Implementasi Naive Bayes Classifier dalam Analisa Rekrutmen di PT. Mitra Pinasthika Mulia

Ardian Teja, Silvia Rostianingsih, Alvin Nathaniel Tjondrowiguno

Abstract


PT. Mitra Pinasthika Mulia is a company engaged in the distribution of motorcycles, spare parts and accessories that require applications for applicant analysis or prediction. At present, PT. Mitra Pinasthika Mulia get applicants in several ways, namely from applicant's letter, website, job fair, and job street. There are many applicant data entered and that data is still manually selected. But this is not effective because it will require time and effort, therefore this application was made to help speed up the applicant selection process by providing predictive values to each applicant. The method used is one of the data mining methods, which is Naive Bayes Classifier. The Naive Bayes Classifier method is one method of classification or prediction with a model for calculating probabilities from a category that has existing attribute parameters, and determines which category is the most optimal. The attribute parameters used to measure applicants are age, gender, experience, education, language skills, identity, and applied position. The results of this study showed best f-score of 45,9% and an accuracy of 97,25% for predictions of acceptance, and best accuracy rate of 96,87% for predictions of work position.

Keywords


data mining; naive bayes classifier; classification; f-score

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References


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