Bitcoin prices exhibit high volatility and complex non-linear patterns, making accurate price predictions challenging in the context of cryptocurrency market research. In this study, an optimized SVR-PSO predictive modeling approach is developed to help predict Bitcoin prices, and its performance is compared against a...
Rizky Parlika, Wahyudi Wahyudi, Ahmad Budi Trisnawan et al.· JURNAL FASILKOM· 0 citations
This study uses Support Vector Regression (SVR) to forecast Bitcoin realized volatility across four timeframes (2h, 4h, 8h, and daily) and four forecast horizons using Binance OHLCV data from January 2023 to June 2026. Realized volatility is estimated using a 5-period rolling standard deviation of log returns. We const...
Syaiful Hidayat, Mohammad Faris Al Fatih, Rizky Parlika· JURNAL FASILKOM· 0 citations
Cryptocurrency markets are characterized by high volatility, rapid price fluctuations, and substantial uncertainty, creating challenges for investment risk interpretation. This study develops a descriptive risk-interpretation framework, rather than a price-prediction or decision-optimization model, by integrating multi...
Velian Prapatoni, Rizky Parlika, Firza Prima Aditiawan· bit-Tech· 0 citations
A Support Vector Regression model for predicting the next-day Bitcoin closing price and compares Grid Search tuning with the Whale Optimization Algorithm using weighted Time Series Cross-Validation demonstrate a trade-off between slightly lower test errors and substantially greater optimization cost.
Wahyudi Wahyudi, Rizky Parlika, Yisti Vita Via· bit-Tech· 0 citations
These findings establish Categorical Boosting's performance as strong benchmark evidence on a single public dataset; however, further validation on newer campaigns, unseen domains, adversarially obfuscated sites, and cross-dataset settings is still required to confirm its generalization capabilities.
Muhammad Hilmy Aziz, Rizky Parlika, Kartini Kartini· bit-Tech· 0 citations
This work attempts to provide a thorough comparative analysis mapping the precise accuracy–efficiency trade-off between Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models under a standardized Grid Search hyperparameter optimization pipeline using a recent Bitcoin closing-price dataset.
