Model Debit Daerah Aliran Sungai Jangkok  Berdasarkan Hasil Prediksi Model Statistical Downscaling Nonparametrik Kernel Curah Hujan Dan Temperatur

Hadijati, Mustika; Irwansyah,

doi:10.14710/medstat.12.2.236-245

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…Unlike other linear estimators, kernel estimators have special expertise in optimizing bandwidth usage. In this study, the selection of optimal bandwidth is also an important consideration in obtaining a good estimate, which is balanced between bias and variance [15].…”

Section: B Kernel Function Estimator In Classical Nonparametric Time ...mentioning

confidence: 99%

“…While the final estimation result of the b value is 𝑏 = 𝑦 𝑖 − 𝒘𝜑(𝒙 𝑖 ) − 𝜀 for 0 ≤ 𝛼 𝑖 ≤ 𝐶 and 𝑏 = 𝑦 𝑖 − 𝒘𝜑(𝒙 𝑖 ) + 𝜀 for 0 ≤ 𝛼 𝑖 * ≤ 𝐶 so that the SVR function can be formulated as in the following equation (15):…”

Section: Support Vector Regression (Svr) In Modern Nonparametric Time...mentioning

confidence: 99%

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Prediction of Rupiah Exchange Rate Against US Dollar Using Kernel-Based Time Series Approach

Sifa,

Galena,

Mardianto

et al. 2024

Inferensi

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Fluctuations in the rupiah exchange rate against the United States Dollar from 2020 to early 2024 have been analyzed using classical and modern time series approaches. In this study, the classical time series approach based on Gaussian Kernel successfully provides predictions with an RMSE value of 57.5722 and a MAPE of 0.29%. Meanwhile, the modern approach with RBF Kernel SVR shows an RMSE value of 74.9201 and a MAPE of 0.41%. The results of the model performance comparison show the superiority of the classical approach with the Gaussian Kernel in predicting the rupiah exchange rate against the US Dollar as an impact of the Federal Funds Rate (FFR) policy. Therefore, it is recommended to use the classical time series method based on the Gaussian Kernel in dealing with the impact of the FFR policy to improve the accuracy of predicting the Rupiah exchange rate against the United States Dollar. This research supports the achievement of the 8th Sustainable Development Goals (SDGs) related to economic and social matters while providing a better understanding of currency exchange rate fluctuations and providing recommendations that can help in managing economic risks related to global monetary policy.

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Section: B Kernel Function Estimator In Classical Nonparametric Time ...mentioning

confidence: 99%

Section: Support Vector Regression (Svr) In Modern Nonparametric Time...mentioning

confidence: 99%

Prediction of Rupiah Exchange Rate Against US Dollar Using Kernel-Based Time Series Approach

Sifa,

Galena,

Mardianto

et al. 2024

Inferensi

View full text Add to dashboard Cite

show abstract

“…Some used the quartic kernel function, such as in modeling the water discharge in the Jangkok Watershed, Lombok Island, by autoregressive pre-whitening [6]; also in predicting the daily rainfall and simulating monthly rainfall data of Dodokan Watershed in the statistical downscaling model [8], [5]. Other studies also used the Gaussian kernel function in the studies, such as expecting the temperature in Selaparang Lombok and predicting the monthly rainfall of Sembalun Station in a statistical downscaling model [7], [9]; also in modeling crude birth rate in West Nusa Tenggara Province [15]. This study estimated and determined the kernel regression curves using seven (7) types of kernel functions.…”

Section: Joji Ardian Pembargi Ena Setiawana Rosanti Wahidatussolihah ...mentioning

confidence: 99%

Modeling the Hotel Tax Revenue in Central Lombok using Nonparametric Regression

Fitriyani,

Pembargi,

Setiawana

et al. 2023

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In Central Lombok Regency, the hotel tax is one of the highest incomes contributing to Regional Original Revenue. A hotel tax is a tax on services provided by the hotel. This research aims to estimate the nonparametric kernel regression curve on hotel tax revenue data in Central Lombok. The method used is nonparametric kernel regression analysis with the seven kernel functions. The results of the analysis with the Generalized Cross Validation (GCV) criteria, the optimal bandwidth values generated by the seven kernel functions have varying values. Although the bandwidth values vary, the resulting estimation results are similar, and the comparison of the Mean Square Error (MSE) values of the seven kernel functions is not significantly different.

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“…Former studies have utilized nonparametric kernel regression to model time series data, such as in modeling climate data in Lombok Island by autoregressive prewhitening. The approach was also used in the statistical downscaling model in several data (Hadijati et al, 2016a(Hadijati et al, , 2016b(Hadijati et al, , 2017(Hadijati et al, , 2021(Hadijati et al, , 2022. Other researchers also used the kernel approach, such as modeling crude birth rate and malnourished children in West Nusa Tenggara Province (Pratiwi et al, 2020, Sauri et al, 2020, modeling hotel tax revenue, and forecasting local original income in Central Lombok (Pembargi et al, 2023a(Pembargi et al, , 2023b.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Economic Growth Rate in West Nusa Tenggara Province with Longitudinal Kernel Nonparametric Regression

Rizaldi,

Fitriyani,

Baskara

2024

EMJ

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Economic growth can indicate the success of economic development in people's lives, so it is essential to study the relationship between economic growth and factors that affect economic growth. Regression analysis is one of the most widely used statistical data analysis methods to determine the relationship pattern between the independent and dependent variables. Three methods can be used to estimate the regression curve, one of which is nonparametric regression. Economic growth data is one form of longitudinal data, with observations of independent subjects, with each subject being observed repeatedly over different periods. Kernel nonparametric regression model applications can be used for longitudinal data. This research aims to estimate the curve and get the best regression model. In this research, the smoothing technique chosen to estimate the nonparametric regression model for longitudinal data is the kernel triangle estimator, which can be obtained by minimizing the square of error using Weighted Least Squares (WLS) and selecting the optimum bandwidth using the Generalized Cross Validation (GCV) method. This study uses the economic growth rate in West Nusa Tenggara as the dependent variable and the human development index, population density, general allocation funds, local revenue, and labor force participation as independent variables. The result showed that the model is less accurate because of the low value of the coefficient for determination and the high value of the mean absolute percentage error (MAPE). This can be caused by the selection of bandwidth intervals that are too small.

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Model Debit Daerah Aliran Sungai Jangkok Berdasarkan Hasil Prediksi Model Statistical Downscaling Nonparametrik Kernel Curah Hujan Dan Temperatur

Cited by 3 publications

References 6 publications

Prediction of Rupiah Exchange Rate Against US Dollar Using Kernel-Based Time Series Approach

Prediction of Rupiah Exchange Rate Against US Dollar Using Kernel-Based Time Series Approach

Modeling the Hotel Tax Revenue in Central Lombok using Nonparametric Regression

Modeling of Economic Growth Rate in West Nusa Tenggara Province with Longitudinal Kernel Nonparametric Regression

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