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Red apple is one of the fruit plants with a lot of enthusiasts so it is very popular in the market. Red apples also have several types that at first glance look similar to one another. This is what makes it difficult for people to distinguish between red apples that are consumed, especially since there is no information label to explain these apples. Therefore, in this study a classification of red apples was carried out based on their shape and color characteristics. Image data used is secondary data in * JPG format with a size of 100 x 100 pixels. The method used is the extraction of the Mean HSV color feature (the output value is 3) and the Moment Invariant form feature (the output value is 7) so that each image has 10 values. Image classification results were obtained using Euclidean Distance. Meanwhile, the test scenario used K-Fold Cross Validation where 1,710 image data were divided into 10-folds with 171 images in each subset. From 10-fold tested 50 times, so that an average accuracy of 98.82% was obtained. The highest accuracy was obtained in the 46th test of 99.12% and the lowest accuracy was in the 48th test of 98.54%.
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 N. Wijaya and A. Ridwan, “Klasifikasi Jenis Buah Apel Dengan Metode K-Nearest Neighbors,” vol. 08, p. 5, 2019.
 A. A. Nurcahyani and R. Saptono, “Identifikasi kualitas beras dengan citra digital,” Scientific Journal of Informatics, vol. 2, no. 1, pp. 63–72, 2015.
 F. Muwardi and A. Fadlil, “Sistem Pengenalan Bunga Berbasis Pengolahan Citra dan Pengklasifikasi Jarak,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 3, no. 2, p. 124, Jan. 2018, doi: 10.26555/jiteki.v3i2.7470.
 A. S. Arifianto, T. Rizaldi, and M. Yuris, “Klasifikasi Citra Buah Apel Batu Menggunakan Teknik Digital Image Processing Berdasarkan Fitur Warna HIS,” Prosiding, 2016.
 A. K. Tarigan, S. D. Nasution, S. Suginam, and A. Karim, “Aplikasi Pembelajaran Citra Dengan Menggunakan Metode Computer Assisted Instruction (CAI),” JURIKOM (Jurnal Riset Komputer), vol. 3, no. 4, 2016.
 A. Qur’ania, L. Karlitasar, and S. Maryana, “ANALISIS TEKSTUR DAN EKSTRAKSI FITUR WARNA UNTUK KLASIFIKASI APEL BERBASIS CITRA,” p. 9, 2012.
 V. Y. I. Ilwaru, Y. A. Lesnussa, E. M. Sahetapy, and Z. A. Leleury, “APLIKASI OPERASI HIMPUNAN DAN MATEMATIKA MORFOLOGI PADA PENGOLAHAN CITRA DIGITAL,” BAREKENG, vol. 10, no. 2, pp. 83–96, Dec. 2016, doi: 10.30598/barekengvol10iss2pp83-96.
 R. D. Kusumanto, A. N. Tompunu, W. S. Pambudi, J. T. Komputer, and P. N. Sriwijaya, “Klasifikasi Warna Menggunakan Pengolahan Model Warna HSV,” Jurnal Ilmiah Elite Elektro, vol. 2, no. 2, pp. 83–87, 2011.
 B. Y. Budi Putranto, W. Hapsari, and K. Wijana, “SEGMENTASI WARNA CITRA DENGAN DETEKSI WARNA HSV UNTUK MENDETEKSI OBJEK,” INF, vol. 6, no. 2, Feb. 2011, doi: 10.21460/inf.2010.62.81.
 K. Ayuningsih, Y. A. Sari, and P. P. Adikara, “Klasifikasi Citra Makanan Menggunakan HSV Color Moment dan Local Binary Pattern dengan Naïve Bayes Classifier,” p. 8.
 A. Bhardwaj, M. Kaur, and A. Kumar, “Recognition of plants by Leaf Image using Moment Invariant and Texture Analysis,” vol. 3, no. 1, p. 12, 2013.
 Z. Huang and J. Leng, “Analysis of Hu’s Moment Invariants on Image Scaling and Rotation,” vol. 7, p. 6.
 N. S. Limin, J. Y. Sari, and I. P. N. Purnama, “Identifikasi Tingkat Kematangan Buah Pisang Menggunakan Metode Ektraksi Ciri Statistik Pada Warna Kulit Buah,” Ultimatics, vol. 10, no. 2, pp. 98–102, Mar. 2019, doi: 10.31937/ti.v10i2.1004.
 D. A. Nasution, H. H. Khotimah, and N. Chamidah, “Perbandingan Normalisasi Data untuk Klasifikasi Wine Menggunakan Algoritma K-NN,” CESS (Journal of Computer Engineering, System and Science), vol. 4, no. 1, pp. 78–82, 2019.