Data Mining Selection of Prospective Government Employees with Employment Agreements using Naive Bayes Classifier

Abstract

The implementation of health in Indonesia is still marked by problems in managing the workforce, especially among honorary health workers. The State Personnel Agency seeks to improve service quality by selecting good human resources to enhance community services. Several Ministries agreed to change the team member recruitment system from Prospective Civil Servants to Government Employees with Work Agreements. In the field, in the Government Employees with Work Agreements acceptance process, there are still pros and cons, both from the rules and the appointment process. The community hopes that the appointment process can be objective and open so that no group is disadvantaged. To achieve these expectations, researchers used data mining to classify health workers who would become Government Employees with Work Agreements. The data mining process uses probability with the Naive Bayes Classifier algorithm from historical data on Government Employees with Work Agreements receipts for 2021. Data on the history of Government Employees with Work Agreements acceptance of health workers as many as 1078 data have been filtered and cleaned. The results of testing the data are 0.00012 for the worthy assumptions and 0.0032 for the unworthy assumptions. Data processing results will be visually displayed using bubble diagrams and Python programming. The researcher concluded that the process of classifying prospective first-aid team members for medical personnel could be done by data mining using the Naïve Bayes algorithm. The results of this classification can be used as a reference for the following year's Government Employees with Work Agreements revenue classification process.

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