# Comparison of Image Extraction Model for Cocoa Disease Fruits Attack in Support Vector Machine Classification
> Basri

URL kanonis: https://discover.unhas.ac.id/publications/comparison-of-image-extraction-model-for-cocoa-disease-fruits-attack-in-support
Jurnal / Konferensi: Proceedings Ieit 2022 2022 International Conference on Electrical and Information Technology
Tahun terbit: 2022
DOI: https://doi.org/10.1109/IEIT56384.2022.9967910
Citations: 16

## Authors
- Basri

## Abstract
This study aims to compare the results of four feature extraction models in the case of early recognition of disease attacks on cocoa fruits. The image extraction models used in this study are Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), Hue Saturation Value (HSV), and Gray-level Co-occurrence Histograms (GLCH). In addition, the Support Vector Machine (SVM) model was used for the classification technique to evaluate the extraction results from the cocoa image dataset. The classification results using SVM showed the best performance on feature extraction HSV in all types of Kernel SVM used (Linear, RBF, and Polynomial), with the highest accuracy of 80.95% on RBF Kernel. Furthermore, the HSV performance in recognizing disease attacks on cocoa fruits, based on Precision, Recall, and F1-Score values, showed that, on average, HSV had a better value than other feature extraction methods.

## Keywords
- Support vector machine
- Pattern recognition (psychology)
- Artificial intelligence
- Feature extraction
- Kernel (algebra)
- Computer science
- HSL and HSV
- Histogram
- Mathematics
- Image (mathematics)
- Medicine
- Virus
- Combinatorics
- Virology

---
Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
Saat mengutip, gunakan DOI bila tersedia atau URL kanonis di atas.