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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Emerging Science Journal Ecosains
Harya Widiputra
Perbanas Institute
Computer Science & IT Economics, Econometrics & Finance Engineering Environmental Science

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GA-Optimized Multivariate CNN-LSTM Model for Predicting Multi-channel Mobility in the COVID-19 Pandemic Harya Widiputra
Emerging Science Journal Vol 5, No 5 (2021): October
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/esj-2021-01300

Abstract

The primary factor that contributes to the transmission of COVID-19 infection is human mobility. Positive instances added on a daily basis have a substantial positive association with the pace of human mobility, and the reverse is true. Thus, having the ability to predict human mobility trend during a pandemic is critical for policymakers to help in decreasing the rate of transmission in the future. In this regard, one approach that is commonly used for time-series data prediction is to build an ensemble with the aim of getting the best performance. However, building an ensemble often causes the performance of the model to decrease, due to the increasing number of parameters that are not being optimized properly. Consequently, the purpose of this study is to develop and evaluate a deep learning ensemble model, which is optimized using a genetic algorithm (GA) that incorporates a convolutional neural network (CNN) and a long short-term memory (LSTM). A CNN is used to conduct feature extraction from mobility time-series data, while an LSTM is used to do mobility prediction. The parameters of both layers are adjusted using GA. As a result of the experiments conducted with data from the Google Community Mobility Reports in Indonesia that ranges from the beginning of February 2020 to the end of December 2020, the GA-Optimized Multivariate CNN-LSTM ensemble outperforms stand-alone CNN and LSTM models, as well as the non-optimized CNN-LSTM model, in terms of predicting human movement in the future. This may be useful in assisting policymakers in anticipating future human mobility trends. Doi: 10.28991/esj-2021-01300 Full Text: PDF
Prediksi Indeks BEI dengan Ensemble Convolutional Neural Network dan Long Short-Term Memory Harya Widiputra; Adele Mailangkay; Elliana Gautama
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 3 (2021): Juni 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (673.534 KB) | DOI: 10.29207/resti.v5i3.3111

Abstract

The Indonesian Stock Exchange (IDX) stock market index is one of the main indicators commonly used as a reference for national economic conditions. The value of the stock market index is often being used by investment companies and individual investors to help making investment decisions. Therefore, the ability to predict the stock market index value is a critical need. In the fields of statistics and probability theory as well as machine learning, various methods have been developed to predict the value of the stock market index with a good accuracy. However, previous research results have found that no one method is superior to other methods. This study proposes an ensemble model based on deep learning architecture, namely Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), called the CNN-LSTM. To be able to predict financial time series data, CNN-LSTM takes feature from CNN for extraction of important features from time series data, which are then integrated with LSTM feature that is reliable in processing time series data. Results of experiments on the proposed CNN-LSTM model confirm that the hybrid model effectively provides better predictive accuracy than the stand-alone time series data forecasting models, such as CNN and LSTM.
Persepsi Bank pada Pelaksanaan Restrukturisasi Kredit di Era Pandemi Covid-19 Bekman Siagian; Endang Swasthika; Harya D Widiputra; Dyah N Taurusianingsih
Ecosains: Jurnal Ilmiah Ekonomi dan Pembangunan Vol 11, No 1 (2022): Ecosains: Jurnal Ilmiah Ekonomi dan Pembangunan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ecosains.11812757.00

Abstract

The Covid-19 pandemic has had a direct or indirect impact on banking. This study aims to investigate the conditions and perceptions of the banking industry in dealing with the Covid-19 pandemic and credit restructuring policies in Indonesia. This study is the result of a survey of 35 Perbanas member banks representing all bank groups based on their core capital. The results of the descriptive analysis found that the banking industry was quite strong, responded well to the shocks that occurred, and had strong optimism about the banking recovery. This condition is considered to support Indonesia's financial stability.
Co-Authors Bekman Siagian Dyah N Taurusianingsih Elliana Gautama Endang Swasthika Mailangkay, Adele B. L.