Nadya Permatasari Batubara, . (2020) KLASIFIKASI REMPAH RIMPANG BERDASARKAN CIRI WARNA RGB DAN TEKSTUR GLCM MENGGUNAKAN ALGORITMA NAIVE BAYES. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
This research will discuss how to classify several types of spices based on the Naïve Bayes algorithm by using RGB color feature extraction and GLCM texture. The stages in the digital image classification process in this study are pre-image processing, segmentation, feature extraction, classification and performance testing. The stages of extracting features or information in a digital image are very influential to recognize the object in the image, the more features that are extracted will affect the level of accuracy of image classification. The process carried out in this research is to change the RGB to Grayscale to get the gray image, after changing the image to Grayscale. Perform image enhancement with intensity adjustments to increase the level of image contrast. After making the image placement, the image is segmented by thresholding using the Otsu method. The results of the segmentation carried out namely Region of Interest (RoI) produce pixel multiplication. After that the feature is extracted using the Gray Level Co-occurrence Matrix (GLCM) and the extraction of the RGB features extracted into the GLCM. The last stage of this research is the classification using the Naïve Bayes algorithm. The final score from classify Naïve Bayes getting 52%.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [Pembimbing 1 : Didit Widiyanto] [Pembimbing 2 : Nurul Chamidah] [Penguji 1 : Jayanta] [Penguji 2 : Bambang Tri Wahyono] |
| Uncontrolled Keywords: | Keywords: Spices, Naïve Bayes, RGB, GLCM |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | Nadya Permatasari Batubara |
| Date Deposited: | 12 Jan 2022 04:54 |
| Last Modified: | 12 Jan 2022 04:54 |
| URI: | http://repository.upnvj.ac.id/id/eprint/6847 |
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