KLASIFIKASI DATA MAHASISWA UNTUK MEREKOMENDASIKAN KELOMPOK STUDI MAHASISWA MENGGUNAKAN ALGORITMA NAIVE BAYES
Students have an important role in the running of an education at the university level. As a student, developing soft skills and hard skills is very important. These two things are the main points in self-development and also to recommend students related to student study groups at the UPN Veteran J...
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2023-07-13.
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| 100 | 1 | 0 | |a Alfian Pratama, . |e author |
| 245 | 0 | 0 | |a KLASIFIKASI DATA MAHASISWA UNTUK MEREKOMENDASIKAN KELOMPOK STUDI MAHASISWA MENGGUNAKAN ALGORITMA NAIVE BAYES |
| 260 | |c 2023-07-13. | ||
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| 500 | |a http://repository.upnvj.ac.id/25314/9/RIWAYAT%20HIDUP.pdf | ||
| 500 | |a http://repository.upnvj.ac.id/25314/10/LAMPIRAN.pdf | ||
| 500 | |a http://repository.upnvj.ac.id/25314/11/HASIL%20PLAGIARISME.pdf | ||
| 500 | |a http://repository.upnvj.ac.id/25314/12/ARTIKEL%20KI.pdf | ||
| 520 | |a Students have an important role in the running of an education at the university level. As a student, developing soft skills and hard skills is very important. These two things are the main points in self-development and also to recommend students related to student study groups at the UPN Veteran Jakarta Faculty of Computer Science. These problems make a thought to be able to create a group that can accommodate students in developing themselves in lectures. However, because of this, new problems arise for students who do not know which way to go in exploring a particular field. Therefore, to make it easier for students who have doubts in choosing a student study group, in this study a classification was carried out using student data to be able to determine the choice of student study group. In its application, the dataset was obtained through a survey using the Google form platform as many as 136 data from students of the Faculty of Computer Science Batch 2021-2022. The data obtained went through several processes, namely data pre-processing, data cleaning, and data transformation. The processed data is built using the naïve Bayes classification algorithm with the naïve Bayes multinomial type because almost all data has categorical data types. Based on the results of the study, the accuracy obtained by the model and after calculating the accuracy using the confusion matrix on the model, obtained an accuracy of 85.7%, a precision of 100%, and a recall value of 77.7%. Keywords : Classify, Naive Bayes, Student Study Group | ||
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| 690 | |a QA75 Electronic computers. Computer science | ||
| 655 | 7 | |a Thesis |2 local | |
| 655 | 7 | |a NonPeerReviewed |2 local | |
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