Nurakhmah, Atika Rizki (2021) Sistem Informasi Pengelolaan Keuangan Sekolah (SIPKS) Dengan Generative Adversarial Networks Untuk Face Recognition. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Proses pengajuan pencairan dana anggaran pada mayoritas sekolah masih dilakukan secara manual. Proses manual ini membutuhkan waktu yang cukup lama dan memperbesar resiko terjadinya kesalahan, kekeliruan, maupun kecurangan dalam prosesnya, sehingga menimbulkan kerugian bagi sekolah. Oleh karena itu, dibuatlah aplikasi Sistem Informasi Pengelolaan Keuangan Sekolah (SIPKS). Tugas akhir ini membahas perancangan dan implementasi SIPKS pada platform website dan android. Pada SIPKS diterapkan teknologi face recognition untuk validasi persetujuan pengajuan dengan sample yang diperoleh menggunakan Generative Adversarial Networks, algoritma deep learning yang memungkinkan wajah untuk dikenali meskipun kualitas foto wajah blur. Algoritma ini menghasilkan gambar baru dengan kualitas fokus yang lebih baik, sehingga memperbaiki data set untuk face recognition. Aplikasi SIPKS diharapkan dapat meminimalisir terjadinya kecurangan dan kekeliruan dalam pengelolaan keuangan sekolah.
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The process for submitting budget funds to schools is still done manually. This manual process takes a long time, allow mistakes and cheating to occur in the process, which can cause losses for schools itself. Therefore, the School Financial Management Information System (SIPKS) application was made. This final project discusses the design and implementation of SIPKS on website and android platforms. SIPKS applies face recognition technology to validate submission agreements with samples obtained using Generative Adversarial Networks, a deep learning algorithm that allows faces to be recognized even though the quality of facial photos is blurry. This algorithm produces new images with better focus quality, thus improving the dataset for face recognition. The SIPKS application is expected to minimize damage and mistakes in managing school finances.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | SIPKS, Generative Adversarial Networks, Android, Website, School Finances SIPKS, Generative Adversarial Networks, Android, Website, Anggaran Dana |
| Subjects: | T Technology > T Technology (General) > T58.6 Management information systems T Technology > TA Engineering (General). Civil engineering (General) > TA1650 Face recognition. Optical pattern recognition. |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Informatics Engineering > 55201-(S1) Undergraduate Thesis |
| Depositing User: | Atika Rizki Nurakhmah |
| Date Deposited: | 09 Jul 2021 02:27 |
| Last Modified: | 23 Aug 2024 03:34 |
| URI: | http://repository.its.ac.id/id/eprint/84307 |
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