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All Journal Zero : Jurnal Sains, Matematika, dan Terapan PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Rindang Bangun Prasetyo
Politeknik Statistika STIS
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Mathematics Physics

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Small Area Estimation of Multidimensional Poverty in East Java Province Using Satellite Imagery Helen Cantika Laura Aisyatul Ridho; Rindang Bangun Prasetyo
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.417

Abstract

The government has so far focused on a monetary approach to overcoming poverty, while poverty is multidimensional. Holistic and accurate poverty indicators are needed as material for policy formulation, such as the Multidimensional Poverty Index (IKM), which is calculated from raw data from the National Socioeconomic Survey (SUSENAS). However, the direct estimation of the multidimensional poverty headcount (AKM) is only accurate at the provincial level, as seen from the relative standard error (RSE) of several districts and cities, which is still above 25 percent. Increasing the sample size requires time, effort, and cost, so the Small Area Estimation (SAE) method can be an alternative. Apart from using official statistics for accompanying variables, satellite imagery has the advantage of being up-to-date and available up to a granular level. This study aims to estimate the AKM at the district/city level in East Java Province by utilizing satellite imagery and official statistics in SAE. The results showed that SAE HB Beta-logistics, with the accompanying variables combined with satellite imagery and official statistics, has a higher accuracy than direct estimation.
Hybrid GSTAR-Machine Learning Model for Forecasting Tourists Numbers in Yogyakarta Gama Putra Sohibien; Annisa Nurul Azmi; Wahyuni Andriana Sofa; Cucu Sumarni; Rindang Bangun Prasetyo; Christiana Anggraeni Putri
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 2 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i2.26381

Abstract

Tourism management in DI Yogyakarta is vital to ensure tourism benefits local communities. A key challenge lies in the uncertainty and spatial interdependence of tourist visits among neighboring regions. While the GSTAR model captures spatial relationships, its accuracy decreases with outliers, non-linearity, and assumption violations. To overcome these issues, this study integrates GSTAR with machine learning. Using 168 observations of tourist visits across DI Yogyakarta's regencies/cities (January 2010-December 2023), GSTAR-GLS-XGBoost model achieved 22-34% lower RMSE than other models. Tourist numbers fluctuate greatly, with peaks in May, June, July, and December. Practically, these findings can help local governments and stakeholders optimize resource allocation, plan promotions, and prepare facilities during peak seasons for sustainable tourism management in DI Yogyakarta.
Co-Authors Annisa Nurul Azmi Christiana Anggraeni Putri Cucu Sumarni Gama Putra Sohibien Helen Cantika Laura Aisyatul Ridho Wahyuni Andriana Sofa