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All Journal Jurnal Informatika dan Teknik Elektro Terapan Dielektrika : Jurnal Ilmiah Kajian Teori dan Aplikasi Teknik Elektro JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) MOTIVECTION : Journal of Mechanical, Electrical and Industrial Engineering Jurnal Informatika Teknologi dan Sains (Jinteks) JEITECH (JOURNAL OF ELECTRICAL ENGINEERING AND INFORMATION TECHNOLOGY)
Akbar, Lalu A. Syamsul Irfan
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Journal : Journal of Computer System and Informatics (JoSYC)

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Prediksi Nilai Tukar Mata Uang Menggunakan Algoritma Long Short-Term Memory dan Random Forest Hidayat, Imam; Akbar, Lalu A. Syamsul Irfan; Rachman, Ahmad SjamSjiar
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6200

Abstract

Currency exchange rate is an exchange between two different currencies, which is a comparison of the value or price between the two currencies and this comparison is often called the exchange rate. Currency exchange rate movements are very complex and influenced by many factors, including economic, political, and social factors. In an effort to understand and predict these movements, many studies have been conducted using various methods of analysis and prediction. however, there is still no consensus on the best method to predict exchange rate movements. This study aims to compare the performance between the Long Short Term-Memory and Random Forest algorithms in predicting the exchange rate of the Rupiah (IDR) against the Singapore Dollar (SGD). By utilizing the historical data of currency exchange rate movements, the main data and the data of import and export values from the two countries as additional variables, After going through a series of stages ranging from data collection, preprocessing, to modeling, the evaluation results show that the Long Short Term-Memory algorithm has a better performance with a Root Mean Square Error (RMSE) of 152.28, Mean Absolute Percentage Error (MAPE) 1.25%, and 98.74% accuracy, while Random Forest has an RMSE of 284.3, a MAPE of 2.07%, and an accuracy of 97.93%. These results show that Long Short Term-Memory is superior in capturing complex exchange rate change patterns, making it a more effective choice in predicting currency exchange rates than Random Forest.
Perkiraan Suhu Menggunakan Algoritma Recurrent Neural Network Long Short Term Memory Zahidin, Ilham; Kanata, Bulkis; Akbar, Lalu A. Syamsul Irfan
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6242

Abstract

Air temperature is a critical variable in weather conditions that affects various aspects of human life, including health, agriculture, and the economy. In Indonesia, particularly in Mataram City, which is situated in a tropical region, significant temperature changes can impact sectors such as tourism, agriculture, and daily activities. Accurate temperature forecasting can aid the public, industries, and the government in making more informed decisions, both for short-term and long-term planning. However, weather in tropical regions like Mataram tends to be difficult to predict accurately due to its dynamic nature and the influence of multiple atmospheric factors. Conventional weather prediction methods often fail to capture the complex patterns in historical temperature data, necessitating more advanced methods to improve forecast accuracy. Recurrent Neural Networks (RNNs), particularly the Long Short-Term Memory (LSTM) variant, have proven to be highly effective tools for modeling complex time series data. This algorithm can retain long-term information and recognize patterns in data that change over time, making it well-suited for temperature prediction challenges. In this study, the RNN-LSTM algorithm is applied to forecast temperatures in Mataram City, aiming to improve forecast accuracy and produce results useful for various purposes. The temperature prediction model using the LSTM algorithm involves several steps: data collection, data normalization, splitting data into test and training sets, building the LSTM model by determining the number of epochs, layers, and batch size, and finally, evaluating the model with RMSE. Two parameters, epoch and batch size, influence the LSTM model’s forecasting results in this study. Epochs used in this study are 5, 10, 20, 30, 40, 50, and 100, with a fixed batch size of 32. The LSTM algorithm employs the RMSProp optimizer. The temperature prediction model using the LSTM method achieved the best average accuracy with a batch size of 32 and 50 epochs, yielding an RMSE value of 0.13 and a prediction accuracy of 99.96% in forecasting Mataram City’s temperature for the year 2023.
Co-Authors A. Sjamsjiar Rachman Adi Pramana, I Gusti Bagus Ariessaputra, Suthami Astarina, Raditia Azis, Imam Abdul Bulkis Kanata Cipta Ramadhani Djul Fikry Budiman, Djul Fikry Febtiawan, Ergi Putra I Made Sulastra, I Made Ida Ayu Putu Sri Widnyani Jayawarsa, A.A. Ketut Nababan, Sabar Putra, Rayasa Puringgar Prasadha Rachman, Ahmad SjamSjiar Zahidin, Ilham