Performance Evaluation of Adaptive Neuro-Fuzzy Inference System (ANFIS) In Predicting New Students (Case Study : UBP Karawang)
The process of admitting new students is an annual routine activity that occurs in a university. This activity is the starting point of the process of searching for prospective new students who meet the criteria expected by the college. One of the colleges that holds new student admissions every yea...
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| Format: | Article |
| Language: | English |
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Universitas Buana Perjuangan Karawang
2021-07-01
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| Series: | Buana Information Technology and Computer Sciences |
| Subjects: | |
| Online Access: | https://journal.ubpkarawang.ac.id/index.php/bit-cs/article/view/1417 |
| _version_ | 1811246544103407616 |
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| author | Tatang Rohana Bayu Priyatna |
| author_facet | Tatang Rohana Bayu Priyatna |
| author_sort | Tatang Rohana |
| collection | DOAJ |
| description | The process of admitting new students is an annual routine activity that occurs in a university. This activity is the starting point of the process of searching for prospective new students who meet the criteria expected by the college. One of the colleges that holds new student admissions every year is Buana Perjuangan University, Karawang. There have been several studies that have been conducted on predictions of new students by other researchers, but the results have not been very satisfying, especially problems with the level of accuracy and error. Research on ANFIS studies to predict new students as a solution to the problem of accuracy. This study uses two ANFIS models, namely Backpropagation and Hybrid techniques. The application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model in the predictions of new students at Buana Perjuangan University, Karawang was successful. Based on the results of training, the Backpropagation technique has an error rate of 0.0394 and the Hybrid technique has an error rate of 0.0662. Based on the predictive accuracy value that has been done, the Backpropagation technique has an accuracy of 4.8 for the value of Mean Absolute Deviation (MAD) and 0.156364623 for the value of Mean Absolute Percentage Error (MAPE). Meanwhile, based on the Mean Absolute Deviation (MAD) value, the Backpropagation technique has a value of 0.5 and 0.09516671 for the Mean Absolute Percentage Error (MAPE) value. So it can be concluded that the Hybrid technique has a better level of accuracy than the Backpropation technique in predicting the number of new students at the University of Buana Perjuangan Karawang |
| first_indexed | 2024-04-12T14:54:55Z |
| format | Article |
| id | doaj.art-0b47564db21142658a32a3619114be4a |
| institution | Directory Open Access Journal |
| issn | 2715-2448 2715-7199 |
| language | English |
| last_indexed | 2024-04-12T14:54:55Z |
| publishDate | 2021-07-01 |
| publisher | Universitas Buana Perjuangan Karawang |
| record_format | Article |
| series | Buana Information Technology and Computer Sciences |
| spelling | doaj.art-0b47564db21142658a32a3619114be4a2022-12-22T03:28:16ZengUniversitas Buana Perjuangan KarawangBuana Information Technology and Computer Sciences2715-24482715-71992021-07-0122313710.36805/bit-cs.v2i2.14171417Performance Evaluation of Adaptive Neuro-Fuzzy Inference System (ANFIS) In Predicting New Students (Case Study : UBP Karawang)Tatang Rohana0Bayu Priyatna1Universitas Buana Perjuangan KarawangUniversitas Buana Perjuangan KarawangThe process of admitting new students is an annual routine activity that occurs in a university. This activity is the starting point of the process of searching for prospective new students who meet the criteria expected by the college. One of the colleges that holds new student admissions every year is Buana Perjuangan University, Karawang. There have been several studies that have been conducted on predictions of new students by other researchers, but the results have not been very satisfying, especially problems with the level of accuracy and error. Research on ANFIS studies to predict new students as a solution to the problem of accuracy. This study uses two ANFIS models, namely Backpropagation and Hybrid techniques. The application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model in the predictions of new students at Buana Perjuangan University, Karawang was successful. Based on the results of training, the Backpropagation technique has an error rate of 0.0394 and the Hybrid technique has an error rate of 0.0662. Based on the predictive accuracy value that has been done, the Backpropagation technique has an accuracy of 4.8 for the value of Mean Absolute Deviation (MAD) and 0.156364623 for the value of Mean Absolute Percentage Error (MAPE). Meanwhile, based on the Mean Absolute Deviation (MAD) value, the Backpropagation technique has a value of 0.5 and 0.09516671 for the Mean Absolute Percentage Error (MAPE) value. So it can be concluded that the Hybrid technique has a better level of accuracy than the Backpropation technique in predicting the number of new students at the University of Buana Perjuangan Karawanghttps://journal.ubpkarawang.ac.id/index.php/bit-cs/article/view/1417anfisbackpropagationhybridprediction |
| spellingShingle | Tatang Rohana Bayu Priyatna Performance Evaluation of Adaptive Neuro-Fuzzy Inference System (ANFIS) In Predicting New Students (Case Study : UBP Karawang) Buana Information Technology and Computer Sciences anfis backpropagation hybrid prediction |
| title | Performance Evaluation of Adaptive Neuro-Fuzzy Inference System (ANFIS) In Predicting New Students (Case Study : UBP Karawang) |
| title_full | Performance Evaluation of Adaptive Neuro-Fuzzy Inference System (ANFIS) In Predicting New Students (Case Study : UBP Karawang) |
| title_fullStr | Performance Evaluation of Adaptive Neuro-Fuzzy Inference System (ANFIS) In Predicting New Students (Case Study : UBP Karawang) |
| title_full_unstemmed | Performance Evaluation of Adaptive Neuro-Fuzzy Inference System (ANFIS) In Predicting New Students (Case Study : UBP Karawang) |
| title_short | Performance Evaluation of Adaptive Neuro-Fuzzy Inference System (ANFIS) In Predicting New Students (Case Study : UBP Karawang) |
| title_sort | performance evaluation of adaptive neuro fuzzy inference system anfis in predicting new students case study ubp karawang |
| topic | anfis backpropagation hybrid prediction |
| url | https://journal.ubpkarawang.ac.id/index.php/bit-cs/article/view/1417 |
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