PEMANFAATAN METODE K-NEAREST NEIGHBOR PADA KLASIFIKASI IMAGE BERDASARKAN POLA FITUR DAN TEKSTUR

Authors

  • Nurul Fuad Fakultas Teknik Program Studi Teknik Informatika

DOI:

https://doi.org/10.30736/jti.v2i1.30

Keywords:

Image classification, feature extraction, k-nearest neighbor

Abstract

Content-based image search can use Content Based Image Retrieval (CBIR). CBIR works by measuring the similarity of query images with all the images in the database so that the query cost is directly proportional to the number of images in the database. Limiting the range of image search by way of classification is one way to reduce the query cost on CBIR. Application of K-Nearest Neighbor method aims to classify the image as well as to measure the level of accuracy and time of classification. In this study built a software that can extract the color and texture features of an image by using the Color Histogram method and the Edge Histogram Descriptor. The results of the feature extraction process are then used by the software in the learning process and classification by the K-Nearest Neighbor method. The software is built with structured analysis and design methods then implemented using VB.net programming language The final result of classification is then tested with parameter level accuracy and classification time. The test results show that the combination of color and texture features provides a higher level of accuracy than classification based on features and textures but requires longer classification time.

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Author Biography

Nurul Fuad, Fakultas Teknik Program Studi Teknik Informatika

Dosen Fakultas Teknik Program Studi Teknik Informatika

References

CBIR:Features, http://www.ee.columbia.edu/~xlx/courses/vis-hw3/page2.html, didownload pada April 2007

CBIR: Texture Features, 2007, www.cs.auckland.ac.nz/compsci708s1c/, didownload pada tanggal 11 April 2007

Jiawei Han, Micheline Kamber, 2002, "Data Mining Concept and Techniques", Academic Press

Kenneth R. Castleman, 1996, "Digital Image Processing", Prentice Hall

Maher A. Sid Ahmed, 1995, "Image Processing: Theory, Algorithm and Architecture", McGrawHill

Rafael C. Gonzales, Richard E. Woods, 2002, "Digital Image Processing", Pentice Hall

Sundaram RMD, "Image Mining, Intricacies and Innovations", http://www.amrita.edu/cde/, didownload pada tanggal 11 April 2007

Teknomo, Kardi. K-Nearest Neighbors Tutorial, 2006,http://people.revoledu.com/kardi/tutorial/KNN/,didownload pada tanggal 11 Desember 2006

Uniform Quantization, 2007, http://www.cs.wpi.edu/~matt/courses/cs563/, didownload pada tanggal 11 April 2007

PlumX Metrics

Published

2017-08-28

How to Cite

Fuad, N. (2017). PEMANFAATAN METODE K-NEAREST NEIGHBOR PADA KLASIFIKASI IMAGE BERDASARKAN POLA FITUR DAN TEKSTUR. Joutica : Journal of Informatic Unisla, 2(1). https://doi.org/10.30736/jti.v2i1.30

Issue

Section

Jouticla Jurnal Teknik Informatika