Otomatisasi klasifikasi kematangan buah mengkudu berdasarkan warna dan tekstur

Authors

  • Selvia Ferdiana Kusuma Teknik Informatika - Politeknik Kediri
  • Ratri Enggar Pawening Sekolah Tinggi Teknologi Nurul Jadid
  • Rohman Dijaya Universitas Muhammadiyah Sidoarjo

DOI:

https://doi.org/10.26594/register.v3i1.576

Keywords:

classification of noni, color, classification, texture, klasifikasi mengkudu, klasifikasi, KNN, SVM, tekstur, warna

Abstract

 

Buah Mengkudu merupakan komoditi ekspor yang sedang berkembang di Indonesia. Proses pengklasifikasian kematangan buah Mengkudu perlu dilakukan agar kualitas buah Mengkudu yang di ekspor dapat terjamin. Proses klasifikasi dengan jumlah yang banyak akan sulit apabila dilakukan secara manual. Oleh karena itu, penelitian ini diperlukan untuk menghasilkan proses otomatisasi klasifikasi kematangan buah Mengkudu. Metode yang diusulkan untuk melakukan otomatisasi klasifikasi adalah proses pengenalan karakteristik buah Mengkudu berdasarkan fitur tekstur dan warna. Fitur tektur dan fitur warna didapatkan melalui proses pengolahan citra digital buah Mengkudu. Penelitian ini membuktikan bahwa pengklasifikasian buah Mengkudu dengan algoritma Support Vector Machines (SVM) menghasilkan nilai persentase lebih tinggi dari pada menggunakan algoritma k-Nearest Neighbors (KNN). Hasil persentase tertinggi yang didapatkan yaitu sebesar 87.22%.

 

 

 

Noni fruit is an export commodities that were flourishing in Indonesia. Noni fruit maturity classification process should be done in order the quality of the noni fruit which is exported can be guaranteed. Classification process in large quantities will be difficult if it is done manually. Therefore this research is needed to produce an automation classification process of noni fruit ripeness. The proposed method is characteristic introduction of noni fruit based on texture and color features. Texture and color features are obtained from digital image processing of noni fruit. This research proves that the classification of noni fruit with SVM algorithm produces better accuracy than using KNN algorithm. The highest accuracy is equal to 87.22%.

Author Biography

Selvia Ferdiana Kusuma, Teknik Informatika - Politeknik Kediri

Dosen Teknik Informatika

References

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Published

2017-01-01

How to Cite

[1]
S. F. Kusuma, R. E. Pawening, and R. Dijaya, “Otomatisasi klasifikasi kematangan buah mengkudu berdasarkan warna dan tekstur”, regist. j. ilm. teknol. sist. inf., vol. 3, no. 1, pp. 17–23, Jan. 2017.

Issue

Vol. 3 No. 1 (2017): January

Section

Article

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