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Journal : Techno Nusa Mandiri : Journal of Computing and Information Technology

DATA MINING DENGAN REGRESI LINIER BERGANDA UNTUK PERAMALAN TINGKAT INFLASI Amrin Amrin
Jurnal Techno Nusa Mandiri Vol 13 No 1 (2016): Techno Nusa Mandiri : Journal of Computing and Information Technology Periode Ma
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

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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 + 0,164X2 + 0,271X3 + 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.
ANALISA KELAYAKAN PEMBERIAN KREDIT MOBIL DENGAN MENGGUNAKAN NEURAL NETWORK BACKPROPAGATION Amrin Amrin
Jurnal Techno Nusa Mandiri Vol 12 No 1 (2015): Techno Nusa Mandiri : Journal of Computing and Information Technology Periode Ma
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (590.911 KB)

Abstract

Problems are often encountered in the provision of credit is to determine lending decisions to someone, while other issues are not all credit payments can run well. Among the causes are errors of judgment in making credit decisions. In this study will be used back propagation neural network method to analyze the feasibility of providing car loans. From the test results to measure the performance of the method is to use testing methods Confusion Matrix and ROC curve, it is known that the method of back propagation neural network has a value of 89% accuracy and AUC value of 0.831. This shows that the model produced, including the classification is quite good because it has the AUC values between 0.8-0.9.
PERAMALAN TINGKAT INFLASI INDONESIA MENGGUNAKAN NEURAL NETWORK BACKPROPAGATION BERBASIS METODE TIME SERIES Amrin Amrin
Jurnal Techno Nusa Mandiri Vol 11 No 2 (2014): Techno Nusa Mandiri : Journal of Computing and Information Technology Periode Se
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (313.198 KB)

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.