Educational Data Mining untuk Prediksi Kelulusan Mahasiswa Menggunakan Algoritme Naïve Bayes Classifier
The quality of students can be seen from the academic achievements, which are evidence of the efforts made by students. Student academic achievement is evaluated at the end of each semester to determine the learning outcomes that have been achieved. If a student cannot meet certain academic criteria...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Ikatan Ahli Informatika Indonesia
2020-02-01
|
| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | http://jurnal.iaii.or.id/index.php/RESTI/article/view/1502 |
| _version_ | 1797329819590983680 |
|---|---|
| author | Edi Sutoyo Ahmad Almaarif |
| author_facet | Edi Sutoyo Ahmad Almaarif |
| author_sort | Edi Sutoyo |
| collection | DOAJ |
| description | The quality of students can be seen from the academic achievements, which are evidence of the efforts made by students. Student academic achievement is evaluated at the end of each semester to determine the learning outcomes that have been achieved. If a student cannot meet certain academic criteria that are stated by fulfilling the requirements to continue his studies, the student may have the potential to not graduate on time or even Drop Out (DO). The high number of students who do not graduate on time or DO in higher education institutions can be minimized by detecting students who are at risk in the early stages of education and is supported by making policies that can direct students to complete their education. Also, if the time for completion of student studies can be predicted then the handling of students will be more effective. One technique for making predictions that can be used is data mining techniques. Therefore, in this study, the Naive Bayes Classifier (NBC) algorithm will be used to predict student graduation at Telkom University. The dataset was obtained from the Information Systems Directorate (SISFO), Telkom University which contained 4000 instance data. The results of this study prove that NBC was successfully implemented to predict student graduation. Prediction of the graduation of these students is able to produce an accuracy of 73,725%, precision 0.742, recall 0.736 and F-measure of 0.735. |
| first_indexed | 2024-03-08T07:10:58Z |
| format | Article |
| id | doaj.art-c317c0f534534b808181892a371649b5 |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T07:10:58Z |
| publishDate | 2020-02-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-c317c0f534534b808181892a371649b52024-02-03T03:07:53ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602020-02-01419510110.29207/resti.v4i1.15021502Educational Data Mining untuk Prediksi Kelulusan Mahasiswa Menggunakan Algoritme Naïve Bayes ClassifierEdi Sutoyo0Ahmad Almaarif1Program Studi Sistem Informasi, Fakultas Rekayasa Industri, Universitas TelkomProgram Studi Sistem Informasi, Fakultas Rekayasa Industri, Universitas TelkomThe quality of students can be seen from the academic achievements, which are evidence of the efforts made by students. Student academic achievement is evaluated at the end of each semester to determine the learning outcomes that have been achieved. If a student cannot meet certain academic criteria that are stated by fulfilling the requirements to continue his studies, the student may have the potential to not graduate on time or even Drop Out (DO). The high number of students who do not graduate on time or DO in higher education institutions can be minimized by detecting students who are at risk in the early stages of education and is supported by making policies that can direct students to complete their education. Also, if the time for completion of student studies can be predicted then the handling of students will be more effective. One technique for making predictions that can be used is data mining techniques. Therefore, in this study, the Naive Bayes Classifier (NBC) algorithm will be used to predict student graduation at Telkom University. The dataset was obtained from the Information Systems Directorate (SISFO), Telkom University which contained 4000 instance data. The results of this study prove that NBC was successfully implemented to predict student graduation. Prediction of the graduation of these students is able to produce an accuracy of 73,725%, precision 0.742, recall 0.736 and F-measure of 0.735.http://jurnal.iaii.or.id/index.php/RESTI/article/view/1502data mining, classification, naive bayes classifier, student graduation |
| spellingShingle | Edi Sutoyo Ahmad Almaarif Educational Data Mining untuk Prediksi Kelulusan Mahasiswa Menggunakan Algoritme Naïve Bayes Classifier Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) data mining, classification, naive bayes classifier, student graduation |
| title | Educational Data Mining untuk Prediksi Kelulusan Mahasiswa Menggunakan Algoritme Naïve Bayes Classifier |
| title_full | Educational Data Mining untuk Prediksi Kelulusan Mahasiswa Menggunakan Algoritme Naïve Bayes Classifier |
| title_fullStr | Educational Data Mining untuk Prediksi Kelulusan Mahasiswa Menggunakan Algoritme Naïve Bayes Classifier |
| title_full_unstemmed | Educational Data Mining untuk Prediksi Kelulusan Mahasiswa Menggunakan Algoritme Naïve Bayes Classifier |
| title_short | Educational Data Mining untuk Prediksi Kelulusan Mahasiswa Menggunakan Algoritme Naïve Bayes Classifier |
| title_sort | educational data mining untuk prediksi kelulusan mahasiswa menggunakan algoritme naive bayes classifier |
| topic | data mining, classification, naive bayes classifier, student graduation |
| url | http://jurnal.iaii.or.id/index.php/RESTI/article/view/1502 |
| work_keys_str_mv | AT edisutoyo educationaldatamininguntukprediksikelulusanmahasiswamenggunakanalgoritmenaivebayesclassifier AT ahmadalmaarif educationaldatamininguntukprediksikelulusanmahasiswamenggunakanalgoritmenaivebayesclassifier |