PENGEMBANGAN SISTEM CERDAS UNTUK PREDIKSI DAFTAR KEMBALI MAHASISWA BARU DENGAN METODE NAIVE BAYES (STUDI KASUS: UNIVERSITAS PENDIDIKAN GANESHA)
Ganesha University of Education or Undiksha is one of the state universities in Bali, precisely in the city of Singaraja. In the admission of new students, Undiksha applies 3 admissions paths, as follows the State University National Admission Selection (SNMPTN), State University Joint Entrance Tes...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Institut Bisnis dan Teknologi Indonesia
2020-04-01
|
| Series: | SINTECH (Science and Information Technology) Journal |
| Subjects: | |
| Online Access: | https://ejournal.instiki.ac.id/index.php/sintechjournal/article/view/523 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850197842805653504 |
|---|---|
| author | Komang Aditya Pratama Gede Aditra Pradnyana I Ketut Resika Arthana |
| author_facet | Komang Aditya Pratama Gede Aditra Pradnyana I Ketut Resika Arthana |
| author_sort | Komang Aditya Pratama |
| collection | DOAJ |
| description |
Ganesha University of Education or Undiksha is one of the state universities in Bali, precisely in the city of Singaraja. In the admission of new students, Undiksha applies 3 admissions paths, as follows the State University National Admission Selection (SNMPTN), State University Joint Entrance Test (SBMPTN), and Independent Entrance Test (SMBJM) consisting of 2 parts namely Computer Based Test (CBT) and Interests and Talents. Each year the committees are busy with the re-registration of prospective students. In determining the number of students quota for re-registration, they are still using the manual method in form of an excel file, so they want to use a system to do the process. These problems can be overcome by using “Intelligent System for Re-Registration of New Students Prediction using the Naive Bayes Method (Case Study: Ganesha University of Education)â€. The Naive Bayes method is used to determine the re-register probability of the new students so that the number of students who re-register can be determining the new students quota. In developing the system, the researcher use the CRISP-DM methodology as a standard of data mining process as well as a research method. The results of this prediction system research show that the system can predict well with the average predictive system accuracy value of 75.56%.
|
| format | Article |
| id | doaj-art-accc2e317bdb4d3393a6d36adcec925a |
| institution | OA Journals |
| issn | 2598-7305 2598-9642 |
| language | English |
| publishDate | 2020-04-01 |
| publisher | Institut Bisnis dan Teknologi Indonesia |
| record_format | Article |
| series | SINTECH (Science and Information Technology) Journal |
| spelling | doaj-art-accc2e317bdb4d3393a6d36adcec925a2025-08-20T02:13:02ZengInstitut Bisnis dan Teknologi IndonesiaSINTECH (Science and Information Technology) Journal2598-73052598-96422020-04-013110.31598/sintechjournal.v3i1.523PENGEMBANGAN SISTEM CERDAS UNTUK PREDIKSI DAFTAR KEMBALI MAHASISWA BARU DENGAN METODE NAIVE BAYES (STUDI KASUS: UNIVERSITAS PENDIDIKAN GANESHA)Komang Aditya Pratama0Gede Aditra Pradnyana1I Ketut Resika Arthana2Universitas Pendidikan GaneshaUniversitas Pendidikan GaneshaUniversitas Pendidikan Ganesha Ganesha University of Education or Undiksha is one of the state universities in Bali, precisely in the city of Singaraja. In the admission of new students, Undiksha applies 3 admissions paths, as follows the State University National Admission Selection (SNMPTN), State University Joint Entrance Test (SBMPTN), and Independent Entrance Test (SMBJM) consisting of 2 parts namely Computer Based Test (CBT) and Interests and Talents. Each year the committees are busy with the re-registration of prospective students. In determining the number of students quota for re-registration, they are still using the manual method in form of an excel file, so they want to use a system to do the process. These problems can be overcome by using “Intelligent System for Re-Registration of New Students Prediction using the Naive Bayes Method (Case Study: Ganesha University of Education)â€. The Naive Bayes method is used to determine the re-register probability of the new students so that the number of students who re-register can be determining the new students quota. In developing the system, the researcher use the CRISP-DM methodology as a standard of data mining process as well as a research method. The results of this prediction system research show that the system can predict well with the average predictive system accuracy value of 75.56%. https://ejournal.instiki.ac.id/index.php/sintechjournal/article/view/523intelligent systemdata miningpredictionnaive bayesCRISP-DM |
| spellingShingle | Komang Aditya Pratama Gede Aditra Pradnyana I Ketut Resika Arthana PENGEMBANGAN SISTEM CERDAS UNTUK PREDIKSI DAFTAR KEMBALI MAHASISWA BARU DENGAN METODE NAIVE BAYES (STUDI KASUS: UNIVERSITAS PENDIDIKAN GANESHA) SINTECH (Science and Information Technology) Journal intelligent system data mining prediction naive bayes CRISP-DM |
| title | PENGEMBANGAN SISTEM CERDAS UNTUK PREDIKSI DAFTAR KEMBALI MAHASISWA BARU DENGAN METODE NAIVE BAYES (STUDI KASUS: UNIVERSITAS PENDIDIKAN GANESHA) |
| title_full | PENGEMBANGAN SISTEM CERDAS UNTUK PREDIKSI DAFTAR KEMBALI MAHASISWA BARU DENGAN METODE NAIVE BAYES (STUDI KASUS: UNIVERSITAS PENDIDIKAN GANESHA) |
| title_fullStr | PENGEMBANGAN SISTEM CERDAS UNTUK PREDIKSI DAFTAR KEMBALI MAHASISWA BARU DENGAN METODE NAIVE BAYES (STUDI KASUS: UNIVERSITAS PENDIDIKAN GANESHA) |
| title_full_unstemmed | PENGEMBANGAN SISTEM CERDAS UNTUK PREDIKSI DAFTAR KEMBALI MAHASISWA BARU DENGAN METODE NAIVE BAYES (STUDI KASUS: UNIVERSITAS PENDIDIKAN GANESHA) |
| title_short | PENGEMBANGAN SISTEM CERDAS UNTUK PREDIKSI DAFTAR KEMBALI MAHASISWA BARU DENGAN METODE NAIVE BAYES (STUDI KASUS: UNIVERSITAS PENDIDIKAN GANESHA) |
| title_sort | pengembangan sistem cerdas untuk prediksi daftar kembali mahasiswa baru dengan metode naive bayes studi kasus universitas pendidikan ganesha |
| topic | intelligent system data mining prediction naive bayes CRISP-DM |
| url | https://ejournal.instiki.ac.id/index.php/sintechjournal/article/view/523 |
| work_keys_str_mv | AT komangadityapratama pengembangansistemcerdasuntukprediksidaftarkembalimahasiswabarudenganmetodenaivebayesstudikasusuniversitaspendidikanganesha AT gedeaditrapradnyana pengembangansistemcerdasuntukprediksidaftarkembalimahasiswabarudenganmetodenaivebayesstudikasusuniversitaspendidikanganesha AT iketutresikaarthana pengembangansistemcerdasuntukprediksidaftarkembalimahasiswabarudenganmetodenaivebayesstudikasusuniversitaspendidikanganesha |