Decision Support System in Managerial Decision Making: A Comparative Study Between Public and Private Sectors in Malaysia

Nowadays, computers are useful tools for managers whether top manager, middle manager or lower manager in any organisation, and information is a vital asset in every modern organisation. So, computers and information are widely used in any purpose of applications. Decision support system (DSS) is one of the computer-based information systems that provides a flexible tool for analysis and also help managers in semi-structured decision making tasks. This study was done to evaluate the usage of DSS and to make comparison between the public and the private sectors in managerial decision making. Comparisons were made based on the seven hypotheses of the study in which management in the private sector has more concern in using DSS than management in the public sector. Regarding the above hypotheses, the study emphasised on the understanding of the usage and current status of DSS in Malaysia, managerial perception, attitudes towards DSS software products, problems and implementation strategies of DSS in the management. Respondents of the study were the middle level managers. Questionnaires were distributed to the selected organisations in Klang Valley. Based on this study, it could be concluded that there were no significant differences between the public and private sectors in terms of experience, effect of DSS use, attitudes towards DSS software products, satisfaction with DSS, problems with DSS use, and implementation strategies. However, there was a significant difference between the public and private sectors in terms of the frequency of using DSS. The public sector tended to irregularly use DSS in their decision making whereas private sector used DSS systematically. Many of the respondents have yet to use DSS because of the lack of knowledge about DSS and not enough support from the top management. Top management must make the new technology available for their employees to use with the necessary hardware, software and DSS prototypes. The adoption of DSS would encourage users to experiment with new ways of working to improve decision making and increase productivity. Overall satisfaction can be fostered by applying DSS to less structured tasks which are formed by users with favourable attitude towards DSS

Perceptions of selected Malaysian information systems practitioners towards software prototyping: an exploratory study

Studies the perceptions of the Malaysian IS practitioners about the use, applicability, problems, and benefits of prototyping approach in the development of software systems. This was accomplished by undertaking an exploratory survey among selected information systems practitioners. The results indicate that the adoption of prototyping approach is relatively a new concept to the Malaysian practitioners. It was found that prototyping models are not generally thrown away, and prototyping in third generation languages is common. Prototyping approach was further applied to develop a wide variety of applications ranging from real time to traditional business systems. Some of these findings are contrary to the existing literature on prototyping. Hence, despite the survey's restriction to small sample, the findings are important to information systems practitioners and academics

Ontology specific visual canvas generation to facilitate sense-making-an algorithmic approach

Ontologies are domain-specific conceptualizations that are both human and machine-readable. Due to this remarkable attribute of ontologies, its applications are not limited to computing domains. Banking, medicine, agriculture, and law are a few of the non-computing domains, where ontologies are being used very effectively. When creating ontologies for non-computing domains, involvement of the non-computing domain specialists like bankers, lawyers, farmers become very vital. Hence, they are not semantic specialists, particularly designed visualization assistance is required for the ontology schema verifications and sense-making. Existing visualization methods are not fine-tuned for non-technical domain specialists and there are lots of complexities. In this research, a novel algorithm capable of generating domain specialists’ friendlier visualization canvas has been explored. This proposed algorithm and the visualization canvas has been tested for three different domains and overall success of 85% has been yielded

Ontology-Based Question Answering System in Restricted Domain

The complexity of natural language presents difficult challenges that traditional Questions and Answers (Q&A) system such as Frequently Asked Questions, relied on the collective predefined questions and answers, unable to address. Traditional Q&A system is unable to retrieve exact answer in response to different kind of natural language questions asked by the user. Therefore, this paper aims to present an architecture of Ontology-based Question Answering (OQA) system, applied to library domain. The main task of OQA system is to parse question expressed in natural language with respect to restricted domain ontology and retrieve the matched answer. Restricted ontology model is designed as a knowledge base to assist the process based on the effective information derived from the questions. In addition, ontology matching algorithm is developed to deal with the questionanswer matching process. A case study is taken from the library of Sultanah Nur Zahirah of Universiti Malaysia Terengganu. A prototype of Sultanah Nur Zahirah Digital Learning ONtologybased FAQ System (SONFAQS) is developed. The experimental result shows that the architecture is feasible and significantly improves man-machine interaction by shortening the searching time

Performance analysis in text clustering using k-means and k-medoids algorithms for Malay crime documents

Few studies on text clustering for the Malay language have been conducted due to some limitations that need to be addressed. The purpose of this article is to compare the two clustering algorithms of k-means and k-medoids using Euclidean distance similarity to determine which method is the best for clustering documents. Both algorithms are applied to 1000 documents pertaining to housebreaking crimes involving a variety of different modus operandi. Comparability results indicate that the k-means algorithm performed the best at clustering the relevant documents, with a 78% accuracy rate. K-means clustering also achieves the best performance for cluster evaluation when comparing the average within-cluster distance to the k-medoids algorithm. However, k-medoids perform exceptionally well on the Davis Bouldin index (DBI). Furthermore, the accuracy of k-means is dependent on the number of initial clusters, where the appropriate cluster number can be determined using the elbow method