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All Journal Sinergi International Journal of Research and Applied Technology (INJURATECH)
Sari, Annisa Wulan
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Comparative Analysis of Machine Learning Techniques for Cryptocurrency Price Prediction Sari, Annisa Wulan
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 2 (2024): Vol 4 No 2 (2024)
Publisher : Universitas Komputer Indonesia

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Abstract

The increasing volatility and complexity of cryptocurrency markets have led to the growing application of machine learning (ML) techniques for accurate price prediction. This study presents a comparative analysis of eleven recent research papers on cryptocurrency forecasting using various ML and deep learning models, including Support Vector Machines (SVM), Random Forests (RF), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and ensemble methods. The findings highlight that deep learning models, particularly GRU and LSTM, often outperform traditional statistical models in capturing non-linear patterns and temporal dependencies. Moreover, feature diversity—such as on-chain data, market sentiment, and macroeconomic indicators—has been shown to significantly enhance predictive performance. However, many studies still lack comprehensive validation strategies and rely solely on historical price data, limiting generalizability. This review identifies key gaps in model benchmarking, feature integration, and evaluation consistency, providing a foundation for future research focused on hybrid models and interpretable AI for financial decision-making.
Experimental investigation of HHO blending in combustion engine performance Martin, Awaludin; Hidayatullah, Abda; Ginting, Yogie Rinaldy; Sari, Annisa Wulan
SINERGI Vol 29, No 3 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.3.006

Abstract

The transition to renewable energy sources has become increasingly critical due to the adverse effects of greenhouse gas emissions. One alternative to reducing fossil fuel dependence is hydrogen. Hydrogen technology can be integrated into internal combustion engines without major design modifications. This study investigates the effects of HHO gas blending on engine performance under varying brake load conditions. The carburetor was modified to allow HHO gas from electrolysis to enter the combustion chamber. The results indicate that HHO blending led to a 4.9% increase in brake power, a 1.66% improvement in thermal efficiency, and a 3% reduction in brake-specific energy consumption (BSEC). Additionally, among different potassium hydroxide (KOH) concentrations, the 30% wt solution exhibited the lowest power consumption for electrolysis.
Co-Authors Awaludin Martin Ginting, Yogie Rinaldy Hidayatullah, Abda