This paper discusses the research design for BMTutor. BMTutor is a prototype for visualizing Malay sentences that is combined with sentence checker, sentence correction and word attribute components. The purpose of BMTutor is to check sentence validation, provide sentence correction for invalid sentence used and produce parse tree visualization. The research design involved can be divided into four phases; categorizing sentence and produce repository (Phase 1), developing models and algorithms (Phase 2); development of a prototype (Phase 3); and prototype testing (Phase 4). To date, this system is the only one designed with the functions and characteristics as in BMTutor. There are two BM parsers to check the validity of simple BM sentences had been developed. Both parsers performed three phases in research design, namely 1) the collection of sentence or CFG, 2) develop a prototype, and 3) conduct evaluation. The phases involved are the basic method in developing a prototype. As a result of the lack of models and algorithms have been introduced in both parsers, the model and algorithm development phase is introduced in the design of BMTutor. Output from the development process shows that the prototype is able to provide sentence correction for all 15 invalid sentences and can produce parse tree visualizations for all 20 sentences used for prototype testing.
Over the last few years, the web has been expanded to serve millions of users for various purposes all over the world. The web content filtering is essential to filter offensive, unwanted web content from web pages, reduced inappropriate content to prevent access to content which could compromise the network and spread malware. It also to tightened network security where web content filtering adds a much-needed layer of security to the network by blocking access to sites that raise an alarm. However, there are lack of comparison between classification techniques in previous studies in order to find the best classifier for the web page classification and the analysis related to it. Thus, the purpose of this study was to apply web page classification techniques and their performances is compared as it is the initial step in data mining before going to web filtering. In this project, three classifiers called Artificial Neural Network, J48 Decision Tree and Support Vector Machine were used to web phishing dataset in order to find the best possible classifier with small computational efforts that will give the best result in classifying the web page.
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