Prediksi Jumlah Kunjungan Wisatawan Mancanegara ke Indonesia

Meimela, Aida

doi:10.36276/mws.v19i1.64

Cited by 2 publications

(3 citation statements)

References 4 publications

(3 reference statements)

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“…In addition to using the local polynomial regression approach, this research also makes predictions using the Autoregressive Integrated Moving Average (ARIMA) method. The advantages of ARIMA are its flexible nature following the pattern of the data, a fairly high level of forecasting accuracy, simplicity, suitability for use in forecasting a number of variables quickly, and cheapness because it only requires historical data [7]. Relevant previous research related to predicting the number of foreign tourist visits to Indonesia has been conducted by [7] which examines the same topic but uses the ARIMA method only.…”

Section: Introductionmentioning

confidence: 99%

“…The advantages of ARIMA are its flexible nature following the pattern of the data, a fairly high level of forecasting accuracy, simplicity, suitability for use in forecasting a number of variables quickly, and cheapness because it only requires historical data [7]. Relevant previous research related to predicting the number of foreign tourist visits to Indonesia has been conducted by [7] which examines the same topic but uses the ARIMA method only. The novelty of this research is that it uses the local polynomial method, which is able to overcome the fluctuating pattern of data quite well.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Local Polynomial Regression and Arima in Predicting the Number of Foreign Tourist Visits to Indonesia

Pratama,

Suryono,

Auliyah

et al. 2024

BAREKENG: J. Math. & App.

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Indonesia is a country that has a variety of exotic tourist destinations and can attract tourists to visit. Currently, tourism is one of the sectors that plays a major role in driving the Indonesian economy. Various tourists, both domestic and foreign, are expected to continue to increase in number every year. Therefore, appropriate policies are needed from the government to develop the tourism sector so that it can be even better over time. This research aims to predict the number of foreign tourist visits to Indonesia using the Autoregressive Integrated Moving Average (ARIMA) model and local polynomial regression. The data used in this research is the number of foreign tourist visits per month from January 2017 to December 2022 obtained from the the Kemenparekraf website. This data is fluctuating so that the method a local polynomial approach is appropriate for this study. The data analysis method used are local polynomial regression and ARIMA model. In the ARIMA model there are assumptions that must be met. In this study, the ARIMA model obtained has met the assumption of residual normality but does not meet the assumption of homoscedasticity so that ARIMA modeling cannot be continued and analysis is only carried out with local polynomial regression. The result of this study is a prediction of future tourist visits. The MAPE value of the local polynomial regression approach is 1.43% which is categorized as a prediction with high accuracy because the value is less than 10%. Thus, the local polynomial regression approach is very well used to predict the number of foreign tourist visits to Indonesia.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of Local Polynomial Regression and Arima in Predicting the Number of Foreign Tourist Visits to Indonesia

Pratama,

Suryono,

Auliyah

et al. 2024

BAREKENG: J. Math. & App.

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“…Aida Meimela's 2021 research entitled "Prediction of the Number of Foreign Tourist Visits to Indonesia" uses the seasonal ARIMA method to predict the number of foreign tourist visits to Indonesia, with prediction results having a good level of accuracy. (Meimela, 2021). While the research that will be conducted is predicting the number of tourist visits to NTB using the SVR method, which has perfect accuracy based on the results of previous studies.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Support Vector Regression in the Prediction of the Number of Tourist Visits to the Province West Nusa Tenggara (Ntb)

Zaeniah

2022

pilar

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— Indonesia has a variety of interesting tourist destinations to visit in each region. One area that is used as a favorite tourist destination is the Province of West Nusa Tenggara (NTB). Data The number of tourists visiting the NTB province from 2014 to 2020 tends to change based on data obtained from the Website of the NTB Provincial Tourism Office. The data on the number of visitors will continue to change, even if there is a possibility that it will increase. This can lead to the unpreparedness of the government and other tourism actors in providing the facilities and infrastructure needed by visitors when there is an increase in the number of tourist visits coming to NTB. Therefore, it is necessary to predict the number of tourist visits to NTB with accurate results. In this study, predictions of the number of tourist visits to the Province of NTB were made using the support vector regression method. This research resulted in an application to predict the number of tourist visits to NTB based on Event, Month, and Year. so that it can provide predictive results that are close to the actual value under normal conditions. The data used in this study is data on the number of tourist visits in 2017-2021 and events held in 2017-2021.

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Prediksi Jumlah Kunjungan Wisatawan Mancanegara ke Indonesia

Cited by 2 publications

References 4 publications

Comparison of Local Polynomial Regression and Arima in Predicting the Number of Foreign Tourist Visits to Indonesia

Comparison of Local Polynomial Regression and Arima in Predicting the Number of Foreign Tourist Visits to Indonesia

Implementation of Support Vector Regression in the Prediction of the Number of Tourist Visits to the Province West Nusa Tenggara (Ntb)

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