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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal of Indonesian Tourism and Policy Studies Parameter: Jurnal Matematika, Statistika dan Terapannya
Sellyra, Eirene Christina
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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DETERMINING FACTORS OF THE NUMBER OF TOURISTS IN 30 COUNTRIESUSING GEOGRAPHYCALLY WEIGHTED PANEL REGRESSION Sellyra, Eirene Christina; Anggara, Dimas; Ferezagia, Debrina Vita
Journal of Indonesian Tourism and Policy Studies Vol. 7, No. 2
Publisher : UI Scholars Hub

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Abstract

This study aims to determine the factors that influence the number of tourists in 30 countries. The research method used is quantitative.The research data is secondary data. The research unit is in the form of 30 countries of observation. The research variables were measured in three years of observation, namely 2018, 2019 and 2020. The variables used were the number of tourists (people), the currency exchange rate of tourist countries against the rupiah, GDP per capita, population density, visa-free visit, consumer price index, life expectancy, economic growth and imports. The results obtained are that the factors that influence the number of foreign tourists visiting the observation countries in 2018 to 2020 vary depending on the area of the observation country. Countries with the number of foreign tourist visits influenced by population density and imports are Malaysia and Singapore. Countries with the number of foreign tourist visits influenced by economic growth and imports are China and South Korea. Countries with the number of foreign tourist visits that are influenced by import factors are countriesBangladesh, Brunei Darussalam, Burma, Hong Kong, India, Pakistan, Thailand and Vietnam. While the rest are not influenced by any factor in the model with a 90% confidence level.The Geographically Weighted Panel Regression (GWPR) model that has been formed is appropriate and has a significant difference compared to the panel regression model due to the location effect which also significantly influences the number of foreign tourist visits to the observed countries. The model has an adjusted r-square value of50.84874% which means the model is able to explain the variance of the number of foreign tourists visiting50.84874% only by variablepopulation density, economic growth, and imports. Meanwhile, the rest is influenced by other variables outside the model.
MAXIMUM EXPONENTIALLY WEIGHTED MOVING AVERAGE WITH MEASUREMENT ERROR (USING COVARIATE METHOD) USING AUXILIARY INFORMATION FOR CEMENT QUALITY CONTROL Sellyra, Eirene Christina; Ahsan, Muhammad; Wibawati, Wibawati
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1333-1348

Abstract

The main quality characteristic at XYZ Inc. that should be observed is Compressive Strength. Cement production quality control is carried out on the average and process variability jointly with the Max-EWMA control chart. Measurement error can be found in the Compressive Strength. It can affect the sensitivity of the control chart, so quality control will be carried out by considering the presence of measurement error. Handling measurement errors can be done through three approaches (covariate method, multiple measurements, and linearly increasing variance). This research only focuses on the covariate method. Auxiliary variables also explain variance in the production process, so they are also considered in this research, with Blaine used as an auxiliary variable. Therefore, the control chart that will be formed is the Max-EWMA ME (Covariate) AI. The Max-EWMA and Max-EWMA ME (Covariate) AI control charts show that the XYZ Inc. cement production process based on variability and process averages is simultaneously statistically controlled. The controlled Max-EWMA control chart has an upper control limit of UCL=1.503018, and parameters dan . Max-EWMA ME (Covariate) AI has in-control parameters . The Max-EWMA ME (Covariate) AI control chart is more sensitive than the Max-EWMA control chart. Cement production capabilities based on Compressive Strength have a Cpl and Cpk capability index of 1.54, which means that the cement production process is capable, consistent, and has high accuracy so that the quality has reached the target.
ADAPTIVE EXPONENTIALLY WEIGHTED MOVING AVERAGE WITH MEASUREMENT ERROR (COVARIATE) WITH AUXILIARY INFORMATION MAXIMUM FOR CEMENT QUALITY CONTROL Sellyra, Eirene Christina; Ahsan, Muhammad; Wibawati, Wibawati
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 1 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i1pp29-46

Abstract

The Shewhart control chartexhibits limitations in detecting small process shifts and monitors the mean and variance separately. To address these shortcomings, this study introduces the Adaptive EWMA with Measurement Error (Covariate Method) and Auxiliary Information Max (AEWMA ME C AI Max) control chart. This novel approach integrates memory-based monitoring, joint mean-variance detection, measurement error correction through the covariate method, utilization of auxiliary variables, and adaptive adjustment mechanisms to enhance sensitivity across various shift magnitudes. The AEWMA ME C AI Max chart was applied to cement production data from PT XYZ, using Blaine fineness as an auxiliary variable for monitoring compressive strength. Comparative analysis demonstrates that the adaptive chart consistently produces control statistics closer to the upper control limit compared to the non-adaptive Max-EWMA ME C AI chart, validating its superior sensitivity in shiftdetection. Furthermore, the cement production process at PT XYZ was found to be statistically capable, with a lower capability index (Ppl) and process performance index (Ppk) of 1.45, indicating consistent compliance with lower specification limits and centered process performance. These results affirm the practical effectiveness of the AEWMA ME C AI Max chart in enhancing process monitoring and capability assessment in industrial applications.
Co-Authors Ahsan, Muhammad ANGGARA, DIMAS Ferezagia, Debrina Vita Wibawati Wibawati