Repository logo
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Italiano
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Srpski (lat)
  • Suomi
  • Svenska
  • Türkçe
  • Tiếng Việt
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Српски
  • Yкраї́нська
  • Log In
    New user? Click here to register. Have you forgotten your password?
  • Communities & Collections
  • All of DSpace
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Italiano
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Srpski (lat)
  • Suomi
  • Svenska
  • Türkçe
  • Tiếng Việt
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Српски
  • Yкраї́нська
  • Log In
    New user? Click here to register. Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "Dwifattah, Muhammad Rizki"

Now showing 1 - 1 of 1
Results Per Page
Sort Options
  • No Thumbnail Available
    Item
    MODEL HYBRID SINGULAR SPECTRUM ANALYSIS DAN NEURAL NETWORK UNTUK PERAMALAN KENAIKAN NILAI INFLASI DI INDONESIA
    (Elfitra, 2023-12) Dwifattah, Muhammad Rizki; Bustami
    Current economic developments cause increasing inflation rates in a country. One of the statistical methods used to determine the increase in inflation values is forecasting using a non-parametric time series model. This research was carried out using Singular Spectrum Analysis and Neural Network as a non-parametric forecasting method with monthly data on inflation values in Indonesia from January 2003 - December 2022. This analysis was carried out by forming a square matrix from the research data so that eigenvalues and eigenvectors were obtained in each matrix. as many as 50. In the calculations, the forecast results obtained for the next 5 month period show insignificant increases and decreases. Based on the accuracy results, an error was obtained using MAPE with forecasting results for the inflation value of 9%, which can be said to be in the very good category.

DSpace software copyright © 2002-2025 LYRASIS

  • Cookie settings
  • Privacy policy
  • End User Agreement
  • Send Feedback