Kebiasaan baharu peluang dan ruang bagi perniagaan dalam talian

Penggunaan teknologi komputer dalam urusan harian telah lama dipraktikkan oleh pelbagai organisasi. Fungsi teknologi komputer telah dipertingkatkan dengan penambahan teknologi sesuai dengan peredaran masa. Bermula dari penggunaan komputer peribadi sehingga kini berubah ke aplikasi mudah alih. Terkini, teknologi jalur lebar, memudahkan jaringan hubungan sama ada setempat dan seluruh dunia tanpa sempadan. Sektor perniagaan tidak ketinggalan dan perlu selari dengan perubahan teknologi supaya boleh berdaya saing dan memudahkan pengurusan harian. Sektor perniagaan yang menggunakan komputer atau aplikasi mudah alih dikenali sebagai perniagaan dalam talian atau lebih terkenal dengan istilah e-Niaga. Segala pengurusan perniagaan bermula dari pengiklanan, pesanan, pembayaran dan penghantaran semuanya diaturkan menggunakan aplikasi komputer sama ada statik atau mudah ali

A survey of statistical approaches for query expansion

A major issue in effective information retrieval is the problem of vocabulary mismatches. The method called query expansion addresses this issue by reformulating each search query with additional terms that better define the information needs of the user. Many researchers have contributed to improving the accuracy of information retrieval systems, through different approaches to query expansion. In this article, we primarily discuss statistical query expansion approaches that include document analysis, search and browse log analyses, and web knowledge analyses. In addition to proposing a comprehensive classification for these approaches, we also briefly analyse the pros and cons of each technique. Finally, we evaluate these techniques using five functional features and experimental settings such as TREC collection and results of performance metrics. An in-depth survey of different statistical query expansion approaches suggests that the selection of the best approach depends on the type of search query, the nature and availability of data resources, and performance efficiency requirements

Students’ Performance Prediction in Higher Education Using Multi-Agent Framework-Based Distributed Data Mining Approach: A Review

An effective educational program warrants the inclusion of an innovative construction that enhances the higher education efficacy in such a way that accelerates the achievement of desired results and reduces the risk of failures. Educational decision support system has currently been a hot topic in educational systems, facilitating the pupil result monitoring and evaluation to be performed during their development. In this literature survey, the authors have discussed the importance of multi-agent systems and comparative machine learning approaches in EDSS development. They explored the relationship between machine learning and multiagent intelligent systems in literature to conclude their effectiveness in student performance prediction paradigm. They used the PRISMA model for the literature review process. They finalized 18 articles published between 2014-2022 for the survey that match the research objectives

Comparison of Statistic Prediction Results in Weka Explorer Interface and Experimenter Environment Interface on Dataset

With the increased interest into data mining as an important tool for data processing and analysis, the researchers are concerned into data mining for real decision making, data mining helps in the organizational decision making, inaccurate information can mislead decisionmakers and cause costly errors. With more data collected for analytical purposes. Techniques data mining through Weka Explorer interface and experimental environment interface into determining the prediction and accuracy using different algorithm ratings to know the performance of best. Study confirm is to categorize data and help users mining useful data and easily identify an appropriate algorithm for an accurate predictive model, to access the best-performing algorithms, minimize errors and minimum time to build models through the Explorer interface and Experimental Environment Interface to get accurate

The development of conceptual KPI model based on balanced scorecard measurement method for tacit knowledge of universities’ academic staff

The academic staffs represent the main knowledge resources in universities. The management and development of academic tacit knowledge, which is acquired through activities such as teaching and research is needed for sustained competitive advantage. However, measuring tacit knowledge of academicians, who are the key individuals in organisations, is difficult due to its intangibility. This may result in problems of identifying and determining the key individuals’ performance. Therefore, this paper proposes the development of a conceptual key performance indicator (KPI) model based on balance scorecard (BSc) approach by measuring the level and performance of tacit knowledge for academic staff in universities. The conceptual KPI model is developed by integrating financial and non-financial measurement indicators. These indicators used in the BSc approach to evaluate the success of a university according to the knowledge resources performance and profits from tacit knowledge

A systematic literature review of an advisory system

The growth of Artificial Intelligence (AI) driven technologies has been proposed as a means to improve the standard of people’s lives. The advent of the advisory system has manifested itself as a significant element in Artificial Intelligence, effectively helping people in various fields. This research presents a systematic literature review of an advisory system. This research initially presents 472 articles by examining the literature between 2015 and 2023. After a meticulous review process, the studies were filtered down to 67 articles for full analysis. This review provides significant contributions to the exploration of advisory systems, specifically in the existing framework of advisory systems, the techniques applied in advisory systems, the domain in which the Artificial Intelligence technique is being applied in the advisory system, and the validation technique used in validating the advisory system. Ultimately, this review contributes to a deeper understanding of an advisory system’s role in Artificial Intelligence and in various domains for optimising an advisory system application

Analysis of K-Mean and X-Mean Clustering Algorithms Using Ontology-Based Dataset Filtering

In the field of computer science, data mining facilitates the extraction of useful knowledge and patterns from a huge amount of data. Various techniques exist in the data mining domain to explore the links, associations, and patterns from data in data warehouses. Among these techniques, clustering is more prominent in analyzing raw and unlabeled data from a large volume of datasets. The clustering mechanism identifies similar features between data objects and arranges them into clusters. In this paper, we have compared the performance of K-Mean and XMean clustering algorithms using two datasets of student enrollment in higher education institutions. Our methodology incorporated ontology to filter the datasets and exploited Rapidminer environment to evaluate the performance of clustering algorithms. The results showed that X-Mean is more suitable for large datasets in terms of discovery and accuracy of clusters

Connecting user emotions and User Experience (UX) among university students during open distance learning

A catastrophic event such as the coronavirus pandemic (COVID -19) could have an impact on the global economy and on people's lives. When the virus COVID -19 began to spread uncontrollably in late February 2020, some schools and universities in the affected countries were closed. The universities have no choice but to use open distance learning (ODL) to stay in business. Therefore, the platform used for ODL should be effective and practical to optimize the learning experience of students. The important aspects include accessibility, practicality, ease of use and usefulness of the platform used. Furthermore, the learning experience would affected the students' emotions in either positive or negative ways. Hence, the aim of this study was to identify the factors that influence the user experience during ODL and how the emotions is connected to the user experience. The results of the multiple linear regression analysis of this study showed that accessibility was the most important factor influencing user experience, followed by desirability and usability of the ODL learning platform. However, the experience was not influenced by gender, age or the institution where the students were enrolled. Besides, poor internet connection causes the negative emotions such as tensed, frustrated and bored to the students and consequently found out that the learning is no longer useful, while positive emotions such as happy and excited would be achieved when the students could appreciate the usability of the ODL platform