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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Sainstek : Jurnal Sains dan Teknologi
Afrimayani, Afrimayani
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation Other

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KONSTRUKSI MODEL HUBUNGAN DUA VARIABEL DENGAN ANALISIS REGRESI SPLINE Yozza, Hazmira; Afrimayani, Afrimayani
Sainstek : Jurnal Sains dan Teknologi Vol 11, No 1 (2019)
Publisher : IAIN Batusangkar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (365.193 KB) | DOI: 10.31958/js.v11i1.1279

Abstract

This paper discussed about the construction of a model the relationship between a dependent variabel and a independent variabel using spline analysis regression.  This methods is usually used when the relationship between variables is unknown.  This methods is applied to construct the growth curve for children under 3 years of age in Padang.
COMPARISON OF DOUBLE EXPONENTIAL SMOOTHING AND FUZZY TIME SERIES MARKOV CHAIN IN FORECASTING FOREIGN TOURIST ARRIVALS Putri, Darvi Mailisa; Afrimayani, Afrimayani; Hasibuan, Lilis Harianti; Ul Hasanah, Fitri Rahmah; Jannah, Miftahul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1817-1828

Abstract

Foreign tourist arrivals are one of the factors that make a positive contribution to a country's economy, especially the addition of foreign exchange. This activity is important for the tourism industry and the government to make policies for progress in the tourism sector. This research aims to forecast data on foreign tourist arrivals, especially land routes. This data set, which is a monthly time series covering the period from January 2018 to October 2023, is sourced from the Central Statistics Agency (BPS). The DES technique is a method that quickly adapts to changes in data patterns and can lessen the impacts of random fluctuations, resulting in more stable estimates. Meanwhile, the FTS-MC approach can handle large data variations by utilizing fuzzy sets. Furthermore, combining fuzzy time series with Markov Chains increases forecast accuracy by taking into account state transitions and probability. The research demonstrates that the DES method produces the MAPE value of 0.108530 or 10% which is obtained from the alpha value of 0.9 and beta 0.2. The MAPE 0.108530 means that the ability of the forecasting model is classified as a good category. In the FTS-MC method, the forecast data is close to the actual data. This is indicated by the MAPE value obtained of 0.086850 or 8%, which means that the ability of the forecasting model is very good. Based on the analysis of the two methods, it is concluded that the FTS-MC method is better applied to data on land-based foreign tourist arrivals.
Co-Authors Darvi Mailisa Putri Hasibuan, Lilis Harianti Hazmira Yozza Miftahul Jannah Ul Hasanah, Fitri Rahmah