View Item 
      •   IPB Repository
      • Dissertations and Theses
      • Undergraduate Theses
      • UT - School of Data Science, Mathematic and Informatics
      • UT - Mathematics
      • View Item
      •   IPB Repository
      • Dissertations and Theses
      • Undergraduate Theses
      • UT - School of Data Science, Mathematic and Informatics
      • UT - Mathematics
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Analisis Prediksi Harga Emas Menggunakan Model ARIMAX

      Thumbnail
      View/Open
      Cover (1.895Mb)
      Fulltext (2.128Mb)
      Lampiran (1.722Mb)
      Date
      2025
      Author
      Azzahrah, Soraya
      Silalahi, Bib Paruhum
      Budiarti, Retno
      Metadata
      Show full item record
      Abstract
      Emas telah lama diakui sebagai aset safe-haven yang mampu mempertahankan nilainya di tengah ketidakpastian ekonomi global. Oleh karena itu, kemampuan untuk memprediksi harga emas dengan akurat menjadi sangat penting, terutama bagi investor dan pembuat kebijakan dalam merumuskan keputusan yang tepat terlebih banyak faktor yang bisa mempengaruhi harga emas salah satunya ialah Index Impor Emas. Metode yang cocok ialah ARIMAX (Autoregressive Integrated Moving Average with Exogenous Variables), yang mampu mengintegrasikan variabel eksternal, seperti indeks impor emas, dalam model prediksinya. Penelitian ini menggunakan 57 data, yang terdiri dari 47 data pelatihan dan 10 data pengujian, dengan tujuan mengembangkan model secara akurat. Hasil penelitian menunjukkan bahwa model ARIMAX(0,1,1) adalah yang paling efektif, dengan nilai AIC sebesar 527.41, yang lebih rendah dibandingkan model lainnya. Model ini juga menunjukkan tingkat akurasi yang baik, dengan MAPE sebesar 2.86% pada data pelatihan dan 8.11% pada data uji, serta korelasi 0.95 antara data aktual dan prediksi. Hasil prediksi harga emas untuk tahun 2024 menunjukkan tren kenaikan yang signifikan, dengan harga tertinggi diprediksi mencapai $2653.30 per ounce pada bulan Desember 2024.
       
      Gold has long been recognized as a safe-haven asset capable of maintaining its value amid global economic uncertainty. Therefore, the ability to accurately predict gold prices is crucial, especially for investors and policymakers in making informed decisions, as numerous factors can influence gold prices, one of which is the Gold Import Index. The appropriate method is ARIMAX (Autoregressive Integrated Moving Average with Exogenous Variables), which can incorporate external variables, such as the gold import index, into its predictive model. This study uses 57 data points, consisting of 47 training data and 10 testing data, with the aim of developing an accurate model. The results show that the ARIMAX(0,1,1) model is the most effective, with an AIC value of 527.41, which is lower than other models. This model also shows a good level of accuracy, with a MAPE of 2.86% on the training data and 8.11% on the test data, as well as a correlation of 0.95 between the actual data and the predictions. The gold price predictions for 2024 show a significant upward trend, with the highest price predicted to reach $2,653.30 per ounce in December 2024.
       
      URI
      http://repository.ipb.ac.id/handle/123456789/168844
      Collections
      • UT - Mathematics [89]

      Copyright © 2020 Library of IPB University
      All rights reserved
      Contact Us | Send Feedback
      Indonesia DSpace Group 
      IPB University Scientific Repository
      UIN Syarif Hidayatullah Institutional Repository
      Universitas Jember Digital Repository
        

       

      Browse

      All of IPB RepositoryCollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

      My Account

      Login

      Application

      google store

      Copyright © 2020 Library of IPB University
      All rights reserved
      Contact Us | Send Feedback
      Indonesia DSpace Group 
      IPB University Scientific Repository
      UIN Syarif Hidayatullah Institutional Repository
      Universitas Jember Digital Repository