Yogyakarta, Indonesia
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Abdullah Z.,University of Malaysia, Terengganu | Herawan T.,Ahmad Dahlan University | Deris M.M.,University Tun Hussein Onn Malaysia
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

Development of least association rules mining algorithms are very challenging in data mining. The complexity and excessive in computational cost are always become the main obstacles as compared to mining the frequent rules. Indeed, most of the previous studies still adopting the Apriori-like algorithms which are very time consuming. To address this issue, this paper proposes a scalable trie-based algorithm named SLP-Growth. This algorithm generates the significant patterns using interval support and determines its correlation. Experiments with the real datasets show that the SLP-algorithm can discover highly positive correlated and significant of least association. Indeed, it also outperforms the fast FP-Growth algorithm up to two times, thus verifying its efficiency. © 2010 Springer-Verlag.


Abdullah Z.,University of Malaysia, Terengganu | Herawan T.,Ahmad Dahlan University | Deris M.M.,University Tun Hussein Onn Malaysia
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

A research in mining least association rules is still outstanding and thus requiring more attentions. Until now; only few algorithms and techniques are developed to mine the significant least association rules. In addition, mining such rules always suffered from the high computational costs, complicated and required dedicated measurement. Therefore, this paper proposed a scalable model called Critical Least Association Rule (CLAR) to discover the significant and critical least association rules. Experiments with a real and UCI datasets show that the CLAR can generate the critical least association rules, up to 1.5 times faster and less 100% complexity than benchmarked FP-Growth. © 2010 Springer-Verlag Berlin Heidelberg.


Herawan T.,Ahmad Dahlan University | Vitasari P.,National Institute of Technology Bandung | Abdullah Z.,University of Malaysia, Terengganu
International Journal of Continuing Engineering Education and Life-Long Learning | Year: 2013

One of the commonly and popular techniques used in data mining application is association rules mining. The purpose of this study is to apply an enhanced association rules mining method, so called significant least pattern growth (SLP-growth) proposed by Abdullah et al. (2010a) for capturing interesting rules in student suffering language and social anxieties dataset. The datasets were taken from a survey among engineering students in Universiti Malaysia Pahang (UMP). The results of this research will provide useful information for educators to make a decision on their students more accurately, and to adapt their teaching strategies accordingly. It can be helpful to assist students in handling their fear of language and social. Furthermore, it is also useful in increasing the quality of learning. Copyright © 2013 Inderscience Enterprises Ltd.


Sutikno T.,Ahmad Dahlan University | Sutikno T.,University of Technology Malaysia | Idris N.R.N.,University of Technology Malaysia | Jidin A.,Hang Tuah University
Renewable and Sustainable Energy Reviews | Year: 2014

The first and the most important step in solving the environmental problems created by cars with internal combustion engines is research and development of electric vehicles. Selection of a proper drive and optimal control strategy of electric vehicles are the major factors to obtain optimal energy management in order to extend the running distance per battery charge. This paper presents a brief review of direct torque control (DTC) of induction motors (IM) as well as its implementation for electric vehicle (EV) applications. First, the basic DTC technique based on hysteresis controllers will be introduced, and then an overview of the major problems in a basic DTC scheme will be presented and explained, as well as some efforts for improving the technique. The main section presents a critical review of DTC for EV applications, taking into consideration the vehicle mechanics and aerodynamics of electric vehicles. The review is very important to provide guidelines and insights for future research and development on the DTC of IM drives for sustainable reliability and energy efficient EV applications. © 2014 Published by Elsevier Ltd. All rights reserved.


Jidin A.,University Technical Malaysia Melaka | Jidin A.,University of Technology Malaysia | Idris N.R.N.,University of Technology Malaysia | Yatim A.H.M.,University of Technology Malaysia | And 3 more authors.
IEEE Transactions on Industrial Electronics | Year: 2011

A dynamic overmodulation strategy for fast dynamic torque control in direct torque control (DTC)-hysteresis-based induction machine is proposed. The fastest dynamic torque response with a six-step mode can be achieved in the proposed method by switching only the most optimized voltage vector that produces the largest tangential component to the circular flux locus. This paper also discusses the performance of dynamic torque control in basic DTC in order to justify on how the proposed selected voltage vector results in excellent dynamic torque performance. The main benefit of the proposed method is its simplicity, since it only requires a minor modification to the conventional DTC-hysteresis-based structure and does not require a space vector modulator. To verify the feasibility of the proposed dynamic overmodulation strategy, simulation and experimentation, as well as comparison with the conventional DTC scheme, are carried out. Results showed a significant improvement in the dynamic torque response when compared to the conventional DTC-hysteresis-based method. © 2009 IEEE.


Sulisworo D.,Ahmad Dahlan University
International Journal of Emerging Technologies in Learning | Year: 2012

Collaboration has become one of an essential skill necessary for effective functioning in society in the new era. As a consequence, the learning strategy at the higher education should consider this shifting. Web 2.0 technology is a new trend in communication technology that has become a basis of the new generation internet to make it a more mature and distinctive medium of communication. The problem is how to bring the offline learning using cooperative learning based on classroom to the online learning using this wiki. The learning design on this topic will give wide opportunity to access learning that more suitable to the new skill in the new era. Wikispaces is one of wiki facilities that operated in the web based. This wiki is so simple but suitable for collaborative learning. The learning scenario is Jig Saw approach modified to fit in the online collaborative learning environment. This technique, including two different treatments with different small groups in order to help learning and improving cooperation between students. Using this structure, students are responsible for share their skill or knowledge each other material.


Soetedjo H.,Ahmad Dahlan University
Sensors and Transducers | Year: 2011

The detections have been carried out to the nitrocellulose membrane (wet and dry condition) using a self-construction of polarized reflection spectroscopy. The measurement was done by illuminating the sample at an incident angle of 70° using a visible light source and then analyzing the reflected light off the sample received by the detector of spectrometer. From the investigation, the change of water content introduced to the membrane has been observed respects to the observation time. The water molecules present in or left the porous of membrane could be detected through the investigating of reflected intensities obtained from the measurement. Copyright © 2011 IFSA.


Yanto I.T.R.,Ahmad Dahlan University
International Journal of Software Engineering and its Applications | Year: 2013

Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. In this paper, we study on the problem of clustering categorical data, where data objects are made up of non-numerical attributes. We propose MECC (Minimum Error Classification Clustering), an alternative technique for categorical data clustering using VPRS taking into account minimum error classification. The technique is implemented in MATLAB. Experimental results on two benchmark UCI datasets show that MECC technique is better than the baseline categorical data clustering techniques with respect to selecting the clustering attribute © 2013 SERSC.


Yanto I.T.R.,Ahmad Dahlan University | Vitasari P.,Universiti Malaysia Pahang | Herawan T.,Ahmad Dahlan University | Deris M.M.,University Tun Hussein Onn Malaysia
Expert Systems with Applications | Year: 2012

Computational models of the artificial intelligence such as rough set theory have several applications. Data clustering under rough set theory can be considered as a technique for medical decision making. One possible application is the clustering of student suffering study's anxiety. In this paper, we present the applicability of variable precision rough set model for clustering student suffering studies anxiety. The proposed technique is based on the mean of accuracy of approximation using variable precision of attributes. The datasets are taken from a survey aimed to identify of studies anxiety sources among students at Universiti Malaysia Pahang (UMP). At this stage of the research, we show how variable precision rough set model can be used to groups student in each study's anxiety. The results may potentially contribute to give a recommendation how to design intervention, to conduct a treatment in order to reduce anxiety and further to improve student's academic performance. © 2011 Elsevier Ltd. All rights reserved.


Sutikno T.,Ahmad Dahlan University | Sutikno T.,University of Technology Malaysia | Idris N.R.N.,University of Technology Malaysia | Jidin A.,University Technical Malaysia Melaka | Cirstea M.N.,Anglia Ruskin University
IEEE Transactions on Industrial Informatics | Year: 2013

This paper presents a novel direct torque control (DTC) approach for induction machines, based on an improved torque and stator flux estimator and its implementation using field-programmable gate arrays (FPGA). The DTC performance is significantly improved by the use of FPGA, which can execute the DTC algorithm at higher sampling frequency. This leads to the reduction of the torque ripple and improved flux and torque estimations. The main achievements are: 1) calculating a discrete integration operation of stator flux using backward Euler approach; 2) modifying a so called nonrestoring method in calculating the complicated square root operation in stator flux estimator; 3) introducing a new flux sector determination method; 4) increasing the sampling frequency to 200 kHz such that the digital computation will perform similar to that of the analog operation; and 5) using two's complement fixed-point format approach to minimize calculation errors and the hardware resource usage in all operations. The design was achieved in VHDL, based on a Matlab/Simulink simulation model. The Hardware-in-the-Loop method is used to verify the functionality of the FPGA estimator. The simulation results are validated experimentally. Thus, it is demonstrated that FPGA implementation of DTC drives can achieve excellent performance at high sampling frequency. © 2005-2012 IEEE.

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