Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Jurnal Sistem informasi dan informatika (SIMIKA) E-Link: Jurnal Teknik Elektro dan Informatika Jurnal Sistem Informasi dan Sistem Komputer
Siswanto, Paulus William
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal Sistem informasi dan informatika (SIMIKA)

 1 
ANALISIS PERBANDINGAN METODE ARIMA DAN LEAST SQUARE UNTUK PREDIKSI HARGA EMAS : PENDEKATAN PROBABILISTIK DAN STATISTIK Anggelia, Dita; Riti, Yosefina Finsensia; Siswanto, Paulus William
Jurnal Sistem Informasi dan Informatika (Simika) Vol 7 No 1 (2024): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v7i1.3197

Abstract

Gold, a precious metal renowned for its value in various sectors, including investment and jewelry, is often considered a secure asset within investment portfolios. However, its prices exhibit high volatility influenced by economic, geopolitical, and global financial factors. Previous research has focused on predictive methods to anticipate gold price movements. In recent years, heightened complexity and uncertainty, exacerbated by global factors such as economic shifts and the Covid-19 pandemic, emphasizes the urgency of accurate gold price predictions. This study comprehensively analyzes and compares the performance of Autoregressive Integrated Moving Average (ARIMA) and Ordinary Least Squares (OLS) in forecasting gold prices, utilizing statistical and probabilistic approaches. ARIMA excels in handling time series data, identifying complex patterns, and forecasting price changes based on historical trends. Conversely, OLS, a probabilistic method, stands out in adjusting linear models to gold price data, providing detailed insights into influencing factors. The research employs a 5-year gold price dataset (2018-2023) and evaluates the models' performance using Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). Results indicate OLS outperforms ARIMA, with lower MSE (45.79 vs. 284.83) and MAPE (0.0026 vs. 0.0066). This study contributes nuanced insights for market participants, investors, and researchers to comprehend commodity market behaviour, particularly in gold, emphasizing the importance of accurate prediction methods in strategic decision-making.
Co-Authors Alexander, Edwin Anggelia, Dita Herijanto, Christopher Kevin Palis, Yohanes Junardi Riti, Yosefina Finsensia