International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Comparative Evaluation of Academic Performance in Waikato Environment for Knowledge Analysis Using Multiple Classification Algorithms

Abstract

Data mining (DM) is the process of applying algorithms on large databases with the aim of discovering knowledge that would help in taking informed decisions by the management of academic institutions (Chalurapruek, S, et al, 2018). This paper seeks to discover the best classifiers to be used on educational data when using Waikato Environment for Knowledge Analysis (WEKA). The variables of importance namely carry-over, marital status, age range, entry mode and accommodation location were selected by J45 classifier. Four sampled datasets from four schools/faculties namely School of Physical Sciences (SPS), School of Environmental Studies (SES), School of Technology and Science Education (STSE), School of Agriculture and Agricultural Technology (SAAT) belonging to Modibbo Adama University of Technology (MAUTECH), Yola, Nigeria, were used for the DM task. All the classifiers available in WEKA suite were applied independently on the four different datasets. Notably, classifiers such as J48, NaiveBayes, Logistics and Regression gave better performances when compared with the rest. In the comparative analysis, the Regression model had the overall best performance of 98.366 %, 99.3197 %, 96.3964 % and 96.875 % on the four datasets respectively. The computed average performance of each of the four classifiers on the four datasets gave97.5446 %, 94.6954 %, 95.670125 %, and 97.739275 % respectively.

Country : Nigeria

1 Asabe Sandra Ahmadu PhD2 Etemi Joshua Garba PhD3 Ally Dauda Ahmadu

  1. Department of Computer Science, Modibbo Adama University Yola, Nigeria
  2. Department of Computer Science, Modibbo Adama University Yola, Nigeria
  3. ICT Centre, Federai University Wukari, Nigeria

IRJIET, Volume 5, Issue 7, July 2021 pp. 73-80

doi.org/10.47001/IRJIET/2021.507013

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