Semarang, Indonesia

Time filter

Source Type

Purwanto,Multimedia University | Purwanto,Dian Nuswantoro University | Eswaran C.,Multimedia University | Logeswaran R.,Multimedia University
Applied Intelligence | Year: 2012

The need for improving the accuracy of time series prediction has motivated researchers to develop more efficient prediction models. The accuracy rates resulting from linear models such as linear regression (LR), exponential smoothing (ES) and autoregressive integrated moving average (ARIMA) are not high as they are poor in handling the nonlinear time series data. Neural network models are considered to be better in handling such nonlinear time series data. In the real-world problems, the time series data consist of complex linear and nonlinear patterns and it may be difficult to obtain high prediction accuracy rates using only linear or neural network models. Hybrid models which combine both linear and neural network models can be used to obtain high prediction accuracy rates. In this paper, we propose an enhanced hybrid model which indicates for a given input data which choice is better between the two options, namely, a linear-nonlinear combination or a nonlinear-linear combination. The appropriate combination is selected based on a linearity test of data. From the experimental results, it is found that the proposed hybrid model comprising linear-nonlinear combination performs better than other models for the data that have a linear relationship. On the contrary, the hybrid model comprising nonlinear-linear combination performs better than other models for the data that have a nonlinear relationship. © 2012 Springer Science+Business Media, LLC.


Ernawan F.,Dian Nuswantoro University | Abu N.A.,University Technical Malaysia Melaka | Suryana N.,University Technical Malaysia Melaka
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2011

Speech recognition is still a growing field. It carries strong potential in the near future as computing power grows. Spectrum analysis is an elementary operation in speech recognition. Fast Fourier Transform (FFT) is the traditional technique to analyze frequency spectrum of the signal in speech recognition. Speech recognition operation requires heavy computation due to large samples per window. In addition, FFT consists of complex field computing. This paper proposes an approach based on discrete orthonormal Tchebichef polynomials to analyze a vowel and a consonant in spectral frequency for speech recognition. The Discrete Tchebichef Transform (DTT) is used instead of popular FFT. The preliminary experimental results show that DTT has the potential to be a simpler and faster transformation for speech recognition. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).


Ernawan F.,Dian Nuswantoro University | Ernawan F.,Hang Tuah University | Abu N.A.,Hang Tuah University | Suryana N.,Hang Tuah University
2013 International Conference of Information and Communication Technology, ICoICT 2013 | Year: 2013

The human visual systems are able to perceive various intensity of the colour image while it is not able to respond small different colour signals of the image. The sensitivity of the color image can be measured by a psychovisual threshold. A psychovisual threshold represents the sensitivity of the human eye to the image. The quantization tables are obtained to determine psychovisual threshold that can be perceived visually by the human eye. This paper presents a generating of the quantization tables from a psychovisual threshold on gray-scale TMT image compression. This paper introduces the concept of psychovisual threshold into TMT image compression. TMT image compression has been shown to perform better than the standard JPEG image compression. This model has been implemented on TMT image compression. The experiment results show that a psychovisual threshold for TMT basis function provides better image compression performance. © 2013 IEEE.


Ernawan F.,Dian Nuswantoro University | Abu N.A.,University Technical Malaysia Melaka | Rahmalan H.,University Technical Malaysia Melaka
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2012

Currently, mobile image applications spend a lot of computing process to display images. A true color raw image contains billions of colors and it consumes high computational power in most mobile image applications. At the same time, mobile devices are only expected to be equipped with lower computing process and minimum storage space. Image dithering is a popular technique to reduce the numbers of bit per pixel at the expense of lower quality image displays. This paper proposes a novel approach on image dithering using 2x2 Tchebichef moment transform (TMT). TMT integrates a simple mathematical framework technique using matrices. TMT coefficients consist of real rational numbers. An image dithering based on TMT has the potential to provide better efficiency and simplicity. The preliminary experiment shows a promising result in term of error reconstructions and image visual textures. © 2012 SPIE.


Ernawan F.,Dian Nuswantoro University | Ernawan F.,University Technical Malaysia Melaka | Abu N.A.,University Technical Malaysia Melaka | Suryana N.,University Technical Malaysia Melaka
Advanced Science Letters | Year: 2014

JPEG baseline coding is a popular basic image compression technique. The JPEG-3 image compression is an extension of standard JPEG image compression which uses a quality factor to scale quantization table. In order to achieve high compression rate, the JPEG-3 image compression uses lower quality factor. The lower bit rates of image compression using scaling factor will degrade the visual quality on the images being compressed. This paper proposes a high compression scheme based on psychovisual threshold as an adaptive image compression. The comparison between JPEG-3 image compression using the typical quality factor and an adaptive psychovisual threshold has been done. The experimental results of an adaptive quantization tables based on psychovisual threshold show an improvement on the quality image reconstruction at the lower average bit length of Huffman code. © 2014 American Scientific Publishers All rights reserved.


Wahono R.S.,Dian Nuswantoro University | Wahono R.S.,University Technical Malaysia Melaka | Herman N.S.,University Technical Malaysia Melaka
Advanced Science Letters | Year: 2014

Recently, software defect prediction is an important research topic in the software engineering field. The accurate prediction of defect prone software modules can help the software testing effort, reduce costs, and improve the software testing process by focusing on fault-prone module. Software defect data sets have an imbalanced nature with very few defective modules compared to defect-free ones. The software defect prediction performance also decreases significantly because the dataset contains noisy attributes. In this research, we propose the combination of genetic algorithm and bagging technique for improving the performance of the software defect prediction. Genetic algorithm is applied to deal with the feature selection, and bagging technique is employed to deal with the class imbalance problem. The proposed method is evaluated using the data sets from NASA metric data repository. Results have indicated that the proposed method makes an impressive improvement in prediction performance for most classifiers. © 2014 American Scientific Publishers All rights reserved.


Ernawan F.,University Technical Malaysia Melaka | Ernawan F.,Dian Nuswantoro University | Abu N.A.,University Technical Malaysia Melaka | Suryana N.,University Technical Malaysia Melaka
Advanced Science Letters | Year: 2014

Colour image carries a certain amount of perceptual redundancy for the human eyes. The human eye is capable of perceiving various levels of colours. The sensitivity of human eye is useful for perceptual visual quality image in image compression. The visual sensitivity of the colour image in terms of image compression can be measured by a psychovisual threshold to generate the quantization tables. This paper will investigate a psychovisual threshold level for Tchebichef moment transform (TMT) from the contribution of its moments. In this paper presents a new technique to generate quantization table for TMT image compression based on psychovisual error threshold. The experimental results show that these new finer quantization tables based on psychovisual threshold for TMT provides better quality image and lower average bit length of Huffman code than previously proposed TMT quantization. © 2014 American Scientific Publishers All rights reserved.


Basari A.S.H.,Hang Tuah University | Hussin B.,Hang Tuah University | Ananta I.G.P.,Hang Tuah University | Zeniarja J.,Dian Nuswantoro University
Procedia Engineering | Year: 2013

Nowadays, online social media is online discourse where people contribute to create content, share it, bookmark it, and network at an impressive rate. The faster message and ease of use in social media today is Twitter. The messages on Twitter include reviews and opinions on certain topics such as movie, book, product, politic, and so on. Based on this condition, this research attempts to use the messages of twitter to review a movie by using opinion mining or sentiment analysis. Opinion mining refers to the application of natural language processing, computational linguistics, and text mining to identify or classify whether the movie is good or not based on message opinion. Support Vector Machine (SVM) is supervised learning methods that analyze data and recognize the patterns that are used for classification. This research concerns on binary classification which is classified into two classes. Those classes are positive and negative. The positive class shows good message opinion; otherwise the negative class shows the bad message opinion of certain movies. This justification is based on the accuracy level of SVM with the validation process uses 10-Fold cross validation and confusion matrix. The hybrid Partical Swarm Optimization (PSO) is used to improve the election of best parameter in order to solve the dual optimization problem. The result shows the improvement of accuracy level from 71.87% to 77%. © 2013 The Authors.


Ernawan F.,Dian Nuswantoro University | Noersasongko E.,Dian Nuswantoro University | Abu N.A.,University Technical Malaysia Melaka
Proceedings - 1st International Conference on Informatics and Computational Intelligence, ICI 2011 | Year: 2011

Traditionally, speech recognition requires large computational windows. This paper proposes an approach based on 256 discrete orthonormal Tchebichef polynomials for efficient speech recognition. The method uses a simplified set of recurrence relation matrix to compute within each window. Unlike the Fast Fourier Transform (FFT), discrete orthonormal Tchebichef transform (DTT) provides simpler matrix setting which involves real coefficient number only. The comparison among 256 DTT, 1024 DTT and 1024 FFT has been done to recognize five vowels and five consonants. The experimental results show the practical advantage of 256 Discrete Tchebichef Transform in term of spectral frequency and time taken of speech recognition performance. 256 DTT produces frequency formants relatively identical similar output with 1024 DTT and 1024 FFT in term of speech recognition. The 256 DTT has a potential to be a competitive candidate for computationally efficient dynamic speech recognition. © 2011 IEEE.


Setyono A.,Dian Nuswantoro University
International Journal of Interactive Mobile Technologies | Year: 2015

This paper presents an adaptive MMS framework to build a mobile telemedicine system for sending multimedia messages and for streaming audio and video files. With the proposed framework, the capability of MMS can be increased to send and receive multimedia files of larger sizes without degrading the quality of data which is not possible with the present MMS system. This feature is particularly important for telemedicine environments as a good quality of multimedia data is required to accomplish accurate medical diagnosis. The experimental results demonstrate that the proposed framework outperforms the existing MMS system with regard to the file size that can be transmitted and to the quality of data. The modified MMS framework is adaptive in the sense that it allows transmission of different sizes and types of multimedia messages. This framework can form the basis for developing a practical mobile telemedicine system.

Loading Dian Nuswantoro University collaborators
Loading Dian Nuswantoro University collaborators