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All Journal Jurnal Informatika Ekonomi Bisnis Jurnal Ilmu Sosial, Ekonomi, dan Pendidikan Jurnal Ilmu Sosial, Ekonomi dan Pendidikan Journal of Emerging Economics and Business
Muliyono
Universitas Muhammadiyah Sumatera Barat
Economics, Econometrics & Finance Education Environmental Science Social Sciences Other

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Identifikasi Chatbot dalam Meningkatkan Pelayanan Online Menggunakan Metode Natural Language Processing Muliyono
Jurnal Informatika Ekonomi Bisnis Vol. 3, No. 4 (December 2021)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (694.814 KB) | DOI: 10.37034/infeb.v3i4.102

Abstract

Chatbot is a software with artificial intelligence that can imitate human conversations through text messages or voice messages. This chatbot can convey information, according to the knowledge that has been given previously. Helping the limitations of the academic section in answering questions posed by students. The method in this study was sourced from a questionnaire distributed to students at the Muhammadiyah University of West Sumatra. Based on the analysis of the questionnaire, there are 40 questions that are often asked by students to the academic section. Then it is processed using Natural Language Processing (NLP). Natural Language Processing is a branch of science from artificial intelligence that is able to study communication between humans and computers through natural language. The processing stage is to identify the intent, process the input and display the results according to the input. The results of the test using a questionnaire addressed to 227 students got a score of 3,55 with a very good predicate. Then do the test using 40 question and answer data. So, obtained 37 appropriate answers and 3 answers that are not in accordance with the percentage of answer accuracy generated from the chatbot is 92.5 percent. The results of this test have been able to respond to the questions asked by students. This chatbot can make it easier for students to get information with a very good level of accuracy
Prediksi Arah Harga Cryptocurrency Menggunakan Hybrid Lstm Encoder dan Xgboost Head: Implementasi dan Evaluasi pada 10 Aset Digital Utama Ade Kurniawan; Muliyono; Hari Suriadi
Jurnal Ilmu Sosial, Ekonomi dan Pendidikan Vol. 1 No. 2 (2025): Oktober 2025
Publisher : Suria Academic Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67467/jisep.v1i2.43

Abstract

The primary challenge in cryptocurrency price prediction lies in the market’s highly volatile, non-linear, and near–random walk behavior, which makes traditional predictive models unable to achieve consistent accuracy. This study aims to develop and evaluate a hybrid model combining Long Short-Term Memory (LSTM) and XGBoost to predict price direction and returns for ten major cryptocurrencies using daily data from 2023 to 2025. Historical data were processed through feature engineering, normalization, and sliding-window sequence construction, and the models were evaluated using TimeSeriesSplit to prevent data leakage. The results show that the hybrid model consistently outperformed both LSTM and XGBoost, achieving an average directional accuracy of 58.6%, significantly higher than the baselines (51.7% for LSTM and 53.6% for XGBoost). The average RMSE of 0.0289 indicates stable return predictions without systematic bias. Statistical validation through paired t-tests and McNemar tests confirmed the significance of the improvement at p < 0.001. A trading simulation using a 1-day holding period produced an annualized return of 41.5% with a Sharpe ratio of 1.12, outperforming the buy-and-hold strategy. These findings highlight that integrating LSTM’s temporal representation with XGBoost’s non-linear learning capabilities is an effective and computationally efficient approach for cryptocurrency price forecasting, offering practical value for the development of algorithmic trading systems.
Predicting Cryptocurrency Price Direction Using a Hybrid LSTM Encoder and XGBoost Head: Implementation and Evaluation on Ten Major Digital Assets Ade Kurniawan; Muliyono; Hari Suriadi
Journal of Emerging Economics and Business Vol. 1 No. 1 (2025): December
Publisher : Journal of Emerging Economics and Business

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The primary challenge in cryptocurrency price prediction lies in the market’s highly volatile, non-linear, and near–random walk behavior, which makes traditional predictive models unable to achieve consistent accuracy. This study aims to develop and evaluate a hybrid model combining Long Short-Term Memory (LSTM) and XGBoost to predict price direction and returns for ten major cryptocurrencies using daily data from 2023 to 2025. Historical data were processed through feature engineering, normalization, and sliding-window sequence construction, and the models were evaluated using TimeSeriesSplit to prevent data leakage. The results show that the hybrid model consistently outperformed both LSTM and XGBoost, achieving an average directional accuracy of 58.6%, significantly higher than the baselines (51.7% for LSTM and 53.6% for XGBoost). The average RMSE of 0.0289 indicates stable return predictions without systematic bias. Statistical validation through paired t-tests and McNemar tests confirmed the significance of the improvement at p < 0.001. A trading simulation using a 1-day holding period produced an annualized return of 41.5% with a Sharpe ratio of 1.12, outperforming the buy-and-hold strategy. These findings highlight that integrating LSTM’s temporal representation with XGBoost’s non-linear learning capabilities is an effective and computationally efficient approach for cryptocurrency price forecasting, offering practical value for the development of algorithmic trading systems.
Prediksi Arah Harga Cryptocurrency Menggunakan Hybrid Lstm Encoder dan Xgboost Head: Implementasi dan Evaluasi pada 10 Aset Digital Utama Ade Kurniawan; Muliyono; Hari Suriadi
Jurnal Ilmu Sosial, Ekonomi dan Pendidikan Vol. 1 No. 2 (2025): Oktober 2025
Publisher : Suria Academic Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67467/jisep.v1i2.43

Abstract

The primary challenge in cryptocurrency price prediction lies in the market’s highly volatile, non-linear, and near–random walk behavior, which makes traditional predictive models unable to achieve consistent accuracy. This study aims to develop and evaluate a hybrid model combining Long Short-Term Memory (LSTM) and XGBoost to predict price direction and returns for ten major cryptocurrencies using daily data from 2023 to 2025. Historical data were processed through feature engineering, normalization, and sliding-window sequence construction, and the models were evaluated using TimeSeriesSplit to prevent data leakage. The results show that the hybrid model consistently outperformed both LSTM and XGBoost, achieving an average directional accuracy of 58.6%, significantly higher than the baselines (51.7% for LSTM and 53.6% for XGBoost). The average RMSE of 0.0289 indicates stable return predictions without systematic bias. Statistical validation through paired t-tests and McNemar tests confirmed the significance of the improvement at p < 0.001. A trading simulation using a 1-day holding period produced an annualized return of 41.5% with a Sharpe ratio of 1.12, outperforming the buy-and-hold strategy. These findings highlight that integrating LSTM’s temporal representation with XGBoost’s non-linear learning capabilities is an effective and computationally efficient approach for cryptocurrency price forecasting, offering practical value for the development of algorithmic trading systems.
Co-Authors Ade Kurniawan Ade Kurniawan Hari Suriadi