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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Informatika dan Sistem Informasi
Alexander Alexander
Bina Nusantara University
Computer Science & IT Decision Sciences, Operations Research & Management

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Wavelet-Based Color Histogram on Content-Based Image Retrieval Alexander Alexander; Jeklin Harefa; Yudy Purnama; Harvianto Harvianto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 3: June 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i3.7771

Abstract

The growth of image databases in many domains, including fashion, biometric, graphic design, architecture, etc. has increased rapidly. Content Based Image Retrieval System (CBIR) is a technique used for finding relevant images from those huge and unannotated image databases based on low-level features of the query images. In this study, an attempt to employ 2nd level Wavelet Based Color Histogram (WBCH) on a CBIR system is proposed. Image database used in this study are taken from Wang’s image database containing 1000 color images. The experiment results show that 2nd level WBCH gives better precision (0.777) than the other methods, including 1st level WBCH, Color Histogram, Color Co-occurrence Matrix, and Wavelet texture feature. It can be concluded that the 2nd Level of WBCH can be applied to CBIR system.
Comparison Classifier: Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN) In Digital Mammogram Images Jeklin Harefa; Alexander Alexander; Mellisa Pratiwi
Jurnal Informatika dan Sistem Informasi Vol. 2 No. 2 (2016): Jurnal Informatika dan Sistem Informasi
Publisher : Universitas Ciputra Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (363.26 KB)

Abstract

In order to begin the initial check on breast cancer, radiologist can use Computer Aided Diagnosis (CAD) as another option to detect breast cancer. During breast cancer check, human error is often to affecting the result. Several research before have proved that CAD is able to detect breast cancer spot more accurate. The purpose of this research is to find reliable method to classify breast cancer abnormalities. Mammography Image Analysis Society (MIAS) database is used as the sample data to the proposed system in this research. Mammograms are divided into three categorize which are normal, benign and malignant according to MIAS database. Features included in this experiment are extracted by using gray level co-occurrence matrices (GLCM) at 0º, 45º, 90º and 135º with a block size of 128x128. In classification process, this research attempt to compare k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) classifier in order to achieve the better accuracy. The result shows that SVM outperforms KNN in breast cancer abnormalities classification with 93.88% accuracy.
Co-Authors Harvianto Harvianto Jeklin Harefa Mellisa Pratiwi Yudy Purnama