Browsing by Author "Salau-Ibrahim, T.T."

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    Design and Implementation of Electronic Election Campaign System
    (College of Natural Sciences, Al-Hikmah University, Ilorin - Al-Hikmah Journal of Pure and Applied Sciences. 2(2), 122 – 129, 2016) Salau-Ibrahim, T.T.; Abdulsalam, S.O.
    Researches have been conducted over the years till date on the importance of election campaign in the fields of electoral studies, political studies and the use of information systems to assist the process. The use of computer technology in developing information systems to support several aspects of election campaign has also continued to evolve. System analysis best practices on the use of qualitative research methodology, Unified Modelling Language (UML) to enhance communication as well as rapid prototyping tools were explored and those well suited were used effectively. The emphasis of this work is to deduce how to provide computer support to improve gathering, analysis of data and information from face-to-face canvassing as well as conducting effective get-out-the-vote (GOTV) activities before and on election day. The system will be web based in order to ease communication and user interaction with the system. As one might expect for web based systems, Hyper Text Markup Language (HTML) will be used to display results in the web browser that will be visible to the user. Therefore, in this work, PHP Hypertext Preprocessor (PHP) was used for coding the core logic of the system and this runs on Apache web server, JavaScript for validation and printing facility, cascading style sheet (CSS) and lastly MySQL for database all residing on Apache web server. The present application will assist campaign managers, staff, as well as volunteers to carry out their duties more accurately and effectively. Keywords: Information Systems, System Analysis, Software Engineering, Election Campaign, Campaign Violence, face-to- face canvassing
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    Knowledge Discovery from Educational Database Using Apriori Algorithm
    (Georgian Technical University an Niko Muskhelishvili Institute of Computational Mathematics, Georgia - Georgian Electronic Scientific Journals (GESJ): Computer Science and Telecommunications, 1(51): 41 – 51, 2017) Abdulsalam, S.O.; Hambali, M.A.; Salau-Ibrahim, T.T.; Saheed, Y.K.; Babatunde, A.N.
    Ability to predict student’s performance has become very crucial in educational environments and plays important role in producing the best quality graduates. There are several statistical tools for analyzing students' performance for knowledge discovery from available data. This study presents data mining in educational sector that identifies students’ failure pattern using Apriori algorithm. The results of 20 students in 25 courses taken in their 100 and 200 level of an educational institute in North Central Nigeria were considered as a case study. The patterns discovered were used to provide recommendations to academic planners so as to improve their level of decision making, restructuring of curriculum, and modifying the prerequisites of various courses. This study revealed some interesting patterns in failed courses as some failed courses have a relationship with other failed courses. A data mining software for mining student failed courses was developed, used to mine students result, and the analysis were described. Keywords: Association Rule Mining, Apriori Algorithm, Academic performance, Educational data mining, Curriculum, Educational database, Students' result repository
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    Student’s Performance Analysis Using Decision Tree Algorithms
    (Faculty of Computers and Applied Computer Science, Tibiscus University of Trinisoara, Romania - Annals Computer Science Series Journal, Faculty of Computers and Applied Computer Science, Tibiscus University of Trinisoara, Romania, 15, 55– 62, 2017) Abdulsalam, S.O.; Saheed, Y.K.; Hambali, M.A.; Salau-Ibrahim, T.T.; Babatunde, A.N.
    Educational Data Mining (EDM) is concerns with developing and modeling methods that discover knowledge from data originating from educational environments. This paper presents the use of data mining approach to study students’ performance in CSC207 (Internet Technology and Programming I) a 200 level course in the department of Computer, Library and Information Science. Data mining provides many approaches that could be used to study the students’ performance, classification task is used in this work to evaluate the student’s performance and as there are numbers of approaches that can be used for data classification, including decision tree method. In this work, decision trees were used which include BFTree, J48 and CART. Students’ attribute such as Attendance, Class test, Lab work, Assignment, Previous Semester Marks and End Semester Marks were collected from the students’ management system, to predict the performance at the end of semester examination. This paper also investigates the accuracy of different Decision tree algorithms used. The experimental results show that BFtree is the best algorithm for classification with correctly classified instance of 67.07% and incorrectly classified instance of 32.93%. KEYWORDS: Classification, Decision tree, Students’ Performance, Educational Data Mining