KLASIFIKASI KEMATANGAN BUAH ALPUKAT MENTEGA BERDASARKAN FITUR WARNA MENGGUNAKAN SUPPORT VECTOR MACHINE

Amir Hamzah; Erma Susanti; Ria Mega Lestari

doi:10.47080/iftech.v6i1.3103

Amir Hamzah Jurusan Informatika Fakultas Teknologi Informasi dan Bisnis, Institut Sains & Teknologi AKPRIND Yoagyakarta
Erma Susanti Institut Sains & Teknologi AKPRIND Yogyakarta
Ria Mega Lestari Institut Sains & Teknologi AKPRIND Yogyakarta

DOI: https://doi.org/10.47080/iftech.v6i1.3103

Keywords: Color, Classification, RGB, SVM, Support Vector Machine

Abstract

The ripeness level of the avocado greatly affects the shelf life of the fruit and also determines the taste of the fruit. Determining the proper ripeness of a fruit will play an important role in increasing the nutritional value and ripeness of avocado affects the quality of avocado oil. Classification of fruit maturity manually has many limitations because it is influenced by human subjectivity so that the application of digital image processing needs to be used.This study aims to classify avocado fruit ripeness which is divided into three categories, namely Unripe, Ripe, and Rotten. This research is intended to facilitate the classification of ripeness in fruit through its color. This study used avocado fruit as an experimental material which was carried out using the SVM (Support Vector Machine) method. The colors of the various avocados and in different positions and conditions of light contrast are used as data to classify the types of avocados. Data collection was taken from the selected Keagle Dataset of 150 data. The data is grouped into two data sets, namely dataset-1 (120 training data and 30 test data) and dataset-2 (90 training data and 60 test data). The image will be converted to grayscale to facilitate the classification process and look for HSV (Hue, Saturation, Value) and RGB (Red, Green, Blue) values to classify the fruit. The results from dataset-1 has an accuracy rate of 86%, 90% precision, and 86% Recall, while dataset-2 has an accuracy rate of 85%, 84% precision, and 85% Recall.

References

Arief, M. (2019). Klasifikasi Kematangan Buah Jeruk Berdasarkan Fitur Warna Menggunakan Metode SVM. Jurnal Ilmu Komputer Dan Desain Komunikasi Visual, 4(1), 9–16.
Arifin, J. (2022). Klasifikasi Citra Tekstur Kayu Menggunakan Gray Level CO-Occurance Matrix dan Local Binary Pattern. Jurnal Informatika Dan Komputer), 6(1), 34–40.
Astrianda, N. (2020). Klasifikasi Kematangan Buah Tomat Dengan Variasi Model Warna Menggunakan Support Vector Machine. VOCATECH: Vocational Education and Technology Journal, 1(2), 45–52. https://doi.org/10.38038/vocatech.v1i2.27
Christian, B. F., Lestari, U., & Andayati, D. (2019). Sistem Aplikasi Identifikasi Kematangan Buah Jeruk Nipis Berdasarkan Fitur Warna dan Menggunakan Support Vector Machine. Jurnal Script, 7(2), 248–256.
Hanafi, M. H., Fadillah, N., & Insan, A. (2019). Optimasi Algoritma K-Nearest Neighbor untuk Klasifikasi Tingkat Kematangan Buah Alpukat Berdasarkan Warna. IT JOURNAL RESEARCH AND DEVELOPMENT, 4(1), 10–18. https://doi.org/10.25299/itjrd.2019.vol4(1).2477
Indrabayu, Arifin, N., & Areni, I. S. (2019). Klasifikasi Kematangan Stroberi Berbasis Segmentasi Warna dengan Metode HSV. Jurnal Penelitian Enjiniring, 23(2), 113–116. https://doi.org/10.25042/jpe.112019.03
Razmjooy, N., Somayeh Mousavi, B., Sargolzaei, P., & Soleymani, F. (2011). Image thresholding based on evolutionary algorithms. International Journal of Physical Sciences, 6(31), 7203–7211. https://doi.org/10.5897/IJPS11.1248
Ritonga, A. S., & Purwaningsih, E. S. (2018). Penerapan Metode Support Vector Machine (SVM) dalam Klasifikasi Kualitas Pengelasan SMAW (Shiled Metal Arc Welding). In Jurnal Ilmiah Edutic (Vol. 5, Issue 1).
Saputra, J., Sa, Y., Yoga Pudya Ardhana, V., & Afriansyah, M. (2023). Klasifikasi Kematangan Buah Alpukat Mentega Menggunakan Metode K-Nearest Neighbor Berdasarkan Warna Kulit Buah. RESOLUSI : Rekayasa Teknik Informatika Dan Informasi , 3(5), 347–354. https://djournals.com/resolusi
Saputra, M. R., & Irsyad, H. (2022). Klasifikasi Tingkat Kemanisan Alpukat Berdasarkan Fitur Hue Saturation Value (HSV) dengan Menggunakan Support Vector Machine (SVM). Jurnal Algoritme, 2(2), 113–119.
Saraswati, M. N., Rito, C. S. H., & Chandra, D. (2023). Face Recognition Menggunakan Metode HAAR Cascade Classifier dan Local Binary Pattern Histogram. Jurnal MEDIA ELEKTRIK, 20(3), 2721–9100.
Sihombing, P. R., & Yuliati, I. F. (2021). Penerapan Metode Machine Learning dalam Klasifikasi Risiko Kejadian Berat Badan Lahir Rendah di Indonesia. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 20(2), 417–426. https://doi.org/10.30812/matrik.v20i2.1174
Vernanda, D., Nugraha Purnawan, N., Herdiawan Apandi, T., Negeri Subang, P., Brigjen Katamso No, J., & Korespondensi, E. (2020). Data Analisis Klasifikasi Tingkat Kemanisan Nanas Menggunakan Svm Data Analysis For Classification Of Pineapple Ripeness Using Support Vector Machine. Jurnal Ilmiah Ilmu Dan Teknologi Rekayasa |, 3(2), 32–41. https://doi.org/10.31962/jiitr.vvii.52
Verti, E. A., Mustikarini, E. D., & Lestari, T. (2021). Diversity of Avocado Germplasm (Persea americana) in Bangka Island Base On Morphological Character. Seminar Nasional Penelitian Dan Pengabdian Masyarakat, Fak Teknik Univ Bangka Belitung, 33–38.
Yana, Y. E., & Nafi’, N. (2021). Klasifikasi Jenis Pisang Berdasarkan Fitur Warna, Tekstur, Bentuk Citra Menggunakan SVM dan KNN. Journal of Computer, Information System, & Technology Management, 4(1), 28–36.