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INDONESIA
Jurnal Aplikasi Statistika & Komputasi Statistik
Published by Politeknik Statistika STIS
ISSN : 20864132     EISSN : 26151367     DOI : -
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics
Redaksi menerima karya ilmiah atau artikel penelitian mengenai kajian teori statistika dan komputasi statistik pada bidang ekonomi dan sosial dan kependudukan, serta teknologi informasi. Redaksi berhak menyunting tulisan tanpa mengubah makna subtansi tulisan. Isi jurnal Aplikasi Statistika dan Komputasi Statistik dapat dikutip dengan menyebutkan sumbernya.
Arjuna Subject : -
Articles 143 Documents
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The Implementation of Geospatial Analysis on Hotel Occupancy Rate Nazuli, Muhammad Fachry; Panuntun, Satria Bagus; Maulana, Addin; Takdir; Pramana, Setia
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 1 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v17i1.797

Abstract

Introduction/Main Objectives: One of the main attributes of hotel selection and customer satisfaction is its location. Background Problems: Strategic location leads to higher demand for accommodation. Accommodation demand is reflected in hotel occupancy levels, which indicate the percentage of reserved rooms at a specific period. Novelty: This study aims to investigate the effect of spatial location on hotel occupancy rates by analyzing data collected in online hotel reservation applications. A study related to the effects of location and hotel occupancy has never been conducted in Indonesia. Research Methods: We use data from hotels located in the province of Yogyakarta, which contains 245 hotels spread over three regencies/cities, namely Yogyakarta City, Sleman Regency, and Bantul Regency. We conducted a spatial regression analysis, namely the Spatial Error Model (SEM), with a spatial weight matrix using a radius of 3.2 km. Finding/Results:  We found that spatial locations affect the occupancy rates of hotels based on the online hotel reservation application that we observed. These spatial locations include the distance from the hotel to the airport, the distance from the hotel to the bus stop, and the number of nearby restaurants, offices, and hotels.
The Application of Partial Proportional Odds Model on Determinants Analysis of Household Food Insecurity Level in Papua, Indonesia Abigael, Rolyn; Sumarni, Cucu; Sastri, Ray
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 1 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v17i1.798

Abstract

Introduction/Main Objectives: Food insecurity in Papua, Indonesia, is still high. However, the study on that issue is limited. This research aims to analyze the determinants of food insecurity in Papua. Background Problems: An ordinal logistic regression can be used. However, this model generally requires the parallel lines assumption. However, somehow, the assumption is often violated. Novelty: This study used a model that relaxes the assumption of parallel lines. This model can capture the condition that some parameters are assumed to meet parallel lines and some do not. Research Methods: In this case, the partial proportional odds model was applied to find the determinant of household food insecurity status by using the National Socioeconomic Survey (SUSENAS) data. Finding/Results: The results show that a female head of household, age 60 years and above, junior high school education and below, has a higher tendency to be at least mildly food insecure, and the effect is the same for each level of food insecurity. Household heads who do not work, work in agriculture, and have household drinking water sources that are not feasible can aggravate the food insecurity level. Meanwhile, food assistance provided by the government influences reducing food insecurity levels.
Estimation of Gross Regional Domestic Product per Capita at the Sub-District Level in Bali, NTB, and NTT Provinces Using Machine Learning Approaches and Geospatial Data Putra, I Made Satria Ambara; Prasetyo, Rindang Bangun; Wiguna, Candra Adi
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 1 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v17i1.803

Abstract

Introduction/Main Objectives: This study aims to estimate Gross Regional Domestic Product (GRDP) per capita at the sub-district level. Background Problems: Currently, GRDP per capita is calculated only at the district level by BPS. Novelty: This study estimates GRDP per capita at the sub-district level using a model developed at the district level, applying machine learning and linear regression methods. Research Methods: The model was constructed using geospatial data sourced from satellite imagery, OpenStreetMap, (Village Potential Statistics) PODES, directories of large mining companies, and directories of the manufacturing industry at the district level. Linear regression and machine learning methods, including neural networks, random forest regression, and support vector regression, were used to develop the model. The research focuses on three provinces: Bali, West Nusa Tenggara (NTB), and East Nusa Tenggara (NTT). Findings/Results: The best-performing model was support vector regression, with MAE and MAPE evaluations of 10.33 million and 26.11%, respectively. The results indicate that sub-districts with high GRDP per capita are typically urban areas that serve as economic hubs. The Williamson Index results show that districts in the eastern region have higher inequality levels compared to those in the western region.

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