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Research Articles Issue 1 · 2023 · pp. 37–50 · Issue page

PRESENTING A DECISION TREE-BASED DATA MINING MODEL TO INCREASE THE SECURITY OF USERS' INFORMATION IN THE USE OF ELECTRONIC SERVICES CONSIDERING INTERNET BANKING SYSTEM

MO
Mohammad EHSANIFAR1
FA
Fatemeh DEKAMINI2
CR
Cristi SPULBAR3
RA
Ramona BIRAU4
RO
Robert Dorin FİLİP5
1 Department of Industrial Engineering, Faculty of Engineering, Arak Branch, Islamic Azad University, Arak, Iran
2 Department of Industri al Management, Faculty of Management, Islamic Azad University, Arak, Iran
3 Department of Finance, Banking and Economic A nalysis, Faculty of Economics and Business Administration, University of Craiova, Craiova, Romania
4 Faculty of Economic Science, University Constantin Brancusi, Tg-Jiu, Romania
5 University of Craiova, Doctoral School of Economic Sciences, Craiova, Romania
Corresponding author: [email protected]
DOI https://doi.org/10.38173/RST.2023.25.1.3:37-50
Received 29 December 2022
Revised 4 February 2023
Accepted 07 March 2023
Available Online 15 March 2023
Abstract
THE MAIN GOAL OF THIS RESEARCH STUDY IS TO PRESENT A DATA MINING MO DEL BASED ON A DECISION TREE TO INCREASE THE SECURITY OF USERS' INFORMATION IN THE USE OF ELECTRONIC SERVICES. TO DETERMINE THE SECURITY OF USERS' INFORMATION IN THE USE OF ELECTRONIC SERVICES. THEREFORE, BASED ON THE DATA AVAILABLE IN THE DATABASE OF 4376 CUSTOMERS OF ELECTRONIC SERVICES IN THE BANK OF AGRICULTURE, THE DATA WAS ANALYZED IN THE DATA MINING MODELS OF CLASSICAL DECISION TREE, NAIVE BIZ, RANDOM FOREST, ROOT TREE, DEEP LEARNING AND NEURAL NETWORK. THEREFORE, BY RUNNING THE RAPIDMINER SOFTWARE, THE OUTPUT OF THE MODEL WAS ANALYZED IN THE PARAMETERS OF ACCURACY, CLASSIFICATION ERROR PERCENTAGE, KAPPA COEFFICIENT AND ABSOLUTE ERROR, AND THE RESULTS SHO WED THAT THE CLASSIC DECISION TREE MODEL AND THE RANDOM FOREST MODEL WERE THE BEST WITH 99.42 PERC ENT EACH. HAVE WHILE THE ROOT TREE IS NOT IN A GOOD CONDITION WITH 87.88, IN RELATION TO THE CLASSIFICATION ERROR, THE CONDITIONS OF THE CLASSIC DECISION TREE AND DECISION FOREST MODELS AND THEN THE NEURAL NETWORK MODEL WERE FOUND TO BE MORE SUITABLE THAN OTHER DATA MINING MODELS; IN RELATION TO THE KAPPA ERROR, THE CLASSICAL DECISION TREE AND RANDOM FOREST OBTAINED A MORE FAVORABLE SITUATION THAN OTHER METHODS WITH 0.989, ALSO IN RELATION TO THE ABSOLUTE NORMAL ERROR, THE CLASSICAL DECISION TREE AND RANDOM FOREST EACH SHOWED A LOWER ERROR WITH A RATE OF 0.039.
Keywords
DATA MINING DECISION TREE ELECTRONIC BANKING USER INFORMATION
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