PREDIKSI HARGA EMAS MENGGUNAKAN METODE AVERAGE BASED FUZZY TIME SERIES

This research aims to analyze and predict gold prices using the Average Based Fuzzy Time Series method. By utilizing historical data on daily gold prices from March 2034 to July 2024, this research identifies existing patterns and trends to produce accurate predictions. The method used Average Based Fuzzy Time Series (ABFTS) allows processing data with irregular fluctuations, thus providing more stable and reliable results. The analysis results show that the developed forecasting model has good accuracy, measured by the Mean Absolute Percentage Error (MAPE), which shows a low error rate. This research also identifies factors that influence gold prices, providing useful insights for investors in making investment decisions. Thus, this research makes a significant contribution to the field of commodity price forecasting, especially gold, and offers practical recommendations for investors and policy makers

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Publication Details

Journal
Jurnal Lebesgue Jurnal Ilmiah Pendidikan Matematika Matematika dan Statistika
Published
2024-12-12
DOI
https://doi.org/10.46306/lb.v5i3.778
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

PREDIKSI HARGA EMAS MENGGUNAKAN METODE AVERAGE BASED FUZZY TIME SERIES

Yusmet Rizal, Suci Ramadhani
Jurnal Lebesgue Jurnal Ilmiah Pendidikan Matematika Matematika dan Statistika
Data Mining and Machine Learning Applications
article

PREDIKSI HARGA EMAS MENGGUNAKAN METODE AVERAGE BASED FUZZY TIME SERIES

Yusmet Rizal, Suci Ramadhani
article en

Abstract

This research aims to analyze and predict gold prices using the Average Based Fuzzy Time Series method. By utilizing historical data on daily gold prices from March 2034 to July 2024, this research identifies existing patterns and trends to produce accurate predictions. The method used Average Based Fuzzy Time Series (ABFTS) allows processing data with irregular fluctuations, thus providing more stable and reliable results. The analysis results show that the developed forecasting model has good accuracy, measured by the Mean Absolute Percentage Error (MAPE), which shows a low error rate. This research also identifies factors that influence gold prices, providing useful insights for investors in making investment decisions. Thus, this research makes a significant contribution to the field of commodity price forecasting, especially gold, and offers practical recommendations for investors and policy makers

Jurnal Lebesgue Jurnal Ilmiah Pendidikan Matematika Matematika dan StatistikaVol. 5(3)
State University of Padang (ID)
Openalex Percentile: Top 61%
Data Mining and Machine Learning Applications
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