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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal of Mathematics Education and Science Limits: Journal of Mathematics and Its Applications
Suryono, Alda Fuadiyah
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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COMPARISON OF LOCAL POLYNOMIAL REGRESSION AND ARIMA IN PREDICTING THE NUMBER OF FOREIGN TOURIST VISITS TO INDONESIA Pratama, Bagas Shata; Suryono, Alda Fuadiyah; Auliyah, Nina; Chamidah, Nur
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0043-0052

Abstract

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.
Pemodelan Angka Harapan Hidup Negara G7 dengan Pendekatan Analisis Regresi Data Longitudinal Farizi, Muhammad Fikry Al; Maula, Sugha Faiz Al; Fajrina, Sofia Andika Nur; Hilma, Dzuria Hilma Qurotu Ain; Suryono, Alda Fuadiyah; Chamidah, Nur
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 1 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 1 Edisi Ma
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v22i1.3368

Abstract

Life expectancy is the average number of years of life a newborn baby will live in a given year. In general, life expectancy is a tool to evaluate government performance in improving community welfare. The aim of this research is prediction using longitudinal data regression analysis methods, namely Generalized Least Square with a Restricted Maximum Likelihood approach using a uniform correlation structure, Autoregressive (AR) (1), and Gaussian with factors that influence life expectancy, namely Tax to GDP ratio, Gross Domestic Product per Capita (GDPPC) and Health Expenditure per Capita from 2000-2020 in G7 countries. Based on the analysis results, it was found that tax revenues had a negative effect of 0.155 but the effect was not significant, GDP had a positive effect of 0.715 but had a significant effect, while health expenditure had a negative effect of 0.49 on Life Expectancy. The research results found that conditions in the G7 that were not ideal caused negative effects on taxes and health spending that were not in accordance with theory. The suggestions that can be given include tax reform from the source and its implementation, such as cigarette tax and sugary drink tax. In addition, it also provides suggestions to include universal health for a healthier and more prosperous society. This research is also in accordance with the aim of Sustainable Development Goals (SDGs) number 3, namely "Ensuring healthy lives and improving the welfare of all populations of all ages" and can be used as a policy reference for Indonesia.
Identifikasi Faktor yang Mempengaruhi Kemiskinan di Papua dengan Principal Component Analysis Ain, Dzuria Hilma Qurotu; Kusuma, Shalwa Oktavia; Zahrani, Vista Vanadya; Suryono, Alda Fuadiyah; Mardianto, M. Fariz Fadillah; Amelia, Dita; Ana, Elly
Journal of Mathematics Education and Science Vol. 7 No. 1 (2024): Journal of Mathematics Education and Science
Publisher : Universitas Nahdlatul Ulama Sunan Giri Bojonegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/james.v7i1.1336

Abstract

Penelitian ini bertujuan untuk menganalisis faktor-faktor kemiskinan terhadap pengentasan kemiskinan di Provinsi Papua. Metode yang digunakan yaitu Analisis Komponen Utama (AKU). Cakupan data yang digunakan dalam penelitian ini adalah data statistik kesejahteraan rakyat Provinsi Papua pada bulan Maret tahun 2021 yang diperoleh dari Badan Pusat Statistik (BPS). Hasil penelitian ini menunjukkan bahwa faktor-faktor yang mempengaruhi kemiskinan di Kabupaten dan Kota Provinsi Papua dapat dikategorikan menjadi tiga komponen yaitu Komponen 1 : “Pendidikan dan Kependudukan“, Komponen 2 : ”Fasilitas Imunisasi dan Penerangan”, serta Komponen 3 :  “Fasilitas Teknologi dan Kesehatan”. Dengan demikian,  penelitian  ini  bermanfaat  bagi  para  pembuat  kebijakan  baik pemerintah  pusat maupun  daerah  untuk  memperhatikan  faktor-faktor  yang  mempengaruhi terjadinya peningkatan kemiskinan di Provinsi Papua. Kemiskinan merupakan prioritas pada SDGs yang dinyatakan pada poin pertama yaitu no poverty (tanpa kemiskinan).
Co-Authors Ain, Dzuria Hilma Qurotu Auliyah, Nina Dita Amelia Elly Ana Fajrina, Sofia Andika Nur Farizi, Muhammad Fikry Al Hilma, Dzuria Hilma Qurotu Ain Kusuma, Shalwa Oktavia M. Fariz Fadillah Mardianto Maula, Sugha Faiz Al Nur Chamidah Pratama, Bagas Shata Zahrani, Vista Vanadya