cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota adm. jakarta timur,
Dki jakarta
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 152 Documents
 12   13   14   15   16 
Estimating Economic Activity Using Geospatial Big Data in East Java, Indonesia: Relative Spatial GDP Index Approach Rifqi Ramadhan; I Made Satria Ambara; Taufiq Agung Kurniawan; Fitri Kartiasih; Raden Muaz Munim; Somethea Buoy
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 2 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v17i2.856

Abstract

Introduction/Main Objectives: GRDP serves as a fundamental indicator for assessing regional economic performance in Indonesia and plays a critical role in development planning. Background Problems: Conventional GRDP measurement in Indonesia relies on survey-based approaches, which are time-consuming, costly, and provide limited spatial detail. Novelty: This study introduces a Relative Spatial GDP Index (RSGI) constructed from geospatial big data such as remote sensing and point of interest (POI) to estimate GRDP more granular in East Java. This approach represents the first geospatial data driven GRDP index developed at such fine spatial resolution in Indonesia. Research Methods: Four weighting schemes were applied to generate RSGI variations, which were then evaluated through regression modeling against official GRDP. They are equal weight, pearson correlation, spearman correlation, and principal component analysis (PCA). Finding/Results: The RSGI PCA produced the best performance (RMSE = 0.73047; MAE = 0.48185; MAPE = 7.00%; R² = 0.7618). PCA weight outperformed other weight by capturing shared variance and generating objective weights that better represent spatial economic intensity. The RSGI PCA demonstrates a strong and significant correlation with GRDP at the sub-district level and provides a robust tool for fine-scale economic estimation.
Integrating Multi-Criteria Decision Analysis and Machine Learning for Fine-Scale Mapping of Safe Drinking Water Access in Bengkulu Province, Indonesia Tampubolon, Andrew Maruli Tua; Josaphat, Bony Parulian; Asriadi Sakka; Yohanes Wahyu Trio Pramono
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 2 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v17i2.866

Abstract

Introduction/Main Objectives: This study aims to develop a 1 km × 1 km level estimation model of safe drinking water access using multisource satellite imagery, point of interest (POI), and aquifer productivity maps. Background Problems: There is a lack of alternative data sources for estimating safe drinking water access that are cost-, time-, and labor-efficient while maintaining high accuracy and frequent updates. Novelty: This study integrates Multi-Criteria Decision Analysis (MCDA) and machine learning methods to estimate and map safe drinking water access at a 1 km × 1 km resolution. Research Methods: Multisource geospatial data were used to construct the model. Within the MCDA approach, the Weighted Product Model (WPM) was employed to develop the Safe Drinking Water Access Index (SDWAI). Meanwhile, the machine learning regression algorithms Adaptive Boosting Regression (ABR) and Gradient Boosting Regression (GBR) were applied to estimate safe drinking water access at a fine spatial scale. The study was conducted in Bengkulu Province, Indonesia. Finding/Results: WPM yielded the best MCDA performance (  = 0.3699, RMSE = 10.6566, MAE = 9.5427, MAPE = 0.1405), while ABR showed the best machine learning performance (  = 0.4361, RMSE = 10.0813, MAE = 8.3750, MAPE = 0.1333).

Page 16 of 16 | Total Record : 152

 12   13   14   15   16 

Filter by Year

2015 2025


Reset
Filter By Issues
All Issue Vol 17 No 2 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 1 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 2 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik Vol 16 No 1 (2024): Jurnal Aplikasi Statistika & Komputasi Statistik Vol 15 No 2 (2023): Journal of Statistical Application and Computational Statistics Vol 15 No 1 (2023): Journal of Statistical Application and Computational Statistics Vol 14 No 2 (2022): Journal of Statistical Application and Computational Statistics Vol 14 No 1 (2022): Jurnal Aplikasi Statistika dan Komputasi Statistik Vol 13 No 2 (2021): Jurnal Aplikasi Statistika dan Komputasi Statistik Vol 13 No 1 (2021): Jurnal Aplikasi Statistika dan Komputasi Statistik Vol 12 No 3 (2020): Jurnal Aplikasi Statistika dan Komputasi Statistik Edisi Khusus Vol 12 No 2 (2020): Journal of Statistical Application and Computational Statistics Vol 12 No 1 (2020): Journal of Statistical Application and Computational Statistics Vol 11 No 2 (2019): Journal of Statistical Application and Computational Statistics Vol 11 No 1 (2019): Journal of Statistical Application and Computational Statistics Vol 10 No 2 (2018): Journal of Statistical Application and Computational Statistics Vol 10 No 1 (2018): Journal of Statistical Application and Computational Statistics Vol 9 No 2 (2017): Journal of Statistical Application and Computational Statistics Vol 9 No 1 (2017): Journal of Statistical Application and Computational Statistics Vol 8 No 2 (2016): Journal of Statistical Application and Computational Statistics Vol 8 No 1 (2016): Journal of Statistical Application & Statistical Computing Vol 7 No 2 (2015): Journal of Statistical Aplication and Statistical Computing Vol 7 No 1 (2015): Journal of Statistical Application and Computational Statistics More Issue