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All Journal Jupiter TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Informatika Global Jurnal Abdimas Mandiri Sibatik Journal : Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan
Yulianti, Evi
Universitas Indo Global Mandiri
Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science Social Sciences

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Journal : Jupiter

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Ensemble Backpropagation Neural Network Dalam Memprediksi Inflasi Imelda Saluza; Lastri Widya Asuti; Dhamayanti; Evi Yulianti
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 15 No 1d (2023): Jupiter Edisi April 2023
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281./6613/15.jupiter.2023.04

Abstract

Global economic volatility that continues to experience spikes is a particular concern for countries in the world, including Indonesia. This is due to the impact that will occur if it continues to increase which can result in a country's economic recession. A country must pay attention to the pressure on the inflation rate. Unreasonable inflation rate volatility can have a negative impact on economic growth. Therefore, it is very important to accurately predict future inflation rates so that it becomes important information for economic policy makers. Inflation prediction is one of the problems that has been widely researched because the data is non-stationary and non-linear, so an algorithm is needed that can overcome this problem. One of the algorithms that can be used is the Backpropagation Neural Network (BPNN), but the BPNN network in its application has many parameters that must be determined so that it often causes overfitting. For this reason, instead of learning from multiple models, the ensemble method is used. The main benefit of this method is to reduce overfitting and at the same time maintain the accuracy and diversity of the BPNN network.
Co-Authors Agrios Kanny Agustina Heryati Ahmad Sanmorino Desi Ratna Sari Dhamayanti Dhamayanti Dhamayanti Dona Marcelina Dwi Ardya Garini Effy Luthfiyyah Nur Faradillah Faradillah Gizcha Putri Destriana Imelda Saluza Imelda Saluza, Imelda K. Ghazali K. Ghazali Lastri Widya Astuti Lastri Widya Asuti Rahma Annissa Sartika, Dewi Sukemi Sukemi Suryati, Suryati Suzan Agustri