Ekstraksi Ciri Orde Pertama dan Metode Principal Component Analysis untuk Mengidentifikasi Jenis Telur Ayam Kampung dan Ayam Arab

*Oky Dwi Nurhayati orcid scopus  -  Universitas Diponegoro, Indonesia
Dania Eridani  -  Universitas Diponegoro, Indonesia
Ajik Ulinuha  -  Universitas Diponegoro, Indonesia
Received: 10 Jul 2019; Revised: 13 Sep 2019; Accepted: 18 Sep 2019; Published: 4 Nov 2019; Available online: 4 Nov 2019.
DOI: https://doi.org/10.21456/vol9iss2pp133-140 View
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Abstract

Chicken eggs become one of the animal proteins commonly used by people, especially in Indonesia. Eggs have high economic value and have diverse benefits and a high nutritional content. Visually to distinguish between domestic chicken eggs and arabic chicken eggs has many difficulties because physically the shape and color of eggs have similarities. This research was conducted to develop applications that were able to identify the types of domestic chicken eggs and Arab chicken eggs using the Principal Componenet Analysis (PCA) method and first order feature extraction. The application applies digital image processing stages, namely resizing image size, RGB color space conversion to HSV, contrast enhancement, image segmentation using the thresholding method, opening and region filling morphology operations, first order feature extraction and classification using the PCA method. The results of identification of types of native domestic chicken eggs and Arabic chicken eggs using the Principal Component Analysis method showed the results of 95% system accuracy percentage, consisting of 90% accuracy of success in the type of domestic chicken eggs and 100% accuracy of success in the type of Arabic chicken eggs.

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Keywords: Domestic Chicken Eggs; Arabian Chicken Eggs; PCA Method; First Order Feature Extraction

Article Metrics:

  1. Binawati, K., 2008. Pengaruh lanskeptur terhadap kualitas telur ayam Arab. Journal of Science. 1 (2) : 28-34
  2. Ditjenpkh, 2016. Statistik Peternakan dan Kesehatan Hewan. Jakarta. Ditjenpkh
  3. Fakumaprita, T., 2017. Identifikasi mutu telur ayam menggunakan jaringan syaraf tiruan perambatan- balik dan berdasarkan persyaratan tingkat mutu-fisik. Skripsi S-1, Universitas Diponegoro, Semarang, 2017
  4. Gonzales, R.C dan Woods, R.E., 2018. Digital Image Processing, Pearson Prentice hall, Upper Saddle River, New Jersey, 2018
  5. Harsadi, P., 2014. Deteksi embrio ayam berdasarkan citra grayscale menggunakan k-means automatic thresholding”, J. Ilmiah Sinus, vol.12, 2014
  6. Kunsah, B., 2016. Analisa kadar protein telur ayam kampung (Gallus Domesticus) terhadap lama penyimpanan pada suhu 12-15℃, The Journal of Muhammadiyah Medical Laboratory Technologist, Mei 2016, Vol. II No.1
  7. Malewadkar, P., Carvalho, F., Naik, S., Dias, N., 2017. Eggs defect detection using image processing, International Journal of Engineering Research & Technology (IJERT), Vol. 6 Issue 07, July, 2017
  8. Muzami, A. Oky, D.N., Kurniawan, T.M., 2016. Aplikasi citra telur ayam omega-3 dengan metode segmentasi region of interest berbasis android. Jurnal Teknologi dan Sistem Komputer, Vol.4, No.2, April 2016
  9. Pradipta, G., Pt D. Wulaning, A., 2017. Perbandingan segmenta telur ayam menggunakan metode otsu berdasarkan perbedaan ruang warna RGB dan HSV, Jurnal Sains dan Teknologi, Vol. 6, No. 1, April 2017
  10. Sujionohadi, Liwon dan Ade, 2016. Beternak Ayam Kampung Petelur. Penebar Swadaya
  11. Sutoyo, T., Mulyanto, E., Suhartono, O.D, 2009. Nurhayati, dan Wijanarto, Teori Pengolahan Citra Digital, Penerbit ANDI, Yogyakarta, 2009
  12. Triharyanto, B., 2001. Beternak Ayam Arab, Penerbit Kanisius, Yogyakarta, 2001

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Last update: 2021-02-26 17:57:43

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