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INDONESIA
Indonesian Journal of Geography
Published by Universitas Gadjah Mada
ISSN : 00249521     EISSN : 23549114     DOI : -
Core Subject : Science,
Earth & Planetary Sciences
Indonesian Journal of Geography ISSN 2354-9114 (online), ISSN 0024-9521 (print) is an international journal of Geography published by the Faculty of Geography, Universitas Gadjah Mada in collaboration with The Indonesian Geographers Association. Our scope of publications includes physical geography, human geography, regional planning and development, cartography, remote sensing, and geographic information system. IJG publishes its issues three times a year in April, August, and December.
Arjuna Subject : -
Articles 4 Documents
 1 
Search results for , issue "article in press" : 4 Documents clear
Investigating the Impact of Social Capital on Social Resilience and Mapping Informal Settlements KAMRAN Jafarpour Ghalehteimouri
Indonesian Journal of Geography ARTICLE IN PRESS
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijg.101871

Abstract

Marginalization and informal urbanization pose significant challenges in global south countries. This study aims to show the crucial role of social capital in enhancing social resilience in informal urban areas. The research utilizes an applied-developmental approach with a descriptive-correlation methodology. Data collection involves library research and fieldwork in five districts of Urmia, with a sample size of 384 determined using Cochran's formula and distributed randomly. The questionnaire's validity was confirmed by experts, and reliability was assessed using the Cronbach's alpha test. Data analysis includes one-sample t-tests and Spearman's correlation in SPSS software, along with the Ordinal Priority Approach (OPA) and the decision-making trial and evaluation laboratory (DEMATEL) methods. The results highlight a direct relationship between social capital and social resilience, with social capital identified as the most influential criterion in Dimtel's model. The study also reveals that Urmia city exhibits above-average levels of social capital, which is associated with increased social resilience in informal urban settlements. This research contributes to understanding the interplay between social capital and social resilience, offering insights for policymakers and urban planners to enhance resilience and living conditions in these areas.Received: 2025-08-15 Revised: 2026-03-05 Accepted: 2026-07-16 Published: 2026-08-31  
Optimizing the Efficiency of Land Registration in Indonesia Through SAM-Geo: An Automated Farmland Detection and Segmentation Method Monica Maharani; Riyas Syamsul Arif; Faisal Ashaari; Nur Muhammad Ikram; Ni Putu Praja Chintya
Indonesian Journal of Geography ARTICLE IN PRESS
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijg.106592

Abstract

The application of Unmanned Aerial Vehicles (UAVs) in the Complete Systematic Land Registration (CSLR) and Participatory Systematic Land Registration (PTSL) programs significantly accelerates land mapping through aerial photogrammetry. This method substantially enhances accuracy in land certification processes, boundary identification, disaster mitigation efforts, and spatial planning efficiency. However, despite its effectiveness, manual intervention remains necessary when ground objects are obstructed or parcel boundaries are visually indistinct. To support more reliable, less subjective parcel delineation, advanced image segmentation methods are needed to streamline the derivation of parcel boundaries from UAV imagery. One technology that can be used to address this issue is Segment Anything Model Geo (SAM-Geo), which enables segmentation of farmland parcels with minimal model retraining. Nevertheless, the “segment anything” capability embedded in SAM‑Geo still requires validation through practical segmentation of surface objects, particularly land parcels in complex field conditions. This study evaluates the effectiveness of SAM-Geo, with an emphasis on segmentation accuracy and efficiency, using UAV‑derived orthophotos from five rice field areas that represent different parcel geometries and boundary qualities. We compare SAM‑Geo against a conventional standard reference method, namely, multiresolution segmentation. The findings show that SAM-Geo generally performs better than multiresolution segmentation in detecting rice field parcels and preserving parcel shapes and boundary clarity, particularly in regular, homogeneous fields with clear earthen embankments. SAM‑Geo achieves higher F1‑scores and Intersection over Union (IoU) than multiresolution segmentation across the five areas, and a paired Wilcoxon signed‑rank test on F1‑scores indicates that this accuracy advantage is statistically significant. At the same time, SAM‑Geo produces a non‑negligible number of false positives at the parcel level in more heterogeneous fields and requires longer processing times than multiresolution segmentation in our implementation. These results suggest that SAM‑Geo offers a promising accuracy‑oriented decision‑support tool for automating farmland parcel identification in favourable conditions, but its outputs still need careful review by cadastral officers, especially in complex landscapes and before being integrated into land certification workflows.Received: 2025-05-08 Revised: 2026-08-11 Accepted: 2026-09-14 Published: 2026-11-02
Spatiotemporal Analysis of Land Subsidence and Associated Land Use Dynamics in Pekalongan: An Integrated GNSS, InSAR, and Google Earth Engine Approach Bambang Darmo Yuwono; Defrizal Defrizal; William P. Sollu; Reyhan Azeriansyah; Muhamad Rifki Pratama
Indonesian Journal of Geography ARTICLE IN PRESS
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijg.112793

Abstract

Rapid land subsidence is commonly experienced along the northern coast of Java, Indonesia, and this geological natural hazard tends to severely threaten infrastructure, including environmental degradation. Despite the urgency to mitigate rapid land subsidence in cities such as Pekalongan, the comprehensively mapping remains challenging due to inherent methodological limitations. The use of Interferometric Synthetic Aperture Radar (InSAR) provides high-density deformation mapping, with its measurements inherently relative and restricted to the satellite’s one-dimensional line-of-sight (LOS). The Global Navigation Satellite Systems (GNSS) offered absolute three-dimensional coordinates but lacked continuous spatial coverage. Several existing studies failed to associate precise subsidence rates with spatiotemporal land-use and land-cover (LULC) transformations. However, this study introduced a novel, integrated geospatial framework that simultaneously resolved geodetic limitations and quantified surface impacts. The main aim focused on combining a GNSS-InSAR inversion framework for absolute spatial mapping with a Google Earth Engine (GEE)-based evaluation of the corresponding land-use dynamics. The developed model identified the main subsidence bowl, which decreased at a peak rate of 10.9 cm per year. Moreover, in coastal Pekalongan, alterations in land cover have led to significant land subsidence, profoundly affecting the area such in Wonokerto, Siwalan, and Mulyorejo.
Spatial Heterogeneity of Pandemic Risk in Urban Areas: A Comparative Analysis of Risk, Spatial Planning, and Urban Preparedness Using OLS and GWR Models Muhammad Ridha Syafii Damanik; Sirojuzilam Sirojuzilam; Heru Santosa; Samsuri Samsuri
Indonesian Journal of Geography ARTICLE IN PRESS
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijg.120307

Abstract

The COVID-19 pandemic has highlighted the limitations of conventional case-based zoning, emphasizing the need for spatially detailed risk mapping in heterogeneous urban environments such as Medan, where density, mobility, and socioeconomic conditions vary considerably across neighborhoods. This study develops a neighborhood-level pandemic risk mapping framework for Medan City using fuzzy logic and multi-criteria analysis, examining relationships among pandemic risk, spatial planning, and city preparedness across 151 urban village in 21 sub-districts. Primary data were gathered through structured interviews with 151 urban villages (kelurahan) representatives and field observations, complemented by secondary statistical data from official government agencies. Data accuracy was verified through data triangulation across primary and secondary sources and expert consistency ratio checks prior to multi-criteria evaluation. Threat, vulnerability, capacity, spatial planning, and preparedness data were standardized using fuzzy membership functions, with risk weights determined through Principal Component Analysis and planning weights derived from the Analytical Hierarchy Process. Spatial layers were integrated through Multi-Criteria Evaluation and weighted overlay in a Geographic Information System, with inter-index relationships assessed using Ordinary Least Squares and Geographically Weighted Regression. The pandemic risk index ranged from 0.015 to 0.809, with 40.44% of urban villages classified as low risk, 46.36% as moderate risk, and 13.25% as high risk. High-risk areas concentrated in the city center and eastern zones, reflecting elevated density and mobility. While the global OLS model showed weak direct effects of risk and spatial planning on preparedness, the local GWR model revealed substantially stronger spatial relationships, with risk demonstrating a significant influence on spatial planning at an R² of 0.7182. In conclusion, this study demonstrates that combining fuzzy multi-criteria risk mapping with local spatial regression provides a robust, spatially targeted framework for urban spatial planners and policy makers to strengthen disaster resilience and prioritize pandemic interventions in highly vulnerable urban neighborhoods.Received: 2026-06-08 Revised: 2026-07-08 Accepted: 2026-09-29 Published: 2026-11-03

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