Neural Network Prediction of Suitability Course for Post PMR Students
This study aims to develop a Neural Network Model for predicting suitability course for post PMR students. Artificial Neural Network technique, using Multilayer Perceptron and backpropagation algorithm was employed in this case study. A total of 127 data sample of Form Four students of year 2003 fr...
| 第一著者: | |
|---|---|
| フォーマット: | 学位論文 |
| 言語: | 英語 |
| 出版事項: |
2003
|
| 主題: | |
| オンライン･アクセス: | https://etd.uum.edu.my/1031/1/LOOI_TUCK.pdf https://etd.uum.edu.my/1031/ |
| Abstract | Abstract here |
| _version_ | 1855352852624441344 |
|---|---|
| author | Tuck, Looi |
| author_facet | Tuck, Looi |
| author_sort | Tuck, Looi |
| description | This study aims to develop a Neural Network Model for
predicting suitability course for post PMR students. Artificial Neural Network technique, using Multilayer Perceptron and backpropagation algorithm was employed in this case study. A total of 127 data sample of Form Four students of year 2003 from SMK St Michael, Alor Star was trained using the above mentioned algorithm. The findings show that the best model composes of 10 nodes in input layer; 7 nodes in hidden layer and one node in output layer. A training percentage of correctness 81.04% and testing percentage of correctness 76.79 were achieved using this model. By identifying critical factors to the students’ suitability to the course, students and parents can make an informed decision. This project should be able to provide us with some insights into the type of pattern
that exits in educational data. Therefore, Neural Network has great potential in educational planning.
|
| format | Thesis |
| id | oai:etd.uum.edu.my:1031 |
| institution | Universiti Utara Malaysia |
| language | English |
| publishDate | 2003 |
| record_format | EPrints |
| record_pdf | Abstract |
| spelling | oai:etd.uum.edu.my:10312013-07-24T12:10:05Z https://etd.uum.edu.my/1031/ Neural Network Prediction of Suitability Course for Post PMR Students Tuck, Looi QA71-90 Instruments and machines This study aims to develop a Neural Network Model for predicting suitability course for post PMR students. Artificial Neural Network technique, using Multilayer Perceptron and backpropagation algorithm was employed in this case study. A total of 127 data sample of Form Four students of year 2003 from SMK St Michael, Alor Star was trained using the above mentioned algorithm. The findings show that the best model composes of 10 nodes in input layer; 7 nodes in hidden layer and one node in output layer. A training percentage of correctness 81.04% and testing percentage of correctness 76.79 were achieved using this model. By identifying critical factors to the students’ suitability to the course, students and parents can make an informed decision. This project should be able to provide us with some insights into the type of pattern that exits in educational data. Therefore, Neural Network has great potential in educational planning. 2003-09-30 Thesis NonPeerReviewed application/pdf en https://etd.uum.edu.my/1031/1/LOOI_TUCK.pdf Tuck, Looi (2003) Neural Network Prediction of Suitability Course for Post PMR Students. Masters thesis, Universiti Utara Malaysia. |
| spellingShingle | QA71-90 Instruments and machines Tuck, Looi Neural Network Prediction of Suitability Course for Post PMR Students |
| thesis_level | Master |
| title | Neural Network Prediction of Suitability Course for Post PMR Students |
| title_full | Neural Network Prediction of Suitability Course for Post PMR Students |
| title_fullStr | Neural Network Prediction of Suitability Course for Post PMR Students |
| title_full_unstemmed | Neural Network Prediction of Suitability Course for Post PMR Students |
| title_short | Neural Network Prediction of Suitability Course for Post PMR Students |
| title_sort | neural network prediction of suitability course for post pmr students |
| topic | QA71-90 Instruments and machines |
| url | https://etd.uum.edu.my/1031/1/LOOI_TUCK.pdf https://etd.uum.edu.my/1031/ |
| work_keys_str_mv | AT tucklooi neuralnetworkpredictionofsuitabilitycourseforpostpmrstudents |