ALUMNI JOB WAITING PERIOD PREDICTION USING NAÏVE BAYES CLASSIFIER AT COMPUTER SCIENCE STUDY PROGRAM UNIVERSITY OF NUSA CENDANA

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Rachmadiansyah Rachmadiansyah
Nelci Dessy Rumlaklak
Arfan Yeheskiel Mauko

Abstract

In a global era that is full of challenges, universities are expected to produce quality graduates in order to compete in the world of work. One indicator that can be used to assess the quality of graduates is the job waiting period. In this research, the researcher implements Naïve Bayes Classifier method using the RapidMiner 7.3 app to generate predictions for the job waiting period and the accuracy rate of the prediction results obtained. The data in this research were obtained from the results of the Tracer Study questionnaire distributed by Computer Science Study Program at The University of Nusa Cendana to determine the career achievements of alumni. The attributes used in this research are Study Period, Grade Point Average (GPA), Organizational Participation, and Competency Mastery with Waiting Period classes which are divided into 4, namely ≤ 10 months, 11 months - 2 years 1 month, 2 years 2 months - 3 years 4 months, and > 3 years 4 months. The prediction results of the job waiting period obtained are presented in the form of a confusion matrix with an accuracy rate of 81.82%.

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How to Cite
[1]
R. Rachmadiansyah, N. Rumlaklak, and A. Mauko, “ALUMNI JOB WAITING PERIOD PREDICTION USING NAÏVE BAYES CLASSIFIER AT COMPUTER SCIENCE STUDY PROGRAM UNIVERSITY OF NUSA CENDANA”, jicon, vol. 10, no. 2, pp. 143-150, Sep. 2022.
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