scispace - formally typeset
Search or ask a question
Author

Wirawati Dewi Ahmad

Bio: Wirawati Dewi Ahmad is an academic researcher. The author has contributed to research in topics: Scholarship & Ensemble learning. The author has an hindex of 1, co-authored 2 publications receiving 6 citations.

Papers
More filters
Journal ArticleDOI
TL;DR: This study found that the classification model from SVM algorithm provided the best result with 86.45% accuracy to correctly classify ‘Eligible’ status of candidates, while RT was the weakest model with the lowest accuracy rate for this purpose.
Abstract: Scholarship is a financial facility given to eligible students to extend Higher Education. Limited funding sources with the growing number of applicants force the Government to find solutions to help speed up and facilitate the selection of eligible students and then adopt a systematic approach for this purpose. In this study, a data mining approach was used to propose a classification model of scholarship award result determination. A dataset of successful and unsuccessful applicants was taken and processed as training data and testing data used in the modelling process. Five algorithms were employed to develop a classification model in determining the award of the scholarship, namely J48, SVM, NB, ANN and RT algorithms. Each model was evaluated using technical evaluation metric , such contingency table metrics, accuracy, precision , and recall measures. As a result, the best models were classified into two different categories: The best model classified for ‘Eligible’ status, and the best model classified for ‘Not Eligible’ status. The knowledge obtained from the rules-based model was evaluated through knowledge analysis conducted by technical and domain experts. This study found that the classification model from SVM algorithm provided the best result with 86.45% accuracy to correctly classify ‘Eligible’ status of candidates, while RT was the weakest model with the lowest accuracy rate of for this purpose, with only 82.9% accuracy. The model that had the highest accuracy rate for ‘Not Eligible’ status of scholarship offered was NB model, whereas SVM model was the weakest model to classify ‘Not Eligible’ status. In addition, the knowledge analysis of the decision tree model was also made and found that some new information derived from the acquisition of this research information may help the stakeholders in making new policies and scholarship programmes in the future.

5 citations

Journal ArticleDOI
TL;DR: An ensemble knowledge model is proposed to support the scholarship award decision made by the organization and generates list of eligible candidates to reduce human error and time taken to select the eligible candidate manually.
Abstract: The role of higher learning in Malaysia is to ensure high quality educational ecosystems in developing individual potentials to fulfill the national aspiration. To implement this role with success, scholarship offer is an important part of strategic plan. Since the increasing number of undergraduates’ student every year, the government must consider to apply a systematic strategy to manage the scholarship offering to ensure the scholarship recipient must be selected in effective way. The use of predictive model has shown effective can be made. In this paper, an ensemble knowledge model is proposed to support the scholarship award decision made by the organization. It generates list of eligible candidates to reduce human error and time taken to select the eligible candidate manually. Two approached of ensemble are presented. Firstly, ensembles of model and secondly ensembles of rule-based knowledge. The ensemble learning techniques, namely, boosting, bagging, voting and rules-based ensemble technique and five base learners’ algorithm, namely, J48, Support Vector Machine (SVM), Artificial Neuron Network (ANN), Naive Bayes (NB) and Random Tree (RT) are used to develop the model. Total of 87,000 scholarship application data are used in modelling process. The result on accuracy, precision, recall and F-measure measurement shows that the ensemble voting techniques gives the best accuracy of 86.9% compare to others techniques. This study also explores the rules obtained from the rules-based model J48 and Apriori and managed to select the best rules to develop an ensemble rules-based models which is improved the study for classification model for scholarship award.

1 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: The efforts in narrowing the gap between low relevancy human text descriptions for Malaysian users and image scene color appearances have been brought into attention and the agreement analysis indicates that the Bright category is the most comprehensible by humans and subsequently followed by the Pastel and Dark categories.
Abstract: Institutions that possess certain collections of digital image libraries, such as museums, are progressively interested in making such collections accessible anytime and anywhere for any Image Retrieval (IR) activities namely browsing and searching. Many researchers have shown that IR methods, in filtering images based on their features such as colors, would provide better indexing and can be able to deliver/provide more accurate results. The color composition of an image, e.g. color histogram has proven to be a powerful feature that can be analyzed and used for image indexing because of its robust standardization of image transformation such as scaling and orientation. In this research, the efforts in narrowing the gap between low relevancy human text descriptions for Malaysian users and image scene color appearances have been brought into attention. The methods are first, to investigate the color concepts and color appearance descriptions of a scene and secondly, to identify a set of ground-truth images for each color appearance category. Psychophysical experiments are conducted to determine a collection of ground-truth images that effectively match five color appearance descriptions for image scenes in accordance with human judgement and perception. The results of the experiments are presented together with the inter-rater agreement analysis. These descriptions that are commonly queried by humans are the following keywords, Bright, Pastel, Dull, Pale, and Dark. The agreement analysis indicates that the Bright category is the most comprehensible by humans and subsequently followed by the Pastel and Dark categories. Dull and Pale categories, on the other hand are fairly understood by humans. All the images involved in this research are landscape painting collections from the internet and they are used for academic purposes only. The results show the top ten ground-truth images for each category that encapsulates a high level of agreeability between humans.

5 citations

Journal ArticleDOI
TL;DR: In this paper, a fine-grained analysis of the academic data is proposed to enhance the credibility of the ranking process through the fine-ground analysis of academic data, and the resulting academic rankings with respect to the Research Faculty, Research Productivity, and Research Impact make the academic ranking process more transparent and finegrained.
Abstract: The academic ranking process has considerably evolved in the past fifteen years and the evolution has gained the momentum in last few years. Starting with the holistic rankings of world universities in 2003, it has crossed the milestone of subject-specific rankings. Nevertheless, the academic rankings published by even the reputed ranking entities are facing various criticism, in terms of their transparency, validity, and coverage. This research effort focuses on enhancing the credibility of the ranking process through the fine-grained analysis of the academic data. The proposed fine-grained analysis drives the researcher’s profiles from the Google Scholar Citations repository. While the DBpedia repository is employed for the information about HEIs and countries. The influential researchers are identified using the ResRank methodology. While, for consistent comparison of the subject-specific rankings of global HEIs, the Grand Average Rank (GAR) metric is employed. The resultant academic rankings with respect to the Research Faculty, Research Productivity, and Research Impact make the ranking process more transparent and fine-grained. The analysis also helps in understanding the causes of differences among the academic rankings published by the ARWU, THE, and QS rankings systems. The growing interest in the subject-specific and sub-discipline-specific rankings is irreversible. The fine-grained analysis is a response to the need.

2 citations

Journal ArticleDOI
TL;DR: In this paper, the association rules mining technique is used to mine the implicit patterns of wound up companies by analyzing relationships between attributes such as total asset, total liability and profit and loss.
Abstract: A company is wound up when the company is unable to pay financial debts or is experiencing serious financial distress. From the year 1998 until 2003, an average of 1166 companies were wound up yearly. This research focuses on the knowledge exploration of wound up companies in Malaysia using association rules mining techniques (quantitative) and the involvement of domain expert in knowledge evaluation (qualitative). Association Rules Mining technique is used to mine the implicit patterns of wound up companies by analyzing relationships between attributes such as total asset, total liability and profit and loss. The human expert functions to verify the significant relation between attributes and the mined patterns. This research succeeded to mine 2 quantitative criteria and 9 qualitative criteria related to wound up companies. The criteria combination can be utilized for evaluating the risk of wound up Malaysian companies in the future.

1 citations