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All Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Informatika: Jurnal Pengembangan IT
Nurardian, Ridwana Septian
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Computer Science & IT Decision Sciences, Operations Research & Management

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Pengembangan Aplikasi Prediksi Harga Emas Berbasis Web Menggunakan Model Time Series Abdullah, Fikrian Nur; Nurardian, Ridwana Septian; Liya, Amel; Saputra, Ari Setia; Saputra, Atio Wahyudi; Bismi, Waeisul
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.9165

Abstract

High gold price volatility due to global economic instability poses challenges in investment decision-making. This research aims to develop a web-based gold price prediction application using a time series model, focusing on the Gated Recurrent Unit (GRU) algorithm. This application is designed to present real-time, accurate, and easily accessible gold price predictions, thereby increasing the efficiency and transparency of information for investment decision making. The development process starts from collecting and preprocessing daily gold price data for the period 2013-2023, then comparing four predictive models: LSTM, GRU, ARIMA, and XGBoost. Evaluation is performed using MAE, RMSE, and R² metrics. Results showed that GRU provided the best performance with an RMSE value of 17.76 and R² of 0.9410. The GRU model is integrated into a web application using the Flask framework, with an interactive HTML-based interface and Chart.js visualization. This application presents real-time gold price predictions and can be accessed by general users and investors. The results of this study show that the time series approach with GRU is effective in projecting gold prices, and can be a relevant tool in supporting data-based investment decisions.
Analisis Komparatif Model ARIMA, LSTM, dan GRU dalam Forecasting Harga Bitcoin Berbasis Streamlit Nurardian, Ridwana Septian; Riyandi, Albert
JURNAL FASILKOM Vol. 16 No. 2 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i2.12126

Abstract

Bitcoin is a cryptocurrency asset characterized by high volatility and non-linear movement patterns that trigger sudden market risks. Considering that the majority of previous studies have solely focused on mathematical metric evaluations without providing practical solutions, this study aims to compare the performance of the conventional statistical model Autoregressive Integrated Moving Average (ARIMA) with Deep Learning architectures, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), and to implement the best-performing model into an interactive dashboard using Streamlit. The dataset used encompasses daily Bitcoin closing prices for the 2020–2026 period. The methodology includes data preprocessing, MinMaxScaler normalization, sequential data generation using a 30-day sliding window technique, chronological data splitting (80:20), and evaluation using MAE, RMSE, MAPE, Accuracy, and R² Score. Experimental results prove that ARIMA fails to adapt to non-stationary data, whereas GRU outperforms LSTM in terms of architectural efficiency and precision. The GRU model achieved the best performance with an MAE of IDR 34,773,098.88, an RMSE of IDR 46,684,832.94, a MAPE of 2.26%, an Accuracy of 97.74%, and an R² Score of 0.9661. The GRU model was then successfully implemented into a Streamlit web dashboard that facilitates the real-time visualization of historical and predicted prices. In conclusion, the GRU architecture is the most effective and efficient approach for Bitcoin price forecasting, successfully bridging the gap between theoretical analysis and the availability of practical applications.
Co-Authors Abdullah, Fikrian Nur Liya, Amel Riyandi, Albert Saputra, Ari Setia Saputra, Atio Wahyudi Waeisul Bismi