Pemanfaatan informasi warna kulit sebagai metode pra-pemrosesan untuk mendukung pendeteksian wajah. Departemen Informatika Institut Teknologi Bandung.

Harinaldi. (2022). Prinsip-prinsip statistik untuk teknik dan sains. Erlangga.

Khabibulloh, M. A., Kusumawardhani, A., Pratama, D. Y. (2021). Rancang bangun sistem deteksi embrio pada telur menggunakan webcam. Jurnal Teknik Pomits, 1(1), 1–6.

Ruslianto, I. (2023). Klasifikasi telur ayam dan telur burung puyuh menggunakan metode connected component analysis. Jurnal Ilmiah SISFOTENIKA, 3(1), 41–50.

Sugiyono. (2019). Statistika untuk penelitian. Alfabeta.

Sunardi, Y. A., & Saifullah, S. (2019). Thermal imaging untuk identifikasi telur. In Prosiding Konferensi Nasional Ke-4, Asosiasi Program Pascasarjana Perguruan Tinggi Muhammadiyah (APPPTM), 152–157.

Suprijatna. (2022). Ilmu dasar ternak unggas. Penebar Swadaya.

Trisnaningtyas, P. R., & Maimunah. (2019). Klasifikasi mutu telur berdasarkan kebersihan kerabang telur menggunakan K-Nearest Neighbor. In Konferensi Nasional Informatika (KNIF). Nurhayati, O. D. (2019). Sistem analisis tekstur secara statistik orde pertama untuk mengenali jenis telur ayam biasa dan telur ayam omega-3. Jurnal Sistem Komputer, 5(2), 79–82. https://doi.org/10.2087/4685

Utami, Y. R. A. (2022). Pengenalan telur berdasarkan karakteristik warna citra. Jurnal Ilmiah SINUS, 7(2), 1–14. https://doi.org/10.1693/1173

Wijaya, T. A., Yudi, P., & Prayudi, Y. (2020). Implementasi visi komputer dan segmentasi citra untuk klasifikasi bobot telur ayam ras. Seminar Nasional Aplikasi Teknologi Informasi, G1-G5. https://doi.org/10.1907/5022