ATMAJA, ADITYA MURTI KUSUMA (2023) MULTI-LAYER PERCEPTRON NEURAL NETWORK FOR ANALYZING THE VARIANT OF THE CORONAVIRUS DISEASE OF 2019 SPREAD. S1 thesis, Universitas Mercu Buana Jakarta.
|
Text (COVER)
01 COVER.pdf Download (741kB) | Preview |
|
|
Text (ABSTRAK)
02 ABSTRAK.pdf Download (135kB) | Preview |
|
![[img]](https://repository.mercubuana.ac.id/style/images/fileicons/text.png) |
Text (BAB I)
03 BAB 1.pdf Restricted to Registered users only Download (135kB) |
|
|
Text (BAB II)
04 BAB 2.pdf Restricted to Registered users only Download (418kB) |
|
|
Text (BAB III)
05 BAB 3.pdf Restricted to Registered users only Download (236kB) |
|
|
Text (BAB IV)
06 BAB 4.pdf Restricted to Registered users only Download (272kB) |
|
|
Text (BAB V)
07 BAB 5.pdf Restricted to Registered users only Download (160kB) |
|
|
Text (DAFTAR PUSTAKA)
08 DAFTAR PUSTAKA.pdf Restricted to Registered users only Download (179kB) |
|
|
Text (LAMPIRAN)
09 LAMPIRAN.pdf Restricted to Registered users only Download (474kB) |
Abstract
Abstract : A pandemic is an epidemic that attack people in the world. At the beginning of 2020, Indonesia and the whole world were shocked by the Coronavirus Disease 2019 (Covid-19) which gave fear to people in the world. besides that, Pandemic gives us a data source which can be used to carry out various kinds of experiments in deep learning. To conduct experiments with covid data, we need an algorithm that can adapt and predict the number of new cases and variants of covid19. Namely is Multilayer Perceptron (MLP) algorithm, MLP has the ability to find and study complex patterns contained in the data provided to them, so we can use this to find there are new variants of covid -19 or not. The results of this study are a model that can predict the number of Covid-19 cases on certain dates and the visualization of the model can be used to determine whether there are new variants or not. This research also makes comparisons between standard neural networks and MLP, the results of MLP are superior and can make pretty good predictions with 97% accuracy with five hidden layers, while normal neural networks are only 66%. Keywords : Coronavirus, deep learning, Hidden layer, Multilayer Perceptron, Prediction Abstrak : Pandemi adalah wabah yang menyerang manusia di dunia. Di awal tahun 2020, Indonesia dan seluruh dunia dihebohkan dengan adanya Coronavirus Disease 2019 (Covid-19) yang menimbulkan ketakutan bagi masyarakat dunia. selain itu, Pandemic memberi kita sumber data yang bisa digunakan untuk melakukan berbagai macam eksperimen dalam deep learning. Untuk melakukan eksperimen dengan data covid, diperlukan suatu algoritma yang dapat mengadaptasi dan memprediksi jumlah kasus baru dan varian covid-19. Yaitu Algoritma Multilayer Perceptron (MLP), MLP memiliki kemampuan untuk menemukan dan mempelajari pola kompleks yang terkandung dalam data yang diberikan kepada mereka, sehingga kita dapat menggunakan ini untuk menemukan ada atau tidaknya varian baru covid -19. Hasil dari penelitian ini adalah sebuah model yang dapat memprediksi jumlah kasus Covid19 pada tanggal tertentu dan visualisasi model tersebut dapat digunakan untuk mengetahui apakah ada varian baru atau tidak. Penelitian ini juga membuat perbandingan antara jaringan saraf standar dan MLP, hasil MLP lebih unggul dan dapat membuat prediksi yang cukup baik dengan akurasi 97% dengan lima lapisan tersembunyi, sedangkan jaringan saraf normal hanya 66%. Kata Kunci : Coronavirus, deep learning, hidden layer, Multilayer Perceptron, prediksi
| Item Type: | Thesis (S1) |
|---|---|
| Call Number CD: | FIK/INFO. 23 173 |
| Call Number: | SIK/18/23/044 |
| NIM/NIDN Creators: | 41519010117 |
| Uncontrolled Keywords: | Coronavirus, deep learning, hidden layer, Multilayer Perceptron, prediksi |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 570 Biology/Biologi, Ilmu Hayat > 579 Microorganisms/Mikroorganisme > 579.2 Viruses, Virology/Virus, Virologi |
| Divisions: | Fakultas Ilmu Komputer > Sistem Informasi |
| Depositing User: | khalimah |
| Date Deposited: | 01 Nov 2023 08:09 |
| Last Modified: | 01 Nov 2023 08:09 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/82526 |
Actions (login required)
 |
View Item |
