Penyelesaian masalah persamaan resapan olakan satu dimensi secara selari dan berjujukan

This article discusses the solution of one-dimensional convection-diffusion equations by using Successive Over-Relaxation Red Black (SORRB) and Gauss-Seidel Red Black (GSRB) method for parallel and sequential. The implementation for this method has been performed on parallel computers for distributed memory systems using Parallel Virtual Machine (PVM) and C which used 18 personal computers with Intel Pentium IV processor. This research successfully results in affirmation of the theories presented by Foster (1995) which proposed that the percentage of efficiency decreases and the speedup factor increases when the numbers of processors are increasing. The research result also shows that the parallelization of GSRB and SORRB method is faster than sequential especially when the number ofprocessors increased. As a conclusion, problem solving by using parallelization will reduce execution time especially in solving large scale problems

Data mining approaches in business intelligence: postgraduate data analytic

Over recent years, there has been tremendous growth of interest in business intelligence (BI) for higher education. BI analysis solutions are operated to extract useful information from a multi-dimensional datasets. However, higher education-based business intelligence is complex to build, maintain and it faces the knowledge constraints. Therefore, data mining techniques provide an effective computational methods for higher educationbased business intelligence. The main purpose of using data mining approaches in business intelligence is to provide decision making solution to higher education management. This paper presents the implementation of data mining approaches in business intelligence using a total of 13508 postgraduates (PG) data. These PG data are to allow the research to identify the postgraduates who Graduate On Time (GOT) via business intelligence process integrating data mining approaches. There are four layers will be discussed in this paper: data source layer (Layer 1), data integration layer (Layer 2), logic layer (Layer 3), and reporting layer (Layer 4). The main scope of this paper is to identify suitable data mining which is to allow decision making on GOT so as to an appropriate analysis to education management on GOT. The results show that Support Vector Machine (SVM) classifier is with better accuracy of 99%. Hence, the contribution of data mining in business intelligence allows an accurate decision making in higher education

Improving gender classification with feature selection in forensic anthropology

Gender classification has been one of the most vital tasks in a real world problem especially when it comes to death investigations. Developing a biological profile of an individual is a crucial step in forensic anthropology process as for the identification of gender. Forensic anthropologists employ the principle of skeleton remains to produce a biological profile. Different parts of skeleton contains different features that will contribute to gender classification. However, not all the features could contribute to gender classification and affect to a low accuracy of gender classification. Therefore, feature selection method is applied to identify the most significant features for gender classification. This paper presents the implementation of feature selection approaches which are Particle Swarm Optimization (PSO) and Harmony Search (HS) algorithm using three different dataset from Goldman Osteometric Dataset, Osteological Collection and George Murray Black Collection. All three dataset contains 4081 samples of metrics measurement and have gone through the process of classification by using Back Propagation Neural Network (BPNN) and Naïve Bayes classifier. The main scope of this paper is to identify the effect of feature selection towards gender classification. The result shows that the accuracy of gender classification for every dataset increased when feature selection is applied to the dataset. Among all the skeleton parts in this experiment, clavicle part achieved the highest increment of accuracy rate which is from 89.76% to 96.06% for PSO algorithm and 96.32% for HS

The effect of social media on researchers’ academic performance through collaborative learning in Malaysian higher education

This study aims to explain the way social media contributes to the enhancement of collaborative learning among researchers Malaysian higher education. The sample comprised of 723 researchers. The findings showed that introvert researchers perceive social media to help in increasing collaborative learning and improving their performance. These researchers are more inclined to communicate through social media as opposed to face-to-face. In addition, the sample researchers are inclined to utilize social media. Therefore, Malaysian higher education institutions are recommended to employ social media in enhancing the researchers’ collaborative learning. The researcher employs the use of theory of technology acceptance model (TAM) for this purpose. The results show that collaborative learning positively and significantly relates to impact intention to use social media for collaborative learning to improve performance of researchers in Malaysian higher education

Hybrid of hierarchical and partitional clustering algorithm for gene expression data

Microarray analysis able to monitor thousands of gene expression data, however, to elucidate the hidden patterns in the data is a complex process. These gene expression data show its imprecision, noise and vagueness due to its high dimensional properties. There are a handful of clustering algorithms have been proposed to extract the important information from the gene expression data. However, identifying the underlying biological knowledge of the data is still hard. To acknowledge these issues, clustering algorithms are used to reduce the data complexity. In this article, hybrid of agglomerative hierarchical clustering and modified k-medoids (partitional clustering) are proposed. Application of the proposed of clustering algorithms to group the genes that have similar functionality which might assist pre-processing procedures. In order to emphasize the quality of the clustering results, cluster quality index (CQI) is determined. Lung and ovary data sets used and the method retrieved a fair clustering with CQI, 0.37 and 0.48 respectively. This research contributes by avoiding biasness toward genes and provide true sense of clustering output using the advantage of hierarchical and partitional clustering methods

Social Media for Collaborative Learning and Engagement: Adoption Framework in Higher Education Institutions in Malaysia

This paper addresses collaborative learning and engagement via intention towards social media use that have been tackled by some researches in terms of its impacts on students’ academic performance. However, only a few of such studies have been carried out in the area of collaborative and engagement use of social media for enhancing researchers’/students’ performance. The present study attempts to determine the way social media can be utilized to enhance researchers’ performance via collaborative and engagement by applying the theory of technology acceptance model (TAM) along with constructivism theory. According to the results, collaborative learning and engagement positively and significantly impact perceived ease of use (PE), perceived usefulness (PU), and intention to use social media (IU) through the social media use in the context of Universiti Teknologi, Malaysia. DOI: 10.5901/mjss.2015.v6n3s1p24

The Effectiveness of Using E-Learning in Malaysian Higher Education: A Case Study Universiti Teknologi Malaysia

E-Learning is a fairly recent word used to define a form of learning that can be performed via websites online learning. The impact of contingent factors on the relationship between six predictors and e-learning effectiveness was investigated. The development and implementation of e-learning today has become an important phases in university. This study is centered on evaluating the e-learning effectiveness in UTM. And in this study, the critical factors affecting e-learning effectiveness were investigated through a survey conducted on students as participants. A total of 268 Universiti Teknologi Malaysia (UTM) (undergraduate students) students were used in the survey. Several factors have been found to correlate with e-learning effectiveness which includes self-efficacy, interface, community, usefulness, students’ satisfaction and intention to use e-learning. The results show that e-learning use positively and significantly related to students' satisfaction, usefulness that is impact intention to use in turn affect e-learning effectiveness. DOI: 10.5901/mjss.2015.v6n5s2p62

Using social media for research: The role of interactivity, collaborative learning, and engagement on the performance of students in malaysian post-secondary institutes

A great deal of research has been conducted regarding the effect of social media on research. However, only a few of these studies have examined the collaborative use of social media as a way to improve the performance of students. Both students and supervisors have expanded their use of social media and understanding how social media can be used to instigate improvements through collaborative learning must be investigated. We conducted a survey of two universities in Malaysia. The goal of this study was to identify the characteristics and factors of social media that improve the academic performance of postsecondary students and students through collaborative learning. Four hundred and twenty-six responses were received from the Malaysian universities surveyed. IBM SPSS and Amos were used to analyze the data. We used a constructivism theory to further explore the data. The results demonstrated that collaborative learning and engagement using social media had a significant and positive affect on the interactions and engagement of members and supervisors of research groups in Malaysian universities