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Artificial Neural Network Model with PSO as a Learning Method to Predict Movement of the Rupiah Exchange Rate against the US Dollar Eko Verianto; Budi Sutedjo Dharma Oetomo
IJAIT (International Journal of Applied Information Technology) Vol 04 No 02 (November 2020)
Publisher : School of Applied Science, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijait.v4i02.3381

The movement of currency exchange rate can be predicted in the next few days, this is used by economic actors to get profit. Artificial Neural Network with the backpropagation learning method is good enough to use for forecasting time series data, it's just that in its application this method was considered to have shortcomings such as a long training time to achieve convergence. The purpose of this research is to form a Multilayer Perceptron Artificial Neural Network model with the Particle Swarm Optimization (PSO) algorithm as a learning method in the case of currency exchange rate prediction. This research produced a model that can predict the movement of the Rupiah exchange rate against the US Dollar, while the model formed was the MLP-PSO model with an error rate of 5.6168 x 10-8, slightly better than the MLP-BP model with an error rate of 6.4683 x 10-8. These results indicated that the PSO algorithm can be used as a learning algorithm in the Multilayer Perceptron Artificial Neural Network.

PENDAMPINGAN DAN PELATIHAN PENGUATAN COMPUTATIONAL THINKING SEBAGAI KEMAMPUAN UTAMA ABAD 21 Antonius Rachmat Chrismanto; Katon Wijana; Eko Verianto; Argo Wibowo; Yuan Lukito
ABDIMAS ALTRUIS: Jurnal Pengabdian Kepada Masyarakat Vol 3, No 2 (2020): Oktober 2020
Publisher : Universitas Sanata Dharma

The skills needed in the 21st century are communication, collaborative, creativity innovation, and, critical thinking problem solving. One that is closely related to mindset is critical thinking problem solving. Critical thinking and being able to solve problems means the ability to understand a complex problem, connect information to other information, and finally find the solutions of the problem. This ability is closely related to the field of Information Technology (IT) because this field really needs a structured, coherent mindset, analysis, and computational thinking. This ability is very much needed by the young generation of Indonesia today.The Information Technology of UKDW Faculty (FTI)’s partner, Bopkri I Yogyakarta High School, has the same vision to prepare students to have real and applicable abilities. Students need regular and structured training to achieve these goals. Bopkri I and FTI work together in the form of community service training in strengthening computational thinking by implementing basic programming, advanced programming, and training evaluation.This program was held in 2 major stages., The first stage consists of strengthening computational thinking using basic programming training in general, and the second using advanced programming training in the form of competitive programming and its simulations. Students were given complete material, face-to-face/online knowledge transfer, complete modules, exercises, and direct simulations by some experienced lecturers from FTI UKDW.

Penerapan LSTM Dengan Regularisasi Untuk Mencegah Overfitting Pada Model Prediksi Tingkat Inflasi di Indonesia Verianto, Eko
Jurnal Sistem Informasi dan Sistem Komputer Vol 9 No 2 (2024): Vol 9 No 2 - 2024
Publisher : STIMIK Bina Bangsa Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51717/simkom.v9i2.460

Permasalahan inflasi dapat mempengaruhi pengembangan strategi, keputusan dan kebijakan pemerintah, sehingga diperlukan pemahaman mendalam mengenai tren inflasi di masa yang akan datang. Dalam menghadapi situasi ini diperlukan model prediksi yang dapat memodelkan tren inflasi di masa yang akan datang dengan tepat. Salah satu pendekatan yang dapat digunakan adalah backpropagation, namun penerapan backpropagation pada permasalahan prediksi seperti pada penelitian sebelumnya mendapatkan tantangan tersendiri, terutama pada data runtun waktu yang biasanya menghadirkan ketergantungan temporal. Penggunaan backpropagation dalam penelitian sebelumnya juga menunjukan perilaku overfitting. Tujuan dari penelitian ini adalah mengatasi ketergantungan temporal pada data runtun waktu menggunakan Long-Short Term Memory (LSTM) dan penerapan dropout dalam arsitektur LSTM untuk mencegah terjadinya overfitting pada model prediksi tingkat inflasi di Indonesia. Hasil dari penelitian ini menunjukan bahwa penerapan LSTM untuk mengatasi data dengan ketergantungan temporal menghasilkan kinerja yang cukup baik dan juga penggunaan dropout pada LSTM dapat mengatasi permasalahan overfitting pada prediksi tingkat inflasi di Indonesia.

TRANSFORMER WITH LAGGED FEATURES FOR HANDLING LONG-TERM DATA DEPENDENCY IN TIME SERIES FORECASTING Verianto, Eko; Shimbun, Annisa Fikria
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 3 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i3.9247

Data with long-term dependencies plays an important role in time series forecasting. However, studying data with long-term dependencies in time series data presents challenges for most algorithms. While some algorithms can forecast time series data, not all can model data with long-term dependencies effectively. The algorithm typically used for forecasting data with long-term dependencies is Long Short-Term Memory (LSTM), but LSTM can still face vanishing gradient issues, making it difficult to identify long-term dependencies in very long datasets. Another algorithm used for forecasting long-term time series data is the transformer. However, this algorithm has not yet shown better performance compared to simple linear models. The goal of this research is to develop an effective solution for forecasting time series data with long-term dependencies. The approach proposed in this research is the transformer with lagged features and also using time series cross-validation techniques. The results of this study show the performance of the transformer model in MAPE per fold on the BBCA stock dataset with a lag=5 and fold=5 configuration as follows: 0.0390, 0.0329, 0.0207, 0.0554, 0.0423. On the USD/IDR exchange rate dataset, the results are 0.0273, 0.0431, 0.0498, 0.0236, 0.237. The results of each fold are inconsistent and show unstable performance, indicating that the transformer with lagged features and using time series cross-validation techniques has not yet been able to provide its best performance in long-term time series forecasting.

TRANSFORMER WITH LAGGED FEATURES FOR HANDLING LONG-TERM DATA DEPENDENCY IN TIME SERIES FORECASTING Verianto, Eko; Shimbun, Annisa Fikria
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 3 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i3.9247

Data with long-term dependencies plays an important role in time series forecasting. However, studying data with long-term dependencies in time series data presents challenges for most algorithms. While some algorithms can forecast time series data, not all can model data with long-term dependencies effectively. The algorithm typically used for forecasting data with long-term dependencies is Long Short-Term Memory (LSTM), but LSTM can still face vanishing gradient issues, making it difficult to identify long-term dependencies in very long datasets. Another algorithm used for forecasting long-term time series data is the transformer. However, this algorithm has not yet shown better performance compared to simple linear models. The goal of this research is to develop an effective solution for forecasting time series data with long-term dependencies. The approach proposed in this research is the transformer with lagged features and also using time series cross-validation techniques. The results of this study show the performance of the transformer model in MAPE per fold on the BBCA stock dataset with a lag=5 and fold=5 configuration as follows: 0.0390, 0.0329, 0.0207, 0.0554, 0.0423. On the USD/IDR exchange rate dataset, the results are 0.0273, 0.0431, 0.0498, 0.0236, 0.237. The results of each fold are inconsistent and show unstable performance, indicating that the transformer with lagged features and using time series cross-validation techniques has not yet been able to provide its best performance in long-term time series forecasting.

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