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All Journal Wacana, Jurnal Sosial dan Humaniora Jurnal Saintek Lahan Kering Jurnal Pengabdian UNDIKMA Jurnal Diferensial Jurnal Info Sains : Informatika dan Sains AKM: Aksi Kepada Masyarakat ABDI UNISAP: Jurnal Pengabdian Kepada Masyarakat
Febrya Christin Handayani Buan
Fakultas Pertanian, Universitas Timor
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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Journal : Jurnal Info Sains : Informatika dan Sains

 1 
Comparison Of Radial Basis Function Neural Networks (RBFNN) And Autoregressive Moving Average (ARMA) Algorithms On Inflation Rate Prediction Models In Batam City Widya Reza; Febrya Christin Handayani Buan; Siti Nurmardia Abdussamad
Jurnal Info Sains : Informatika dan Sains Vol. 14 No. 02 (2024): Informatika dan Sains , Edition, June 2024
Publisher : SEAN Institute

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Abstract

The inflation rate in the city of Batam from January 2023 to April 2024 continues to fluctuate, so an accurate prediction model is needed so that inflation control can be carried out optimally. In this study, we conducted a comparative analysis between the Radial Basis Function Neural Network (RBFNN) method and the Autoregressive Moving Average (ARMA) model in predicting the inflation rate. The data used is historical data on the inflation rate of Batam City from January 2009 to April 2024. The results of the analysis show that the RBFN method with an MSE value of 0.239 is able to provide a more accurate prediction compared to the ARMA model (2.3) with an MSE value of 0.246 in predicting the inflation rate in Batam City. This is due to the RBFN's ability to capture complex and non-linear patterns contained in inflation data. In addition, the performance of RBFNN is also affected by the number of neurons and the basis function used. Thus, the results of this study show that the RBFN method can be an effective and efficient alternative in predicting the inflation rate in Batam City.
Co-Authors Agustinus Yoseph Leu Aldianus Bria Azor Yulianus Tefa Christofel John Bernhard Sendow Debora Chrisinta Dian Grace Ludji Dian I Y Ribka Stefanie Wongso, Paini Sri Widyawati, Tarsisius D B Fenny A K, Evelyn L W, Dian I Y Emanuel Suri Faustinus Kadha Hadi Santoso Laurentinus Indah Epriliati Januario Resky A. Sekab Josua Sahala Kadju, Fransiskus Yulius Dhewa Marianus Nahas Mata, Meri Helsiana Misail Epainetus Silla Nurjannah Nurjannah Rahma Fitriani Sipayung, Boanerges Putra Siti Nurmardia Abdussamad Venantius Oskar Nahak Widya Reza Widya Reza Wilda Lumban Tobing Zofar Agluis Banunaek Zofar Agluis Banunaek