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All Journal Jurnal Teknologi dan Manajemen Informatika
Yessica Yamin
STMIK Time. Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Comparative Analysis of LSTM-Based Models for Daily Gold Price Forecasting Using Time Series and Sentiment Features Yessica Yamin; Robet; Hendri
Jurnal Teknologi dan Manajemen Informatika Vol. 12 No. 1 (2026): Juni 2026
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v12i1.16309

Abstract

This study examines the performance of three forecasting approaches for predicting daily gold prices: a Pure Long Short-Term Memory (LSTM) model, a Hybrid LSTM + Prophet model, and an LSTM model enhanced with sentiment features. Daily price data from 2013 to 2023 were used, and all evaluation metrics were computed on inverse-transformed values to ensure accurate interpretation. The Pure LSTM model shows strong predictive capability, achieving a testing RMSE of 17.4500, MAE of 12.9924, R2 of 0.9431, and a MAPE of 0.72%. The Hybrid LSTM + Prophet model performs considerably worse on unseen data, with a testing RMSE of 71.6272 and an R2 close to zero, indicating that Prophet’s decomposition is unsuitable for highly volatile price movements. The LSTM + Sentiment model provides the best overall performance, with a testing RMSE of 17.2769, MAE of 12.5515, R2 of 0.9442, and a MAPE of 0.70%, showing modest but consistent improvement over the baseline LSTM. These findings highlight the suitability of LSTM-based models for non-seasonal financial time series and demonstrate that sentiment can contribute additional predictive value.
Co-Authors Hendri Hendri Robet Robet