PENGGUNAAN FITUR WARNA DAN TEKSTUR UNTUK CONTENT BASED IMAGE RETRIEVAL CITRA BUNGA

Putri, Rahmaniansyah Dwi (2017) PENGGUNAAN FITUR WARNA DAN TEKSTUR UNTUK CONTENT BASED IMAGE RETRIEVAL CITRA BUNGA. S1 thesis, Universitas Pendidikan Indonesia.

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Official URL: http://repository.upi.edu

Abstract

Pencarian gambar berdasarkan gambar pada database, seringkali dilakukan untuk mengatasi duplikasi pada suatu karya. Content Based Image Retrieval (CBIR) Citra Bunga adalah engine pada komputer untuk melakukan pencarian gambar berdasarkan gambar pada database. Penelitian pada Content Based Image Retrieval (CBIR) Citra Bunga telah dilakukan oleh banyak peneliti. Permasalahan terjadi ketika memilih metode pendekatan seperti preprocessing, ekstraksi fitur dan similarity measure pada CBIR Citra Bunga. Pendekatan yang tidak sesuai dengan data yang diuji, tidak akan memberikan hasil yang optimal. Untuk mengetahui tingkat keberhasilan pendekatan yang digunakan pada CBIR Citra Bunga, digunakan perhitungan nilai precision. Pada penelitian ini, dataset yang akan digunakan adalah dataset Oxford Flower 17. Berdasarkan penelitian sebelumnya, untuk mendapatkan nilai precision yang lebih baik, penelitian ini akan menggunakan ekstraksi fitur warna Hue Saturation Value (HSV), ekstraksi fitur tekstur Gray Level Co-occurrence Matrix (GLCM), dan gabungan kedua fitur dengan pendekatan histogram. Pada penelitian CBIR Citra Bunga ini, terdapat tiga proses yaitu segmentasi menggunakan thresholding, proses ekstraksi fitur, dan pengukuran tingkat kemiripan citra dengan Euclidean Distance. Pengujian pada sistem dilakukan berdasarkan citra yang tersegmentasi dan tidak tersegmentasi. Pengujian sistem dengan hasil Mean Average Precision (MAP) terbesar dihasilkan oleh proses ekstraksi fitur GLCM tidak tersegmentasi sebesar 87,32%, dan untuk nilai MAP terbesar pada citra tersegmentasi dihasilkan pada proses ekstraksi fitur HSV sebesar 83,35%. Kata kunci: Content Based Image Retrieval, ekstraksi fitur HSV, ekstraksi fitur GLCM, thresholding, Euclidean Distance, Mean Average Precision (MAP);--- Searching images based on images in the database, often done to overcome duplication of a work. Content Based Image Retrieval (CBIR) Flower Image is the engine on the computer To perform image-based image search on the database. Research on Content Based Image Retrieval (CBIR) Flower Image has been done by many researchers. Problems occur when choosing approaches such as preprocessing, feature extraction and similarity measure in CBIR Flower Image. Approaches which don't correspond with the data image test, would not provide optimal results. To know the success rate of approach used in CBIR Flower Image, the calculation of precision value is used. In this study, the dataset that will be used is dataset Oxford Flower 17. Based on previous research, to get better precision value, this research will use Hue Saturation Value (HSV) feature extraction, feature extraction of Gray Level Co-occurrence Matrix (GLCM) texture, and combination of both features with histogram approach. In this research, there are three processes: segmentation using thresholding, feature extraction process, and measurement of image similarity level with Euclidean Distance. For testing the system, is based on segmented image and non-segmented image. The result of the largest Mean Average Precision (MAP) produced in this study, resulted from the process of unsegmented image by the GLCM feature extraction of 87.32%, and for the largest MAP value in the segmented image produced by the HSV feature extraction process of 83.35%. Keywords: Content Based Image Retrieval, feature extraction HSV, feature extraction GLCM, thresholding, Euclidean Distance, Mean Average Precision (MAP)

Item Type: Skripsi,Tesis,Disertasi (S1)
Additional Information: No. Panggil : S KOM PUT p-2017; Pembimbing : I. Harsa Wara, yaya Wihardi; NIM. 1304309.
Uncontrolled Keywords: : Content Based Image Retrieval, ekstraksi fitur HSV, ekstraksi fitur GLCM, thresholding, Euclidean Distance, Mean Average Precision (MAP), Content Based Image Retrieval, feature extraction HSV, feature extraction GLCM, thresholding, Euclidean Distance, Mean Average Precision (MAP)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Fakultas Pendidikan Matematika dan Ilmu Pengetahuan Alam > Program Studi Ilmu Komputer
Depositing User: Mrs Dian Arya
Date Deposited: 16 Aug 2018 08:29
Last Modified: 16 Aug 2018 08:29
URI: http://repository.upi.edu/id/eprint/30783

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