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All Journal Al-Jabar : Jurnal Pendidikan Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Prosiding Seminar Nasional Official Statistics
Siallagan, Maria A. Hasiholan
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Utilize imagery and crowdsourced data on spatial employment modelling Pusponegoro, Novi Hidayat; Rachmawati, Ro'fah Nur; Siallagan, Maria A. Hasiholan; Wicaksono, Ditto Satrio
Al-Jabar: Jurnal Pendidikan Matematika Vol 15 No 2 (2024): Al-Jabar: Jurnal Pendidikan Matematika
Publisher : Universitas Islam Raden Intan Lampung, INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ajpm.v15i2.24518

Abstract

Background: Spatial employment modeling investigates employment distribution, patterns, influencing factors, neighboring area impact, and regional policy efficacy. Conventional studies often rely on traditional data sources, which may overlook critical employment-related phenomena. In 2022, Java recorded the lowest labor absorption rate in Indonesia, necessitating a new approach.Aim: This study combines imagery, crowdsourced data, and official statistics to identify factors influencing labor absorption in Java Island.Method: Geographically Weighted Regression (GWR) was employed to account for spatial effects in the data.Results: The model reveals that nighttime light intensity in urban and agricultural areas, along with environmental quality, significantly enhances labor absorption across Java. Internet facilities, universities, and the number of micro and small industries also positively influence most districts/cities.Conclusion: Incorporating new data sources offers valuable insights for understanding employment patterns and can enrich employment research frameworks.
SPATIAL REGRESSION APPROACH TO MODELLING POVERTY IN JAVA ISLAND 2022 Siallagan, Maria A. Hasiholan; Pusponegoro, Novi Hidayat
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1765-1778

Abstract

Geographically Weighted Regression (GWR) model is a powerful tool for analyzing spatial patterns in data. However, the standard form of a spatial model that uses a single bandwidth calibration may be unrealistic because the response-predictor relationship may be either linear or nonlinear. To address this issue, the Multiscale GWR (MSGWR) model offers improved model performance by employing Generalized Additive Model (GAM) with varying bandwidth or smoothing function for each covariate in the model. This research aims to analyze the Percentage of Poor Population (PPP) on Java Island in 2022 using the geospatial models and related socioeconomic and demographic attributes, such as Open Unemployment Rate, Human Development Index, Labor Force Participation Rate, and GRDP Per capita to identify the best model in explaining the spatial pattern and to find out the determinant of PPP on Java Island in 2022. This study uses secondary data from Statistics Indonesia. The findings reveal that the MSGWR model provides the highest R2 and smallest AICc value compared to single bandwidth models, specifically the GWR and MXGWR models. Furthermore, the MSGWR model indicates that HDI has a significant negative effect on PPP, whereas LFPR has a significant positive effect on PPP across all districts in Java Island in 2022.
Model Prediksi Harga Rumah Berbasis Analisis Statistik dan Geospasial Andryana, Jason Justin; Samudera, Titania Puteri; Siallagan, Maria A. Hasiholan; Hongo, Richita; Karnadi, William Philip; Flukeria, Masarina
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2554

Abstract

House prices play a crucial role in shaping the economic and social dynamics of society, influenced by environmental conditions, accessibility, and socio-economic factors. This study develops an adaptive model for predicting house listing prices by integrating statistical and geospatial analysis techniques. Data were collected through web scraping using the Rumah123.com API and cover property prices across subdistrict and district levels in Bogor. Satellite imagery and environmental indicators—such as the Normalized Difference Built-Up Index (NDBI) and Normalized Difference Water Index (NDWI)—were analyzed to uncover market trends. The findings reveal that urban density has negative spillover effects on surrounding areas, while green spaces and high-moisture zones positively influence property values. Digital infrastructure and air quality also affect housing preferences. The Spatial Durbin Error Model (SDEM) accounts for complex spatial dependencies, resulting in more realistic price estimates. These insights contribute to improved decision-making for housing investments by various stakeholders.
Co-Authors Andryana, Jason Justin Flukeria, Masarina Hongo, Richita Karnadi, William Philip Novi Hidayat Pusponegoro Rachmawati, Ro'fah Nur Samudera, Titania Puteri Wicaksono, Ditto Satrio