Implementasi Metode Deep Learning Time Series untuk Prediksi Nilai Kurs Dollar pada Rupiah. (Implementation of Deep Learning Time Series Method to Predict Dollar Exchange Rate on Rupiah).

Savitri, Aisyah Ayu (2024) Implementasi Metode Deep Learning Time Series untuk Prediksi Nilai Kurs Dollar pada Rupiah. (Implementation of Deep Learning Time Series Method to Predict Dollar Exchange Rate on Rupiah). Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

The dollar exchange rate is a macro indicator that reflects economic strength resulting from the global economy. With this dollar exchange rate, international trade transactions can be facilitated by agreeing on the currencies used by the two countries. This prediction of the dollar exchange rate uses data from the past, and the value of the data always changes every day, so this research uses a deep learning time series method with a multivariate time series forecasting approach. This research uses dollar exchange rate data originating from Bank Indonesia (BI) website with a time period from January 2018 to December 2023. And also using supporting variables, namely data on inflation value, export value, import value and gross domestic product (GDP) value with data originating from the Ministry of Trade website (Ministry of Trade). Based on research on the application of the Deep Learning Time Series Method to predict the dollar exchange rate in the rupiah, the results obtained by predicting the selling exchange rate in the following month using 100 epochs were 15,600 with test accuracy results, MSE error value of 24.343, RMSE error value of 156, and The MAPE error value is 0.81. The purchase rate prediction gets a value of 15,500 with test accuracy results with an MSE error value of 27.328, an RMSE error value of 165, and a MAPE error value of 0.87. Based on the results of the analysis of several scenarios that have been carried out, it is known that the selling rate is smaller than the buying rate. The results show that using the hyperparameters epoch 100 in the RNN-LSTM model can provide the best error results.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Prediction, value, multivariate, RNN-LSTM
Subjects:	T Technology > T Technology (General)
Divisions:	Fakultas Teknik > Program Studi Teknik Informatika
Depositing User:	1462000073 Aisyah Ayu Savitri
Date Deposited:	31 Aug 2001 19:35
Last Modified:	05 Oct 2001 15:43
URI:	http://repository.untag-sby.ac.id/id/eprint/41396

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