Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Zero : Jurnal Sains, Matematika, dan Terapan Inferensi
Prasetyo, Rindang Bangun
Politeknik Statistika STIS
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Physics Social Sciences

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Estimation of Paddy Productivity at Subdistrict Level using Geoadditive Small Area Estimation Model in Ponorogo Regency Maulana, Arswenda Putra; Prasetyo, Rindang Bangun
Inferensi Vol 7, No 3 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i3.20505

Abstract

Paddy is the most important food crop in the world and it is the source of food needed by more than half of the population on a global scale. However, the world is experiencing the threat of a food crisis, so the Indonesian government continues to be committed to increasing national paddy production and ensuring food sufficiency in the country by implementing food self-sufficiency programs in each region. Paddy productivity data can be used as one of the government's benchmarks to assess the success of the food self-sufficiency program, but BPS-Statistics Indonesia only provides data on paddy productivity up to the district/cities level. Therefore, this study aims to estimate paddy productivity at sub-district level using the Geo-SAE method. Based on the research results, the estimation of the average paddy productivity in Ponorogo Regency in 2022 using Geo-SAE was obtained at 5.8 tons/ha and resulted in a smaller RSE value compared to the direct estimation at sub-district level. This indicates that the Geo-SAE method has better precision than the direct estimation method. Meanwhile, additional result from estimation of paddy productivity shows that in Ponorogo Regency in 2022 there is a large rice surplus. Therefore, it can be said that Ponorogo Regency is experiencing a very good food sufficiency condition.
Drivers and Impacts of Agricultural Land Conversion: Regression Modelling with Spatial Dependence in West Bandung and Purwakarta Regencies, Indonesia Wijayanto, Arie Wahyu; Prasetyo, Rindang Bangun; Putri, Salwa Rizqina; Sugiarto, Sugiarto; Marsisno, Waris; Wahyuni, Krismanti Tri; Pasaribu, Ernawati; Maghfiroh, Meilinda F N
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 1 (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.v9i1.23939

Abstract

The rapid conversion of farmland to non-agricultural uses in West Java threatens food security, farmer livelihoods, and environmental sustainability. This study investigates the causes and consequences of land conversion in West Bandung and Purwakarta Regencies using a mixed-source data, including geotagging, CAPI, and secondary data from satellite images, focusing on landowners who converted farmland between 2013 and 2021. Multiple linear regression and spatial models, including Spatial Lag Model (SLM), were applied to assess key determinants. The results revealed economic pressures as the main driver, with rice fields most affected and various geographic and infrastructure factors influencing outcomes. The findings underscore the need for targeted policies to balance development with sustainable land and food system management.
Hybrid GSTAR-Machine Learning Model for Forecasting Tourists Numbers in Yogyakarta Sohibien, Gama Putra; Azmi, Annisa Nurul; Sofa, Wahyuni Andriana; Sumarni, Cucu; Prasetyo, Rindang Bangun; Putri, Christiana Anggraeni
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 Arie Wahyu Wijayanto Azmi, Annisa Nurul Maghfiroh, Meilinda F N Marsisno, Waris Maulana, Arswenda Putra Pasaribu, Ernawati Putri, Christiana Anggraeni Putri, Salwa Rizqina Sofa, Wahyuni Andriana Sohibien, Gama Putra Sugiarto, Sugiarto Sumarni, Cucu Wahyuni, Krismanti Tri