PREDIKSI HARGA EMAS DENGAN METODE LONG SHORT-TERM MEMORY

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dc.contributor.authorSholeh, Muhammad
dc.contributor.supervisorAlfirman, Alfirman
dc.date.accessioned2024-03-06T02:25:36Z
dc.date.available2024-03-06T02:25:36Z
dc.date.issued2023-11
dc.description.abstractPredicting gold prices holds significant importance in the realms of finance and investment, enabling market participants to make wiser decisions amidst gold price fluctuations. The training results of the Long Short-Term Memory (LSTM) model displayed remarkable performance, with a Mean Squared Error (MSE) value of 0.0034 and a Mean Absolute Percentage Error (MAPE) value of 7.13%. According to the MAPE criterion, this LSTM model's predictive capabilities can be categorized as highly accurate. These outcomes affirm the potential of the LSTM model in providing precise gold price predictions, aiding market participants in more informed decision-making. The forecasted gold prices using LSTM from January 1, 2023, to March 1, 2023, indicate a daily decrease in gold prices. Consequently, a prudent approach for market participants would be to engage in future gold purchases during the predicted gold price decrease in 2023.en_US
dc.description.sponsorshipFakultas Matematika dan Ilmu Pengetahuan Alamen_US
dc.identifier.citationPerpustakaanen_US
dc.identifier.otherElfitra
dc.identifier.urihttps://repository.unri.ac.id/handle/123456789/11347
dc.language.isoenen_US
dc.publisherElfitraen_US
dc.subjectData Scalingen_US
dc.subjectGold Price Predictionen_US
dc.subjectLSTMen_US
dc.subjectMAPEen_US
dc.subjectMSEen_US
dc.titlePREDIKSI HARGA EMAS DENGAN METODE LONG SHORT-TERM MEMORYen_US
dc.title.alternativeElfitraen_US
dc.typeArticleen_US

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