Metode K-Nearest Neighbor dan Ekstraksi Fitur GLCM untuk Mengklasifikasikan Biji Kopi Robusta dan Arabika Lokal
DOI:
https://doi.org/10.51903/semnastekmu.v2i1.189Keywords:
Biji Kopi, Robusta, Arabika, K-Nearest Neighbor, Gray Level Co-occurrence Matrix.Abstract
Beberapa produk kopi yang cukup terkenal adalah Kopi Robusta dan Kopi Arabika. Memanfaatkan teknologi untuk membantu mengidentifikasi bagaimana perbedaan penampilan biji kopi menjadi salah satu isu yang perlu di uji coba, bahkan, pengidentifikasian menggunakan data citra juga dapat dilakukan dengan optimal. Proses pengidentifikasian tersebut dilakukan dengan cara memanfaatkan teknik pengolahan citra digital. Pada penelitian ini, telah digunakan Algoritma K-Nearest Neighbor (K-NN) dan Ekstraksi Fitur Gray Level Co-occurrence Matrix (GLCM) untuk melakukan proses pengklasifikasian biji kopi Robusta dan Arabika. Digunakan dataset gambar sebanyak 194 gambar biji kopi yang merupakan gabungan dari 97 data citra dari biji kopi Robusta, dan 97 data citra dari biji kopi Arabika. Dari keseluruhan dataset tersebut, digunakan sebanyak 174 data sebagai data latih, dan 20 data sisanya sebagai data uji. Akurasi tertinggi yang dihasilkan dari eksperimen ini sebesar 95% dengan ketentuan jarak piksel = 1, nilai K = 1, dan besar sudur = 45o.
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E. Hari Rachmawanto, G. Rambu Anarqi, D. R. I. Moses Setiadi, and C. Atika Sari, “Handwriting Recognition Using Eccentricity and Metric Feature Extraction Based on K-Nearest Neighbors,” Proc. - 2018 Int. Semin. Appl. Technol. Inf. Commun. Creat. Technol. Hum. Life, iSemantic 2018, pp. 411–416, 2018.
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Published
2023-01-25
How to Cite
Cahaya Jatmoko, & Daurat Sinaga. (2023). Metode K-Nearest Neighbor dan Ekstraksi Fitur GLCM untuk Mengklasifikasikan Biji Kopi Robusta dan Arabika Lokal. Seminar Nasional Teknologi Dan Multidisiplin Ilmu (SEMNASTEKMU), 2(2), 353–366. https://doi.org/10.51903/semnastekmu.v2i1.189
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