Election Voting Trend Prediction System

I. C. Emeto *

Department of Cybersecurity, Federal University of Technology Owerri, Nigeria.

D.C. Elenwo

College of Fisheries and Marine Technology, Lagos, Nigeria.

U.W. Anthony

Department of Cybersecurity, University of Port Harcourt, Choba, Nigeria.

J.C Shawulu

Department of Computer Science, University of Kashere, Gombe, Nigeria.

C.O Ajayi

Department of Computer Science, University of Kashere, Gombe, Nigeria.

A.A Galadima

Department of Cybersecurity, Federal University of Technology Owerri, Nigeria.

*Author to whom correspondence should be addressed.


Abstract

This paper presents the use of Bayesian networks and K-Nearest Neighbor algorithms for predicting election results. Our motivation stemmed from the complexities of the election data available, which spans over 120,000 voting locations across 36 states in Nigeria, and the requirement to develop a procedure that takes into consideration voter trends that are influenced by political parties seeking to win. The system architecture's translation was utilised, and the prototyping methodology was adopted. In order to realize the requirements, the system was designed and implemented using Java and MySQL in accordance with specifications. Since the outcome is positive, it can serve as a benchmark for further study in this field, particularly when it comes to using data mining tools to analyze election results.

Keywords: K-Nearest neighbour, bayesian network, election, voters, prototyping methodology


How to Cite

Emeto, I. C., D.C. Elenwo, U.W. Anthony, J.C Shawulu, C.O Ajayi, and A.A Galadima. 2024. “Election Voting Trend Prediction System”. Asian Journal of Research in Computer Science 17 (10):37-44. https://doi.org/10.9734/ajrcos/2024/v17i10508.