UMPSA CIPTA, CINTA dan CITRA ke arah keunggulan peribadi

Sebagai seorang pensyarah di Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) sejak tahun 2019, adalah menjadi saat indah apabila tercipta sejarah memasuki sebuah universiti gemilang dan terunggul untuk berbakti kepada anak bangsa dan negara

Visualisasi pohon sintaksis berasaskan model dan algoritma sintaks ayat bahasa Melayu

Previous works that produce syntactic tree output has disregarded additional relevant components such as sentence checking, sentence correction, the syntax tree visualization and the words attributes of each sentence. As such, this study aims at producing an algorithm for syntactic tree output enhancement from which the relevant output component mentioned above can be produced. The additional components namely sentence checking, sentence correction, syntax tree visualization (VPS) and word attribute are modelled into a package prior to translating them into a tangible output. In term of rules, previous studies have used phrase-structure rules (RSF) in analysing the Malay sentence. But RSF has been found to be a non-universal formula. Our work has brought us to the introduction of X-bar rules for BM VPS, which consequently becomes one of the contributions of this study. To achieve these objectives (the algorithm, the model and the X-bar rules), five phases of research methods involved namely identifying the research gap, the sentence and rules categorization, model and algorithm design phase, prototype development evaluation and conclusion phase. Parseval assessment method, which is an output evaluation method in natural language processing, was used for the evaluation. Point of analysis were the recall and precision valuation metrics. For VPS output, the average results obtained were 100% for recall and 97.8% for precision. For sentence correction, the results given were 100% for recall and 87.8% for precision. These results proved that the algorithm and model, for syntactic tree output enhancement, are generalisable enough to be tested on other languages. User evaluation on the prototype was also performed yielding in the average subjective satisfaction of 87.9% and a mean score of 6.157, based on semantic differential scales of 1 to 7. Cognitive assessment was also recorded, obtaining average cognitive score of 84.6% with a mean score of 4.230, on the scale 5. Analysis on those results indicated positive scores on the model-based product specifically on usefulness, ease of use, ease of learning, subjective satisfaction, and cognitive measures. It can be concluded that the algorithm and model proposed were useful for the development of the prototype. The prototype is therefore beneficial as an educational assistance to understand Malay sentences when provided with enhanced output on sentence checking, sentence correction, syntax tree visualization (VPS) and words attribut

UMPSA CIPTA, CINTA dan CITRA ke arah keunggulan peribadi

Sebagai seorang pensyarah di Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) sejak tahun 2019, adalah menjadi saat indah apabila tercipta sejarah memasuki sebuah universiti Gemilang dan Terunggul untuk berbakti kepada anak bangsa dan negara. UMPSA adalah tempat kerja pertamaku sebagai seorang pensyarah dan banyak pengalaman dan pengetahuan dapat dipelajari dan ditimba untuk kebaikan diri, keluarga, masyarakat dan negara

BMTutor research design: Malay sentence parse tree visualization

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 (Phase1), 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

Malay declarative sentence: Visualization and sentence correction

Language researchers introduced sentence parse tree visualizations to help in understanding sentence structure, especially in English. Among the applications introduced, phpSyntaxTree and RSyntaxTree give users the opportunity to visualize an English sentence through online interaction.In Malaysia, language research in sentence parse tree visualization for Malay (BM) still hasn’t attracted enough researchers to produce a prototype as has been done in English.However, several parsers for BM sentences have been introduced.The parsers will produce a parse tree as an output from the parsing process. Based on the parsers, methods can be extended to produce sentence parse tree visualizations for BM. Parsers for checking sentence structure need to be included in visualization methods.Visualization methods involved consist of 1) tokenizing, 2) checking the number of words, 3) assigning word class, 4) checking spelling or conjunctions, 5) checking and matching with formula, 6) suggestion or visualization 7) word attributes and 8) visualization from a corpus.A prototype for the methods introduced is still under the development and improvement process.However output from the development process in validating the sentence, giving corrections for incorrect sentences and creating a parse tree has had good output results

Parse tree visualization for Malay sentence (BMTutor)

In Malaysia, various efforts have been made by the government and language researchers in improving student’s ability of mastering Malay language (BM) due to their poor ability in grammar and sentence structure. In terms of technology, to date, there is no computer software or a prototype that is available that can help students in learning the BM sentence structure.Thus, BMTutor is introduced as a solution to this problem. BMTutor is a prototype for visualizing Malay sentence combined with sentence checker, sentence correction and word attribute components.BMTutor is intended to facilitate the learning process of sentence construction and grammatical structure in BM. It is also to enhance the learning process in BM that can be used by communities, especially students. An algorithm in designing BMTutor is discussed in this paper.The algorithm of the software is done sequentially as followed: 1) tokenizing 2) checking the number of words, 3) searching and comparing process to check the spelling or conjunctions, 4) assigning each word with a certain word class, 5) matching with rules, and 6) delivering/producing output (sentence correction or parse tree visualization, word attribute components, and parse tree from sentence examples).Based on the testing conducted, output from the development process shows that the prototype can correct all 15 invalid sentences and can produce parse tree visualization for all 20 sentences

Comparison of syntax tree visualization: Toward Malay Language (BM) syntax tree

This study will analyze natural language syntax tree visualizations to compare visualization methods in order to choose the optimum solution for visualizing a BM syntax tree.Currently no syntax tree visualization for BM has been introduced, and no visualization is yet available in the form of computer software or a prototype.Methods that can be dealt with in creating a BM syntax tree include: tokenizing, a performing search and comparison, matching with the associated rules, and composing.Ten systems were analyzed, and the Link Grammar system was found to be the most viable.The Link Grammar system does not have a hierarchical structure that reflects the language syntax as compared to the SSTC (Structured String-Tree Correspondence) application which does.However, the SSTC shows the tree structure in a hierarchical manner, but it does not have a suitable method to follow in visualizing the BM sentence syntax tree

Comparison of Syntax Tree Visualization: Toward Malay Language (BM) Syntax Tree

Abstract. This study will analyze natural language syntax tree visualizations to compare visualization methods in order to choose the optimum solution for visualizing a BM syntax tree. Currently no syntax tree visualization for BM has been introduced, and no visualization is yet available in the form of computer software or a prototype. Methods that can be dealt with in creating a BM syntax tree include: tokenizing, a performing search and comparison, matching with the associated rules, and composing. Ten systems were analyzed, and the Link Grammar system was found to be the most viable. The Link Grammar system does not have a hierarchical structure that reflects the language syntax as compared to the SSTC (Structured String-Tree Correspondence) application which does. However, the SSTC shows the tree structure in a hierarchical manner, but it does not have a suitable method to follow in visualizing the BM sentence syntax tree

A comparative analysis on artificial intelligence techniques for web phishing classification

Over the last years, the web has beenexpanded 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 maIware, It also to tightened network security where web content filtering adds a much-need layer of security to the network by blocking access to sites that raise an alaQ* 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 it is the initial step in data mining before going to web filtering. In this project, three classifiers called ArCBlial 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