PERAMALAN TINGKAT INFLASI INDONESIA MENGGUNAKAN NEURAL NETWORK BACKPROPAGATION BERBASIS METODE TIME SERIES
Keywords:
Neural Network Backpropagation, Mean Square Error, Data Mining, Inflation, Metode Time Series
Abstract
In this study will be used back propagation neural network method to predict the monthly inflation rate in Indonesia. In the results of the data analysis is concluded that the performance of back propagation neural network that formed by the training data and validated by testing data generates prediction accuracy rate is very good with a mean square error (MSE) is 0.0171. By using a moving average to forecast the independent variables obtained the rate of inflation in the month of July 2014 is 0.514, by using exponential smoothing to forecast the independent variables obtained by the rate of inflation in the month of July 2014 is 0.45, and by using seasonal method to forecast the independent variables obtained by the rate of inflation in the month of July 2014 is 0.93.References
Alpaydin, Ethem. (2010). Introduction to Machine Learning. London: The MIT Press.
Badan Pusat Statistik (BPS). 2014. Inflasi, Diunduh pada tanggal 03 Agustus 2014.
Gorunescu, Florin (2011). Data Mining: Concepts, Models, and Techniques. Verlag Berlin Heidelberg: Springer
Han, J.,&Kamber, M. (2006).Data Mining Concept and Tehniques.San Fransisco: Morgan Kauffman.
Kusrini,&Luthfi, E. T. (2009).Algoritma Data Mining. Yogyakarta: Andi Publishing.
Kusumadewi, Sri (2010). Pengantar Jaringan Syaraf Tiruan. Yogyakarta.Teknik Informatika FT UII.
Larose, D. T. (2005).Discovering Knowledge in Data. New Jersey: John Willey & Sons, Inc.
Maimon, Oded&Rokach, Lior. (2005). Data Mining and Knowledge Discovery Handbook. New York: Springer
Myatt, Glenn J. (2007). Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining. New Jersey: John Wiley & Sons, Inc.
Purnomo, M. H., & Kurniawan, A. (2006). Supervised Neural Network dan Aplikasinya. Yogyakarta: Graha Ilmu.
Puspitaningrum, Diyah (2006). Pengantar Jaringan Syaraf Tiruan. Yogyakarta: Andi Offset.
Santoso, Budi. (2007). Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis. Yogyakarta: Graha Ilmu.
Shukla, A., Tiwari, R., & Kala, R. (2010). Real Life Applications of Soft Computing. United States of America on Taylor and Francis Group, LLC.
Sogala, Satchidananda S. (2006). Comparing the Efficacy of the Decision Trees with Logistic Regression for Credit Risk Analysis. India.
Sugiyono, (2009). Metode Penelitian Bisnis. Bandung: Alfabeta
Badan Pusat Statistik (BPS). 2014. Inflasi, Diunduh pada tanggal 03 Agustus 2014.
Gorunescu, Florin (2011). Data Mining: Concepts, Models, and Techniques. Verlag Berlin Heidelberg: Springer
Han, J.,&Kamber, M. (2006).Data Mining Concept and Tehniques.San Fransisco: Morgan Kauffman.
Kusrini,&Luthfi, E. T. (2009).Algoritma Data Mining. Yogyakarta: Andi Publishing.
Kusumadewi, Sri (2010). Pengantar Jaringan Syaraf Tiruan. Yogyakarta.Teknik Informatika FT UII.
Larose, D. T. (2005).Discovering Knowledge in Data. New Jersey: John Willey & Sons, Inc.
Maimon, Oded&Rokach, Lior. (2005). Data Mining and Knowledge Discovery Handbook. New York: Springer
Myatt, Glenn J. (2007). Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining. New Jersey: John Wiley & Sons, Inc.
Purnomo, M. H., & Kurniawan, A. (2006). Supervised Neural Network dan Aplikasinya. Yogyakarta: Graha Ilmu.
Puspitaningrum, Diyah (2006). Pengantar Jaringan Syaraf Tiruan. Yogyakarta: Andi Offset.
Santoso, Budi. (2007). Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis. Yogyakarta: Graha Ilmu.
Shukla, A., Tiwari, R., & Kala, R. (2010). Real Life Applications of Soft Computing. United States of America on Taylor and Francis Group, LLC.
Sogala, Satchidananda S. (2006). Comparing the Efficacy of the Decision Trees with Logistic Regression for Credit Risk Analysis. India.
Sugiyono, (2009). Metode Penelitian Bisnis. Bandung: Alfabeta
Published
2014-09-15
How to Cite
Amrin, A. (2014). PERAMALAN TINGKAT INFLASI INDONESIA MENGGUNAKAN NEURAL NETWORK BACKPROPAGATION BERBASIS METODE TIME SERIES. Jurnal Techno Nusa Mandiri, 11(2), 129-136. Retrieved from https://ejournal.nusamandiri.ac.id/index.php/techno/article/view/506
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