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All Journal Jurnal Ilmiah Informatika dan Komputer
Devi Tiana Kartikasari
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Journal : Jurnal Ilmiah Informatika dan Komputer

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Penentuan Jenis Tomat Menggunakan Ekstraksi Ciri Bentuk dan Ukuran dengan Metode K-Means Devi Tiana Kartikasari; Retno Wahyusari
JIIFKOM (Jurnal Ilmiah Informatika dan Komputer) Vol 1 No 2 (2022): July
Publisher : Jurusan Informatika STTR Cepu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51901/jiifkom.v1i2.226

Abstract

Tomato plants are developed very rapidly, giving birth to new types of tomatoes. As a result, it is difficult for farmers to distinguish different types of tomatoes from one another. Determination of the type of tomato can be seen based on the size, shape, color and state of the skin of the fruit. One way to distinguish tomatoes from one another is to look at the characteristics of shape and size. Feature extraction is used so that it can be used as a differentiating reference for the type of tomato. Tomatoes are first converted from the original image or RGB to an HSV image then take the channel (S) from HSV for segmentation in order to get the value of extracting shape and size characteristics, grouping the types of tomatoes according to their respective types is by using the K-means method. The choice of the K-Means method is because besides being popular, it is also a simple and effective method. Ensuring that the process carried out gets accurate results, the calculation or process of determining the type of tomato can be added to help tools such as Rapid Miner and Matlab. Extraction of shape and size features with the K-means method was considered capable of distinguishing between types of tomatoes and can be grouped according to type. The number of data sets of 100 tomatoes with 5 types of tomatoes contained 11 points of error in the determination by the K-means method so that the accuracy obtained was 89%.
Co-Authors Wahyusari, Retno