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All Journal Jurnal AKSI (Akuntansi dan Sistem Informasi) DIKEMAS (Jurnal Pengabdian Kepada Masyarakat)
Leriza Anggraini
Universitas Indo Global Mandiri
Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Journal : Jurnal AKSI (Akuntansi dan Sistem Informasi)

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BPNN's Empirical Analysis of Daily Rupiah Exchange Rate Volatility Utilizing Hidden Neuron Optimization Imelda Saluza; Faradillah; Leriza Anggraini
Jurnal AKSI (Akuntansi dan Sistem Informasi) Vol. 7 No. 1 (2022)
Publisher : Politeknik Negeri Madiun

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Abstract

The exchange rate is the greatest financial market in its application. As a result, traders, investors, and other money market participants must be aware of the movement of currency exchange rate data. The fluctuation, or rise and fall, of currency exchange rates reveals the level of volatility in a country. The Backpropagation Neural Network is one of the models that can grasp the features of currency exchange rates (BPNN). BPNN is made up of three layers: input, hidden, and output, and each layer contains neurons. One of the challenges in designing a BPNN network architecture is determining the ideal number of hidden layer neurons. In this work, ten methodologies will be utilized to determine the number of hidden neurons; the ten approaches provide distinct empirical results in accordance with the goal of this study, which is to perform an empirical analysis of currency exchange rates by maximizing the number of hidden neurons. Empirical results reveal that the approach for calculating the number of hidden neurons performs well in terms of MAE and MSE. For the following seven periods, the best approach is used to forecast the Rupiah exchange rate.
Co-Authors Afiah Azizah Afreicylia Shanshabilla Elza Oktariani Endah Purnamasari Endah Purnamasari Faradillah Faradillah Fernando Josse Imelda Saluza, Imelda Jainal Simanjuntak Lastri Astuti Muhammad Afif Muhammad Hafizh Muhammad Renaldo Muthi’ah Putri Mutiara Ratu Ressy Marlina Rima Sabita An Nafisah