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Agnes Lucky Rebecca
Universitas Amikom Yogyakarta, Yogyakarta,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Klasterisasi Jenis Tanah pada Tanaman Cabai Menggunakan Algoritma K-Means Ike Verawati; Agnes Lucky Rebecca
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6140

Abstract

Chili is one of the ingredients of spices that are widely used in Indonesia. Sales opportunities in the market are also very wide so that many are competing to grow chili with the best quality. There are several parameters that can improve the quality of chili, one of which is soil. Where if seen by the eye, soil that is suitable or unsuitable can be distinguished from color and texture, but not many people know the difference. Therefore, in this study, research will be conducted on the clustering of soil images based on color features and texture features using the K-Means algorithm which previously selected features using information gain feature selection. The first stage in this research is image acquisition and then the results will be processed first. From the results of pre-processing, RGB color feature extraction and first-order texture feature extraction will be carried out which is then followed by feature selection using information gain which is expected to produce the best features which will then proceed to clustering using the K-Meaning algorithm. The final step is to conduct an analysis to obtain the results of this study. The results obtained from the clustering that have been carried out can be obtained that the K-Means algorithm can cluster suitable and unsuitable soil images with information gain, resulting in 63 suitable soil images and 37 unsuitable soil images from 100
Co-Authors Ike Verawati