Comparison of C4.5 and Naïve Bayes Algorithms for Assessment of Public Complaints Services - Martin Martin; Lala Nilawati (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 2021)
Public service is one type of service provided by the government. The National Commission on Human Rights as a state institution, one of its functions is to provide services for complaints of cases of human rights violations. The purpose of this study was to find the most appropriate algorithm method by looking at the results of the accuracy and the Area Under Curve (AUC) value. The data used is data from questionnaires regarding assessments related to complaints of cases of human rights violations by the public in 2018, totaling 1750 records. The data is processed using the C4.5 algorithm and Naïve Bayes with the Rapid Miner tools. The results showed that the C4.5 Algorithm has a better accuracy of 99.49% compared to Naïve Bayes of 95.66%. The AUC value produced by the C4.5 algorithm is better at 0.998 and Naïve Bayes by 0.996. In this study, the rule generated by C4.5 will be the basis for making a questionnaire assessment application in the form of visual programming, to help provide an assessment of the satisfaction of complaint services at Komnas HAM. The system is built based on web, using PHP framework, database using MySQL and editor tools using notepad ++.