Validation of New Student Registration Documents at Nurul Jadid University Using Convolutional Neural Network
Every year, Nurul Jadid University admits new students by registering them using the website. Each prospective new student can fill in data independently and upload documents such as Deeds, Family Register, Identity Cards, Diplomas, and SKHU. Often, prospective new students need clarification in up...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Islam Negeri Profesor Kiai Haji Saifuddin Zuhri Purwokerto
2024-10-01
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| Series: | Transactions on Informatics and Data Science |
| Subjects: | |
| Online Access: | https://ejournal.uinsaizu.ac.id/index.php/tids/article/view/12281 |
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| Summary: | Every year, Nurul Jadid University admits new students by registering them using the website. Each prospective new student can fill in data independently and upload documents such as Deeds, Family Register, Identity Cards, Diplomas, and SKHU. Often, prospective new students need clarification in uploading documents; for example, the place for uploading ID cards is filled with uploading diplomas and vice versa. It causes the uploaded data not to match the place or group. Today, no document validation technique can match these types of documents. Therefore, a way is needed to overcome this problem. One way to recognize the document type is by its visual form or image. There are several methods for identifying an image, namely deep learning and neural network models. Where the convolutional neural network is known to be fast in processing data in images, this research aims to validate documents on new student registration data with a deep learning method, namely convolutional neural network (CNN). The experimental results show that the proposed method can classify the Nurul Jadid University new student registration documents with an accuracy rate of 0.91, such as the birth certificate at 0.97, diploma documents at 0.88, Family card documents at 0.88, identity cards at 0.84, exam result certificate with an accuracy 0.94.
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| ISSN: | 3064-1772 |