Klasifikasi Data Mining dalam Memprediksi Kinerja Karyawan dengan Metode Algoritma C4.5 pada Toko Merpati Simpang

Abstract

It has been demonstrated that using computers greatly improves our ability to perform our duties. Information services are vital because, while employee performance may still be predicted manually, the process takes a long time. Data mining technologies, on the other hand, make it easier to anticipate employee success for loyal employees. Employee performance evaluation criteria are necessary in order to increase the accuracy of the assessment results, as Toko Merpati Simpang's employee performance assessments cannot be conducted carelessly. Employee performance has to be analyzed and categorized because up until now, manual employee performance evaluations have only used subjective criteria. The C4.5 Algorithm data mining approach is used in this evaluation of employee performance. The degree of accuracy will be ascertained by comparing these two approaches. Positive and negative emotions are the two categories of sentiment. The aim of this study is to ascertain the degree of accuracy of the comparison between the two tested techniques and to offer information on the quality of one of Toko Merpati Simpang's employee performance assessments using visitor sentiment. The test results will be evaluated using the Rapidminer tool to demonstrate the degree of accuracy for both testing approaches.

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