- COMPARISON OF APPLE IMAGE SEGMENTATION USING BINARY CONVERSION AND K-MEANS CLUSTERING METHODS

Abstraksi

Apples are quite popular consumption among the community and have different kinds of shapes and colors. Apples themselves have many nutrients and various vitamins including fat, as well as energy, carbohydrates, protein, vitamin C, vitamin A, vitamin B2, vitamin B1, and many more. Because of the variety of types of apples, it is difficult for people to distinguish between these types of apples. However, with the development of technology and sophistication, it is now possible to classify the types of apples using digital images. This study aims to segment the image of apples by comparing 2 methods at once to find out which method is the best. This process is an initial stage that must be done before classifying. From the comparison results of apple image segmentation with binary conversion methods and k-means clustering, it can be concluded that the best method is k-means clustering. Because it can segment the image of apples almost perfectly

Kata Kunci: Apples, Image, Binary Conversion, KMeans Clustering

URI
https://ejournal.nusamandiri.ac.id/index.php/pilar/article/view/2256

Bidang ilmu
Image Processing

References

Alamsyah, D., & Pratama, D. (2019). Segmentasi Warna Citra Bunga Daisy dengan Algoritma KMeans pada Ruang Warna Lab. Jurnal Buana Informatika, 10(2), 153. https://doi.org/10.24002/jbi.v10i2.2458 Alvini, S., & Dewi, M. P. (2021). Penerapan Pohon Rentang Minimum pada Graph dalam Segmentasi Citra. UNP Journal of Mathematics, 4(2), 57–61. Ciputra, A., Susanto, A., & dkk. (2018). Dengan Algoritma Naive Bayes Dan Ekstraksi Fitur Citra Digital. Simetris : Jurnal Teknik Mesin, Elektro Dan Ilmu Komputer, 9(1), 465–472. Firmanto, B., Rikasanti, E., & Putra, A. B. W. (2019). Optimasi Hasil Akuisisi Obyek Wajah Jurnal PILAR Nusa Mandiri Vol. 17, No. 1 March 2021 103 Menggunakan Metode Binary Large Objects. Semnas SENASTEK Unikama 2019, 2, 826–840. https://conference.unikama.ac.id/artikel/ind ex.php/senastek/article/view/312 Nafi’iyah, N., & Fatichah, C. (2017). Fuzzy self organizing map untuk proses thresholding pada citra dental panaromic. Seminar Nasional Sistem Informasi, September, 511–524. Premana, A., Bhakti, R. M. H., & Prayogi, D. (2020). Segmentasi K-Means Clustering Pada Citra Menggunakan Ekstrasi Fitur Warna dan Tekstur. Jurnal Ilmiah Intech : Information Technology Journal of UMUS, 2(01). https://doi.org/10.46772/intech.v2i01.190 Prianggara, ferdian wahyu, Setiawan, ahmad bagus, & Farida, intan nur. (2020). Identifikasi Jenis Buah Apel Berdasarkan Ekstraksi Bentuk dan Warna. Qisti, N., Nurwidah, A., Padapi, A., & Haryono, I. (2020). Analisa Kelayakan Usaha Pembuatan Selai Apel di UMS Rappang Store. MALLOMO: Journal of Community Service, 1, 22–29. Rahmah, S. A. (2020). Klasterisasi Pola Penjualan Pestisida Menggunakan Metode K-Means Clustering (Studi Kasus Di Toko Juanda Tani Kecamatan Hutabayu Raja). Djtechno : Journal of Information Technology Research, 1(1), 1–5. Satun, H., & Pandiangan, M. (2020). Segmentasi Citra Untuk Pencarian Kode Warna Cat Menggunakan Metode Thershold Hsv. Bulletin of Information Technology ( BIT ), 1(3), 134– 143. Sibuea, F. L., & Sapta, A. (2017). Pemetaan Siswa Berprestasi Menggunakan Metode K-Means Clustering. JURTEKSI (Jurnal Teknologi Dan Sistem Informasi), 4(1), 85–92. Sinaga, A. S. R. (2017). Implementasi Teknik Threshoding Pada Segmentasi Citra Digital. Jurnal Manajemen Dan Informatika Pelita Nusantara, 1(2), 48–51. Suriani, L. (2020). Pengelompokan Data Kriminal Pada Poldasu Menentukan Pola Daerah Rawan Tindak Kriminal Menggunakan Data Mining Algoritma K-Means Clustering. Jurnal Sistem Komputer Dan Informatika (JSON, 1, 151–157. https://doi.org/10.30865/json.v1i2.1955 Zainuddin, M., Sianturi, L. T., & Hondro, R. K. (2017). Implementasi Metode Robinson Operator 3 Level Untuk Mendeteksi Tepi Pada Citra Digital. Jurnal Riset Komputer (JURIKOM), 4(4), 1–5.