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Sistem Klasifikasi Kualitas Kopra Berdasarkan Warna dan Tekstur Menggunakan Metode Nearest Mean Classifier (NMC)

Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 4 No. 4 (2017) · pp. 297–303

DOI
10.25126/jtiik.201744479
ISSN
2528-6579, 2355-7699
Published
2017-12-31
Publisher
Fakultas Ilmu Komputer, Universitas Brawijaya
Language
en

Abstract

AbstrakKlasifikasi kualitas kopra dengan bantuan komputer menggunakan pengolahan citra dapat membantu mempercepat pekerjaan manusia. Teknik data mining dapat dimanfaatkan untuk klasifikasi kualitas kopra berdasarkan warna RGB (red, green, blue) dan tekstur (energy, contrast, correlation, homogeneity),. Permasalahannya adalah kesulitan dalam memprediksi kualitas kopra dalam kelas A (80-85%), kelas B (70-75%) dan kelas C (60-65%). Tujuan dari penelitian ini ialah membangun aplikasi untuk klasifikasi kualitas kopra berdasarkan warna dan tekstur. Adapun metode klasifikasi yang digunakan adalah nearest mean classifier (NMC). Sebelum proses klasifikasi dilakukan praproses background subtraction dengan metode pixel subtraction untuk memisahkan objek dengan latarnya. Manfaat dari penelitian ini antara lain dapat menghemat waktu dalam melakukan klasifikasi kualitas kopra dan dapat mempermudah penentuan harga jual beli kopra. Berdasarkan hasil evaluasi metode cross validation diperoleh ketelitian rata-rata adalah 80.67% dengan simpangan baku 1.17%. Kata Kunci: Klasifikasi, citra, kopra, nearest mean classifier, pixel subtraction, warna RGB, teksturAbstractThe classification of copra quality with the help of computer by using image processing can help to speed up human work. Data mining techniques can be utilized for copra quality classification based on RGB color (red, green, blue) and texture (energy, contrast, correlation, homogeneity). The problem is the difficulty in predicting the quality of copra in grade of A (80-85%), grade of B (70-75%) and grade of C (60-65%). The purpose of this study is to develope an application for the classification of copra quality based on color and texture. The method used is the nearest mean classifier (NMC). Preprocessing is done before the classification process for background subtraction by using pixel subtraction method to separate the image of object against the background. The benefits of this research are it can save time in classifying the quality of copra and can facilitate the determination of copra price. Based on the evaluation result by using cross validation method obtained the average accuracy is 80.67% with standard deviation is 1.17%. Keywords: classification, image, copra, nearest mean classifier, pixel subtraction, RGB color, texture

Collaboration network of the authors

8 authors · 12 co-authorship links · 4 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Institut Agama Islam Negeri Kendari
  • Universitas Islam Indragiri
  • Universitas Muhammadiyah Kendari
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Muliyani MuliyaniUniversitas Muhammadiyah Kendari1
Arsam ArsamUniversitas Muhammadiyah Kendari1
Samrin SamrinInstitut Agama Islam Negeri Kendari1
Jumarddin La FuaInstitut Agama Islam Negeri Kendari1
Zul ArhamInstitut Agama Islam Negeri Kendari1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://jtiik.ub.ac.id/index.php/jtiik/en/oai, first harvested 27 Okt 2026, last seen 1 Nov 2026.
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	<dc:title xml:lang="id">Sistem Klasifikasi Kualitas Kopra Berdasarkan Warna dan Tekstur Menggunakan Metode Nearest Mean Classifier (NMC)</dc:title>
	<dc:creator xml:lang="en">Abdullah, Abdullah</dc:creator>
	<dc:creator xml:lang="en">Usman, Usman</dc:creator>
	<dc:creator xml:lang="en">Efendi, M</dc:creator>
	<dc:description xml:lang="id">AbstrakKlasifikasi kualitas kopra dengan bantuan komputer menggunakan pengolahan citra dapat membantu mempercepat pekerjaan manusia. Teknik data mining dapat dimanfaatkan untuk klasifikasi kualitas kopra berdasarkan warna RGB (red, green, blue) dan tekstur (energy, contrast, correlation, homogeneity), . Permasalahannya adalah kesulitan dalam memprediksi  kualitas kopra dalam kelas A (80-85%), kelas B (70-75%) dan kelas C (60-65%). Tujuan dari penelitian ini ialah membangun aplikasi untuk klasifikasi kualitas kopra berdasarkan warna dan tekstur. Adapun metode klasifikasi yang digunakan adalah nearest mean classifier (NMC). Sebelum proses klasifikasi dilakukan praproses background subtraction dengan metode pixel subtraction untuk memisahkan objek dengan latarnya. Manfaat dari penelitian ini antara lain dapat menghemat waktu dalam melakukan klasifikasi kualitas kopra dan dapat mempermudah penentuan harga jual beli kopra. Berdasarkan hasil evaluasi metode cross validation diperoleh ketelitian rata-rata adalah 80.67% dengan simpangan baku 1.17%. Kata Kunci: Klasifikasi, citra, kopra, nearest mean classifier, pixel subtraction, warna RGB, teksturAbstractThe classification of copra quality with the help of computer by using image processing can help to speed up human work. Data mining techniques can be utilized for copra quality classification based on RGB color (red, green, blue) and texture (energy, contrast, correlation, homogeneity). The problem is the difficulty in predicting the quality of copra in grade of A (80-85%), grade of B (70-75%) and grade of C (60-65%). The purpose of this study is to develope an application for the classification of copra quality based on color and texture. The method used is the nearest mean classifier (NMC). Preprocessing is done before the classification process for background subtraction by using pixel subtraction method to separate the image of object against the background. The benefits of this research are it can save time in classifying the quality of copra and can facilitate the determination of copra price. Based on the evaluation result by using cross validation method obtained the average accuracy is 80.67% with standard deviation is 1.17%. Keywords: classification,  image, copra, nearest mean classifier, pixel subtraction, RGB color, texture </dc:description>
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	<dc:date>2017-12-31</dc:date>
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	<dc:source xml:lang="id">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol 4 No 4: Desember 2017; 297-303</dc:source>
	<dc:source xml:lang="en">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol. 4 No. 4: Desember 2017; 297-303</dc:source>
	<dc:source>2528-6579</dc:source>
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