Priyanto, Habib Septrian (2023) PENERAPAN MODEL DERET WAKTU HARGA EMAS MENGGUNAKAN LONG SHORT-TERM MEMORY (LSTM) DAN BIDIRECTIONAL LONG SHORT-TERM MEMORY (BI-LSTM)PENERAPAN MODEL DERET WAKTU HARGA EMAS MENGGUNAKAN LONG SHORT-TERM MEMORY (LSTM) DAN BIDIRECTIONAL LONG SHORT-TERM MEMORY (BI-LSTM). Tugas Akhir (S1) - thesis, UNIVERSITAS BAKRIE.
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Abstract
Emas merupakan logam mulia yang sering digunakan alat investasi dan memiliki nilai ekonomis. Kenaikan peminat dan nilai emas pada tahun 2023 naik dari tahun 2019 dikarenakan emas dianggap sebagai investasi yang mudah dilakukan, memiliki risiko rendah, dan cenderung mengalami kenaikan nilai dalam jangka waktu tertentu. Namun, emas memiliki sifat fluktuasi di pasar emas sehingga sangat sulit dan rumit untuk dipelajari. Maka dari itu, solusi dari permasalahan ini adalah menggunakan neural networks sebagai metode untuk prediksi harga emas yaitu algoritma Long Short-Term Memory (LSTM) dan Bidirectional Long Short-Term Memory (Bi-LSTM). Peneliti menggunakan data berisi 1143 baris data dengan jangka waktu 2 Januari 2019 - 19 Juli 2023 yang akan dievaluasi dengan Mean Percentage Absolute Error (MAPE), Root-Mean-Square Error (RMSE), dan Coefficient of determination (R-squared) sebagai output-nya. Hasil model Bi�LSTM lebih baik dibanding LSTM juga pada penelitian sebelumnya dalam aspek evaluasi performa model MAPE. Tetapi, hasil dalam aspek evaluasi peforma model RMSE memiliki hasil lebih baik penelitian sebelumnya daripada hasil penelitian ini
| Item Type: | Thesis (Tugas Akhir (S1) - ) |
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| Uncontrolled Keywords: | emas, deret waktu, neural networks, LSTM, Bi-LSTM |
| Subjects: | Computer Science Computer Science > Informatics Thesis > Thesis (S1) |
| Divisions: | Fakultas Teknik dan Ilmu Komputer > Program Studi Informatika |
| Depositing User: | Habib Septrian Priyanto |
| Date Deposited: | 01 Sep 2023 08:23 |
| Last Modified: | 01 Sep 2023 08:23 |
| URI: | https://repository.bakrie.ac.id/id/eprint/8493 |
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