PREDIKSI MASA TUNGGU KERJA ALUMNI MENGGUNAKAN NAÏVE BAYES CLASSIFIER PADA PROGRAM STUDI ILMU KOMPUTER UNIVERSITAS NUSA CENDANA - Rachmadiansyah Rachmadiansyah; Nelci Dessy Rumlaklak; Arfan Yeheskiel Mauko (J-ICON : Jurnal Komputer dan Informatika, 2022)
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%.