Darlis Herumurti
Department of Informatics, Institut Teknologi Sepuluh Nopember

Published : 17 Documents
Articles

Found 3 Documents
Search
Journal : Jurnal Ilmu Komputer dan Informasi

EEG CLASSIFICATION FOR EPILEPSY BASED ON WAVELET PACKET DECOMPOSITION AND RANDOM FOREST Sugianela, Yuna; Sutino, Qonita Luthfia; Herumurti, Darlis
Jurnal Ilmu Komputer dan Informasi Vol 11, No 1 (2018): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Original Source | Check in Google Scholar | Full PDF (360.748 KB)

Abstract

EEG (electroencephalogram) can detect epileptic seizures by neurophysiologists in clinical practice with visually scan long recordings. Epilepsy seizure is a condition of brain disorder with chronic noncommunicable that affects people of all ages. The challenge of study is how to develop a method for signal processing that extract the subtle information of EEG and use it for automating the detection of epileptic with high accuration, so we can use it for monitoring and treatment the epileptic patient. In this study we developed a method to classify the EEG signal based on Wavelet Packet Decomposition that decompose the EEG signal and Random Forest for seizure detetion. The result of study shows that Random Forest classification has the best performance than KNN, ANN, and SVM. The best combination of statisctical features is standard deviation, maximum and minimum value, and bandpower. WPD is has best decomposition in 5th level.
FEATURE SELECTION METHODS BASED ON MUTUAL INFORMATION FOR CLASSIFYING HETEROGENEOUS FEATURES Pawening, Ratri Enggar; Darmawan, Tio; Bintana, Rizqa Raaiqa; Arifin, Agus Zainal; Herumurti, Darlis
Jurnal Ilmu Komputer dan Informasi Vol 9, No 2 (2016): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Original Source | Check in Google Scholar | Full PDF (283.816 KB)

Abstract

Datasets with heterogeneous features can affect feature selection results that are not appropriate because it is difficult to evaluate heterogeneous features concurrently. Feature transformation (FT) is another way to handle heterogeneous features subset selection. The results of transformation from non-numerical into numerical features may produce redundancy to the original numerical features. In this paper, we propose a method to select feature subset based on mutual information (MI) for classifying heterogeneous features. We use unsupervised feature transformation (UFT) methods and joint mutual information maximation (JMIM) methods. UFT methods is used to transform non-numerical features into numerical features. JMIM methods is used to select feature subset with a consideration of the class label. The transformed and the original features are combined entirely, then determine features subset by using JMIM methods, and classify them using support vector machine (SVM) algorithm. The classification accuracy are measured for any number of selected feature subset and compared between UFT-JMIM methods and Dummy-JMIM methods. The average classification accuracy for all experiments in this study that can be achieved by UFT-JMIM methods is about 84.47% and Dummy-JMIM methods is about 84.24%. This result shows that UFT-JMIM methods can minimize information loss between transformed and original features, and select feature subset to avoid redundant and irrelevant features.
LEAST SQUARES SUPPORT VECTOR MACHINES PARAMETER OPTIMIZATION BASED ON IMPROVED ANT COLONY ALGORITHM FOR HEPATITIS DIAGNOSIS Husain, Nursuci Putri; Arisa, Nursanti Novi; Rahayu, Putri Nur; Arifin, Agus Zainal; Herumurti, Darlis
Jurnal Ilmu Komputer dan Informasi Vol 10, No 1 (2017): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Original Source | Check in Google Scholar | Full PDF (205.767 KB)

Abstract

Many kinds of classification method are able to diagnose a patient who suffered Hepatitis disease. One of classification methods that can be used was Least Squares Support Vector Machines (LSSVM). There are two parameters that very influence to improve the classification accuracy on LSSVM, they are kernel parameter and regularization parameter. Determining the optimal parameters must be considered to obtain a high classification accuracy on LSSVM. This paper proposed an optimization method based on Improved Ant Colony Algorithm (IACA) in determining the optimal parameters of LSSVM for diagnosing Hepatitis disease. IACA create a storage solution to keep the whole route of the ants. The solutions that have been stored were the value of the parameter LSSVM. There are three main stages in this study. Firstly, the dimension of Hepatitis dataset will be reduced by Local Fisher Discriminant Analysis (LFDA). Secondly, search the optimal parameter LSSVM with IACA optimization using the data training, And the last, classify the data testing using optimal parameters of LSSVM. Experimental results have demonstrated that the proposed method produces high accuracy value (93.7%) for  the 80-20% training-testing partition.