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All Journal Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) OPSI JUITA : Jurnal Informatika Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) Briliant: Jurnal Riset dan Konseptual Journal of Development Research Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Creative Information Technology Journal Jurnal INSYPRO (Information System and Processing) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Industri Terintegrasi (JUTIN) Community Empowerment Jurnal Teknologi Informasi Cyberku Jurnal Algoritma Jurnal Riptek
Heru Agus Santoso
Universitas Dian Nuswantoro
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Transportation

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Journal : Building of Informatics, Technology and Science

 1 
Performance Evaluation of Deep Learning Models for Cryptocurrency Price Prediction using LSTM, GRU, and Bi-LSTM Yanimaharta, Arya; Santoso, Heru Agus
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7353

Abstract

Cryptocurrency price prediction poses a significant challenge in the digital finance landscape due to its high volatility and complex data patterns. Traditional statistical methods often fail to capture the nonlinear and temporal dependencies inherent in cryptocurrency price movements. This study addresses this issue by evaluating and comparing the performance of three deep learning architectures, namely Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (Bi-LSTM). in predicting the closing prices of Bitcoin (BTC), Ripple (XRP), and Dogecoin (DOGE). The dataset was obtained from Yahoo Finance, covering the period from January 1, 2020, to April 30, 2025. The models were evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (SMAPE), with a forecasting horizon of 30 days. The results of this study indicate that the LSTM model achieved the highest accuracy for Bitcoin and Ripple, with MAPE values of 2.58% and 4.33%, respectively. Meanwhile, the GRU model demonstrated the best overall performance for Dogecoin, with RMSE (0.0131), MAE (0.0084), MAPE (4.12%), and SMAPE (4.06%). On the other hand, the Bi-LSTM model exhibited the lowest performance across all tested cryptocurrencies. These findings highlight the importance of selecting an appropriate model for developing accurate cryptocurrency price prediction systems. This study contributes to the field by providing a detailed comparative analysis of model performance across cryptocurrencies with differing volatility patterns, offering insights for developing more robust and tailored predictive systems in volatile financial environments.
Prediksi dan Optimalisasi Konsumsi Energi Smart Atmospheric Water Generator (SAWG) Menggunakan XGBoost Regression Wiradinata, Halim Jayakusuma; Santoso, Heru Agus
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8655

Abstract

The decreasing availability of clean water has motivated the use of Smart Atmospheric Water Generator (SAWG) systems as an alternative water source, but their electrical energy consumption fluctuates with ambient conditions and operating patterns. This study develops a predictive model of SAWG energy consumption (kWh) using Extreme Gradient Boosting (XGBoost) and demonstrates a prediction-based operational optimization scheme for energy-efficient scheduling. The SAWG logging dataset (1,601 rows, 9 variables) is preprocessed through missing-value handling, numeric conversion, and noise/outlier detection, resulting in 1,313 usable records. The feature set includes environmental parameters, electrical signals, and time features: hour of day, day of week, and month. Modeling employs chronological time-based splits (80:20 as the main configuration and 60:40 as a robustness check), Time Series Cross-Validation on the training block, and hyperparameter tuning via GridSearchCV. Evaluation on the hold-out test sets shows that the model’s performance in a strict time-series setting remains limited: for the 80:20 split, the test results are approximately MAE = 23.16 kWh, MSE = 648.93 kWh², and R² = −0.22, while for the 60:40 split they are MAE = 27.21 kWh, MSE = 932.17 kWh², and R² = −1.75. Although the model cannot yet explain the overall variance of energy consumption satisfactorily, it can still be used to rank hours by predicted energy. In the prediction-based operational optimization stage, hourly model outputs are fed into a Greedy Scheduler that selects H = 8 operating hours with the lowest predicted energy. Compared with a naive schedule, which yields a total predicted energy of 47.493 kWh over the simulation horizon, the greedy schedule achieves 43.134 kWh, corresponding to an estimated saving of about 9.18%. These results indicate that prediction-based scheduling can reduce SAWG energy consumption without modifying the device hardware and can be further developed as a decision-support component for SAWG operation.
Co-Authors Achmad Naila Muna Ramadhani Achmad Zaki Adhe Irham Thoriq Adhitya Nugraha Adhitya Nugraha Akbar Aji Nugroho Akhmad Pandhu Wijaya, Akhmad Pandhu Alfani, Wahyu Amiril Mukminin Arga Dwi Pambudi Arkav Juliandri Arkav Juliandri Assiva, Mochamad Amry Atanasius Marcello Aziz, Halim Al Catur Supriyanto Catur Supriyanto Catur Supriyanto De Rosal Ignatius Moses Setiadi Dewa Kusuma Wijaya Eko Hari Rachmawanto Fahri Firdausillah Farah Zakiyah Rahmanti Herwin Suprijono Heryanto, M Ary Hidayati, Ulfa Jeffry, Muhammad Kusmiyati Kusmiyati Luh Putu Ratna Sundari Muchti, Muhammad Agusdika Ridho Muhamad Haris Zuhri Muslikh, Ahmad Rofiqul Nur Islahudin Pujiono Pujiono Purwanto Purwanto Rahadian, Helmy Rama Tri Agung Ratih Setyaningrum Rifki, Ahmad Muhson Rizaldy, Adhy Roiya Ravida Ruri Suko Basuki Rustam, Suhardi Santi, Dian Nirmala Saraswati, Galuh Wilujeng Septian Enggar Sukmana Setiadi, Kristoforus Adrian Setyadhi Putra Deriyanto Suhardi Rustam Tamami, Aries Jehan Tjetjep Witjahjono Wijaya, Dewa Kusuma Wijaya, S.T, M.Sc, A.MP, Dewa Kusuma Wiradinata, Halim Jayakusuma Yanimaharta, Arya Zaenal Arifin