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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal ULTIMA Computing
Mutmainnah Muchtar
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Computer Science & IT Control & Systems Engineering Engineering

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Klasifikasi Citra Daun dengan Metode Gabor Co-Occurence Mutmainnah Muchtar; Laili Cahyani
Ultima Computing : Jurnal Sistem Komputer Vol 7 No 2 (2015): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (888.943 KB) | DOI: 10.31937/sk.v7i2.231

Abstract

Plant takes a crucial part in mankind existences. The development of digital image processing technique made the plant classification task become a lot of easier. Leaf is a part of plant that can be used for plant classification where texture of the leaf is a common feature that been used for classification process. Texture offers a unique feature and able to work even when the leaf is damaged or overly big in size which sometimes made the acquisition process become more difficult. This study offers a combination of Gabor filter methods and co-occurrence matrices to produce the most representative features for leaf classification. Classification using SVM with 5-fold cross validation system shows that the proposed Gabor Co-Occurence methods was able to reach average accuracy up to 89.83%. Terms: Leaf, Gabor Co-occurence, Support Vector Machine, Texture
SIMILARITY BASED ENTROPY ON FEATURE SELECTION FOR HIGH DIMENSIONAL DATA CLASSIFICATION Jayanti Yusmah Sari; Mutmainnah Muchtar; Mohammad Zarkasi; Agus Zainal Arifin
Jurnal Ilmu Komputer dan Informasi Vol 7, No 2 (2014): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (255.891 KB) | DOI: 10.21609/jiki.v7i2.263

Abstract

Abstract Curse of dimensionality is a major problem in most classification tasks. Feature transformation and feature selection as a feature reduction method can be applied to overcome this problem. Despite of its good performance, feature transformation is not easily interpretable because the physical meaning of the original features cannot be retrieved. On the other side, feature selection with its simple computational process is able to reduce unwanted features and visualize the data to facilitate data understanding. We propose a new feature selection method using similarity based entropy to overcome the high dimensional data problem. Using 6 datasets with high dimensional feature, we have computed the similarity between feature vector and class vector. Then we find the maximum similarity that can be used for calculating the entropy values of each feature. The selected features are features that having higher entropy than mean entropy of overall features. The fuzzy k-NN classifier was implemented to evaluate the selected features. The experiment result shows that proposed method is able to deal with high dimensional data problem with average accuracy of 80.5%.
Co-Authors Agus Zainal Arifin Jayanti Yusmah Sari Jayanti Yusmah Sari Laili Cahyani Luh Putu Ratna Sundari