Classification of Watermelon Ripeness Levels Using HSV Color Space Transformation and K-Nearest Neighbor Method
DOI:
10.47709/cnahpc.v6i3.3999Keywords:
Watermelon, HSV (Hue Saturation Value), K-Nearest NeighborDimension Badge Record
Abstract
Watermelons had high appeal due to their sweet taste, refreshing nature, and numerous benefits. However, consumers often faced difficulties in selecting suitable fruit because of the subtle differences between fully ripe and half-ripe watermelons. One important indicator of a watermelon’s ripeness was the yellowish pattern on its skin. In this study, the proposed use of digital image processing methods, specifically the HSV Color Space Transformation, was aimed at extracting watermelon images and employing the K-Nearest Neighbor (K-NN) method to classify them into two categories: "Ripe" and "Half-Ripe." HSV (Hue Saturation Value) was a color extraction method used to convert colors from the RGB model. The Hue component indicated the type of color, Saturation measured the purity of the color, and Value measured the brightness of the color on a scale from 0 to 100%. In this research, the K-Nearest Neighbor (K-NN) method was applied to classify watermelon images based on the extraction of skin color features. This method compared a new image (test data) with training images to determine classification based on the nearest distance with a parameter of k=3. The data used consisted of 120 images, with 92 images used as training data and 28 images as test data. Experimental results showed an accuracy of 89%, with 25 images correctly classified and 3 images misclassified.
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References
A’yun, Q., & Utaminingrum, F. (2022). Rancang Bangun Deteksi Kemanisan Buah Semangka menggunakan Metode Gray Level Co-Occurrence Matrix dan Backpropagation Neural Network berbasis Raspberry Pi. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 6(2), 707–712.
Batubara, M. Z., & Nasution, M. I. P. (2023). Sistem Informasi Online Pengelolaan Dana Sosial Pada Rumah Yatim Sumatera Utara. Jurnal Teknologi Dan Sistem Informasi Bisnis, 5(3), 164–171.
Dalimunthe, A. (2021). DETEKSI KEMATANGAN BUAH MANGGIS BERDASARKAN FITUR WARNA CITRA KULIT MENGGUNAKAN METODE TRANSFORMASI RUANG WARNA HSV. Medan.
Ellif, Sitorus, S. H., & Hidayati, R. (2021). KLASIFIKASI KEMATANGAN PEPAYA MENGGUNAKAN RUANG WARNA HSV DAN METODE NAIVE BAYES CLASSIFIER. Coding?: Jurnal Komputer Dan Aplikasi, 09(1), 66–75.
Hasibuan, A. K. (2020). Klasifikasi Jenis Jambu Berdasarkan Daun Menggunakan Metode Principal Component Analysis. Medan. Retrieved from http://repository.uinsu.ac.id/13715/
Himmah, E. F., Widyaningsih, M., & Maysaroh. (2020). Identifikasi Kematangan Buah Kelapa Sawit Berdasarkan Warna RGB Dan HSV Menggunakan Metode K-Means Clustering. Jurnal Sains Dan Informatika, 6(2), 193–202. https://doi.org/10.34128/jsi.v6i2.242
Ibnutama, K., Suryanata, M. G., Putri, R. O., & Hafiz, A. Al. (2023). Seleksi Tingkat Kematangan Citra Buah Belimbing Menggunakan Ruang Warna CMYK. Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika Dan Komputer), 22(2), 302–310.
Khotimah, H., Nafi’iyah, N., & Masruroh. (2019). Klasifikasi Kematangan Buah Mangga Berdasarkan Citra HSV dengan KNN. ELTI Jurnal Elektronika, Listrik Dan Teknologi Informasi Terapan, 2(1), 1–7.
Kusumah, F., & Maulana, A. (2022). Analisis Sistem Pendeteksi Wajah Pada Gambar Dengan Metode K-Nearest Neighbor. Medan: Pascal Books. Retrieved from https://books.google.co.id/books?hl=id&lr=&id=ylxpEAAAQBAJ&oi=fnd&pg=PA29&dq=Kusumah,+F.,+dkk.+2022.+Analisis+Sistem+Pendeteksi+Wajah+Pada+Gambar+Dengan+Metode+K-Nearest+Neighbor.+Cipayung:+Pascal+Books.&ots=gaBMFlVZ6a&sig=CUWKkqjJHWm7yDCjVU3yNJFZW30&redir_esc=y#v=onepage&q&f=false
Liantoni, F., & Nugroho, H. (2019). Classification of Watermelon Maturity Based on the Extraction Characteristics of First Order Statistics with Equalization of Histograms. Informatika Pertanian, 28(1), 43–48.
Marjan, A. R., & Mukhaiyar, R. (2020). Perancangan Konveyor Pengangkut Buah Semangka Berdasarkan Berat Berbasis Microkontroller. Ranah Research?: Journal of Multidisciplinary Research and Development, 3(1), 1–7.
Nuriarta, I. W., Ari, I. A. D. K., & Suryawan, I. G. (2021). ANALISIS VISUAL GAMBAR ANAK PADA MASA PRA-SKEMATIK. Pratama Widya?: Jurnal Pendidikan Anak Usia Dini, 6(2), 165–174.
Safitri, Y., & Juwita, D. S. (2022). PENGABDIAN KEPADA MASYARAKAT TENTANG DIVERSIFIKASI BUAH SEMANGKA DI DESA RIDAN PERMAI KECAMATAN BANGKINANG KOTA KABUPATEN KAMPAR TAHUN 2021. Communnity Development Journal, 3(3), 2193–2195.
Situmoraang, R. N. (2021). Klasifikasi Kesegaran Ikan Berdasarkan Ekstraksi Fitur Menggunakan Metode K-Nearest Neighbor Dan Hue Saturation Value. Medan. Retrieved from http://repository.uinsu.ac.id/15283/
Situmorang, N. W. R. (2019). Penerapan Metode K-Nearest Neighbor dalam Identifikasi Kesegaran Ikan. Medan.
Sukarno. (2020). ANALISA PENDAPATAN MANISAN KULIT SEMANGKA SEBAGAI UPAYA PENINGKATAN NILAI TAMBAH LIMBAH KULIT SEMANGKA. Jurnal Ekonomi Pembangunan, 6(2), 172–181.
Syahranitazli, & Samsudin. (2023). Sistem Informasi Geografis Persebaran Pondok Pesantren Kabupaten Langkat Dan Binjai Menggunakan Leaflet. Jurnal Pendidikan Teknologi Informasi (JUKANTI), 6(1), 2621–1467.
Wibowo, A., Hermanto, D. M. C., Lestari, K. I., & Wijoyo, H. (2021). Deteksi Kematangan Buah Jambu Kristal Berdasarkan Fitur Warna Menggunakan Metode Transformasi Ruang Warna Hsv (Hue Saturation Value) Dan K-Nearest Neighbor. INCODING: Journal of Informatic and Computer Science Engineering, 1(2), 76–88. https://doi.org/10.34007/incoding.v2i1.131
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