Klasifikasi Jeruk Nipis Terhadap Tingkat Kematangan Buah Berdasarkan Fitur Warna Menggunakan K-Nearest Neighbor

Cinantya Paramita, Eko Hari Rachmawanto, Christy Atika Sari, De Rosal Ignatius Moses Setiadi

Abstract


In the process of classification of lime fruit previously done manually using the human eye is a very difficult thing to do. This is proven by being inconsistent and subjective, causing a low level of accuracy. Sometimes there are also differences of opinion from the human eye to one another. Therefore, to increase the level of accuracy and reduce the subjectivity of the human eye, this study proposes the K-Nearest Neighbor algorithm to classify the maturity level of lime based on the skin color of the lime. In this study, the K values used were 1, 3, 5, 7 and 9 to test the search for Euclidean distance and cityblock distance distances on images with pixel sizes of 512x512, 256x256 and 128x128. In the prerosesing stage, the extraction feature process uses mean RGB. The research that has been done proves that with Euclidean distance distance k = 3 and k = 7 has a percentage value of 92% and the cityblock distance distance k = 1 and k = 3 has a percentage value of 88%. Based on the level of accuracy possessed, the color feature k = 3 shows the best k value in the classification of the maturity level of the lime fruit.

Keywords


klasifikasi, algoritma K-Nearest Neighbor (KNN), kematangan jeruk nipis, mean RGB

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References


E. S. Prakoso, “KAJIAN SIFAT FISIK JERUK MANIS (Citrus sinensis) MENGGUNAKAN PENGOLAHAN CITRA DIGITAL,” Jember, 2015.

Andri, Paulus, N. P. Wong, and T. Gunawan, “SEGMENTASI BUAH MENGGUNAKAN METODE K-MEANS CLUSTERING DAN IDENTIFIKASI KEMATANGANNYA MENGGUNAKAN METODE PERBANDINGAN KADAR WARNA,” JSM STIMIK Mikroskil, vol. 15, no. 2, pp. 91–100, 2014.

S. Y. Riska, “Klasifikasi Level Kematangan Tomat Berdasarkan Perbedaan Perbaikan Citra Menggunakan Rata-Rata RGB Dan Index Pixel,” J. Ilm. Teknol. dan Informasia ASIA, vol. 9, no. 2, pp. 18–26, 2015.

R. Munarto, E. Permata, and R. Salsabilla, “KLASIFIKASI KUALITAS BIJI JAGUNG MANIS BERDASARKAN FITUR WARNA MENGGUNAKAN FUZZY LOGIC,” in Simposium Nasional RAPI XIII, 2014, pp. 5–12.

P. R. Trisnaningtyas and Maimunah, “Klasifikasi Mutu Telur Berdasarkan Kebersihan Kerabang Telur Menggunakan K-Nearest Neighbor,” in Konferensi Nasional Informatika (KNIF) 2015, 2015, pp. 241–245.

D. Nugraheny, “METODE NILAI JARAK GUNA KESAMAAN ATAU KEMIRIPAN CIRI SUATU CITRA (KASUS DETEKSI AWAN CUMULONIMBUS MENGGUNAKAN PRINCIPAL COMPONENT ANALYSIS),” J. Angkasa, vol. 7, no. 2, pp. 21–30, 2015.

K. Warman, L. A. Harahap, and P. achwil Munir, “IDENTIFIKASI KEMATANGAN BUAH JERUK DENGAN TEKNIK JARINGAN SYARAF TIRUAN,” J. Rekayasa Pangan dan Pertan., vol. 3, no. 2, pp. 248–253, 2015.

A. Qur’ania, L. Karlitasar, and S. Maryana, “ANALISIS TEKSTUR DAN EKSTRAKSI FITUR WARNA UNTUK KLASIFIKASI APEL BERBASIS CITRA,” in Lokakarya Komputasi dalam Sains dan Teknologi Nuklir, 2012, pp. 296–304.

R. N. Whidhiasih, N. A. Wahanani, and Supriyanto, “KLASIFIKASI BUAH BELIMBING BERDASARKAN CITRA RED-GREEN-BLUE MENGGUNAKAN KNN DAN LDA,” J. Penelit. Ilmu Komputer, Syst. Embed. Log., vol. 1, no. 1, pp. 29–35, 2013.

E. Budianita, Jasril, and L. Handayani, “Implementasi Pengolahan Citra dan Klasifikasi K- Nearest Neighbour Untuk Membangun Aplikasi Pembeda Daging Sapi dan Babi,” J. Sains, Teknol. dan Ind., vol. 12, no. 2, pp. 242–247, 2015.

S. Sugiyanto and F. Wibowo, “KLASIFIKASI TINGKAT KEMATANGAN BUAH PEPAYA (CARICA PAPAYA L) CALIFORNIA (CALLINA-IPB 9) DALAM RUANG WARNA HSV DAN ALGORITMA K-NEAREST NEIGHBORS,” in Prosiding SENATEK, 2015, pp. 335–341.

F. Liantoni, “Klasifikasi Daun Dengan Perbaikan Fitur Citra Menggunakan Metode K-Nearest Neighbor,” ULTIMATICS, vol. VII, no. 2, pp. 98–104, 2015.

I. A. Halela, “Identifikasi Jenis Buah Apel Menggunakan Algoritma K-Nearest Neighbor (KNN) dengan Ekstraksi Fitur Histogram,” Semarang, 2016.

N. A. Fadhlillah, “ANALISIS DAN IMPLEMENTASI KLASIFIKASI K-NEAREST NEIGHBOR TELAPAK KAKI MANUSIA.”

D. Srianto, “PERBANDINGAN K-NEAREST NEIGHBOR DAN NAIVE BAYES UNTUK KLASIFIKASI TANAH LAYAK TANAM POHON JATI,” Techno.COM, vol. 15, no. 3, pp. 241–245, 2016.




DOI: http://dx.doi.org/10.30591/jpit.v4i1.1267

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Creative Commons License
JPIT (Jurnal Informatika: Jurnal Pengembangan IT) is licensed under a Creative Commons Attribution 4.0 International License.