DATA MINING DENGAN REGRESI LINIER BERGANDA UNTUK PERAMALAN TINGKAT INFLASI

  • Amrin Amrin Manajemen Informatika AMIK BSI Jakarta
Keywords: Data Mining, Inflasi, multiple linear regression method

Abstract

In this study will be used multiple linear regression method to predict the monthly inflation rate in Indonesia. In the results of the data analysis is concluded  that the model of multiple linear regression obtained in this study is Y= 0,241X1 + 0,164X+ 0,271X+  0,07X4 + 0,040X5 + 0,060X6 + 0,169X7 - 0,010. The coefficient of regression value is 0,999 and coefficient of determination value is 0,997. the performance of multiple linear regression that formed by the training data and validated by testing data generates prediction accuracy rate is very good with a Mean Absolute Deviation (MAD) is 0.0380, a Mean Square Error  (MSE) is 0.0023, and a Root Mean Square Error (RMSE) is 0.0481.

References

Badan Pusat Statistik (BPS). 2015. Inflasi, Diunduh pada tanggal 03 Desember 2015.

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.

Priyatno, Dwi. (2008). Mandiri Belajar SPSS. Yogyakarta: MediaKom.

Riana, Dwiza. (2012). Statistika Deskriptif itu mudah. Jakarta: Jelajah Nusa.

Sumathi, & S., Sivanandam, S.N. (2006). Introduction to Data Mining and its Applications. Berlin Heidelberg New York: Springer

Supranto, J. 2004. Analisis Multivariat Arti dan Interpretasi. Rineka Cipta. Jakarta.

Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining: Practical Machine Learning and Tools. Burlington: Morgan Kaufmann Publisher.
Published
2016-03-15
How to Cite
Amrin, A. (2016). DATA MINING DENGAN REGRESI LINIER BERGANDA UNTUK PERAMALAN TINGKAT INFLASI. Jurnal Techno Nusa Mandiri, 13(1), 74-79. Retrieved from http://ejournal.nusamandiri.ac.id/index.php/techno/article/view/220
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