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All Journal Jurnal Ilmiah Ekonomi Terpadu Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Miftahul Jannah
Universitas Malikussaleh
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Pengaruh Inflasi dan Pendapatan Perkapita terhadap Konsumsi Rumah Tangga di Provinsi Aceh Fanny Nailufar; Miftahul Jannah; Reza Juanda
Jurnal Ilmiah Ekonomi Terpadu (Jimetera) Vol 2, No 2 (2022): JURNAL ILMIAH EKONOMI TERPADU
Publisher : Universitas Teuku Umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35308/jimetera.v2i2.5876

Abstract

This study examined and analyzed the influence of inflation rate and per capita income on household consumption in Aceh province. This study used secondary data accessed on the Central Bureau of Statistics for 12 years (2009-2020). The method of data analysis used was the Multiple Linear Regression analysis method using Eviews 10. The results study indicated that inflation had no significant effect on Household Consumption in Aceh Province, and Per capita income had a positive and significant effect on Household Consumption in Aceh Province. Simultaneously, Inflation and Per capita Income had a significant effect on Household Consumption in Aceh Province
Application of Long Short-Term Memory (LSTM) Algorithm in Predicting Forex Trading Price Movements on the USD/JPY Pair Miftahul Jannah; Wahyu Fuadi; Zahratul Fitri
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

The foreign exchange (Forex) market offers the potential for high profits but also great risks through currency pair trading. This research proposes the use of the Long Short-Term Memory (LSTM) algorithm, a type of Recurrent Neural Network (RNN), to predict forex price movements for the USD/JPY (American Dollar to Japanese Yen) currency pair based on daily data over a two-year period. The model was designed with a “sequential” architecture consisting of two LSTM layers with 100 units each, followed by a Dropout layer to reduce overfitting and a Dense layer to generate predictions. The total model has 365,905 parameters, with 121,301 parameters trained. During training, model evaluation showed that the combination of batch size 16 and epoch 150 resulted in an RMSE of 0.9840, indicating high accuracy. The application of the model also resulted in an RMSE value of 1.04 and a MAPE of 0.56%, with an average accuracy of 99.44%, indicating a prediction accuracy that successfully follows the actual price trend and is effective in capturing forex price movement patterns in the USD/JPY currency pair, thereby supporting future trading decisions.
Co-Authors Fanny Nailufar Muliani Muliani Reza Juanda Wahyu Fuadi Zahratul Fitri Zulpikar Zulpikar