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All Journal Teknik: Jurnal Ilmu Teknik dan Informatika
Herdiyanto, Qatrunnada Athirah
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Computer Science & IT

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Analisis Komparatif XGBoost dan Temporal Fusion Transformer (TFT) pada Time Series Forecasting Harga Solana Herdiyanto, Qatrunnada Athirah; Juhraini Helfiana Lexa; Chan, M. Zikry Sahendra
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 6 No. 1 (2026): Mei : Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v6i1.1138

Abstract

 Cryptocurrency price prediction, particularly for highly volatile assets like Solana (SOL), is a crucial challenge in time series data analysis in digital finance. This study aims to compare the performance of the XGBoost machine learning algorithm with the Temporal Fusion Transformer (TFT) deep learning model in predicting Solana's daily closing price. The dataset used consists of historical Solana price data and network fundamentals data in the form of Total Value Locked (TVL). The research process includes data preprocessing, dividing training and test data, model training, and evaluation using the Root Mean Squared Error (RMSE) metric. The results show that using the same-day price feature has the potential to cause target leakage, resulting in invalid prediction accuracy. In testing using pure historical data without data leakage, the XGBoost model performed better than TFT with an RMSE of 4.27, while TFT produced an RMSE of 18.59. Furthermore, the integration of network fundamentals data in the form of TVL did not improve prediction accuracy and even caused a decrease in performance for the XGBoost model with an RMSE of 7.10. The results of this study show that the use of historical price action features is more effective than fundamental network indicators for short-term daily Solana price predictions.
Co-Authors Chan, M. Zikry Sahendra Juhraini Helfiana Lexa