IV. PERFORMANCE CRITERIA OF THE MOD

IV. PERFORMANCE CRITERIA OF THE MODELS
The performance of the model is related with how close are
the prediction values for test data and the observed values.
Three different prediction consistency criteria are used in
order to compare the performances of obtained smoothing
spline and kernel regression. These are mean square error
(MSE) (or root mean square error (RMSE)), mean absolute
error (MAE) and mean absolute percentage error (MAPE)
respectively. These criteria are defined as follows:

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Hasil (Bahasa Indonesia) 1: [Salinan]

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IV. PERFORMANCE CRITERIA OF THE MODELSThe performance of the model is related with how close arethe prediction values for test data and the observed values.Three different prediction consistency criteria are used inorder to compare the performances of obtained smoothingspline and kernel regression. These are mean square error(MSE) (or root mean square error (RMSE)), mean absoluteerror (MAE) and mean absolute percentage error (MAPE)respectively. These criteria are defined as follows:

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IV. KRITERIA KINERJA DARI MODEL
Kinerja model ini terkait dengan seberapa dekat
nilai prediksi untuk data uji dan nilai-nilai yang diamati.
Tiga kriteria prediksi konsistensi yang berbeda yang digunakan dalam
rangka untuk membandingkan kinerja yang diperoleh smoothing
spline dan regresi kernel. Ini adalah rata-rata kesalahan persegi
(MSE) (atau akar berarti persegi error (RMSE)), berarti mutlak
kesalahan (MAE) dan berarti persentase kesalahan mutlak (MAPE)
masing-masing. Kriteria ini didefinisikan sebagai berikut:

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