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Forum Geografi
Published by Universitas Muhammadiyah Surakarta
ISSN : 08520682     EISSN : 24603945     DOI : -
Core Subject : Science,
Earth & Planetary Sciences
Aim Forum Geografi is an open-access, peer-reviewed journal dedicated to advancing the field of geography by publishing original scientific articles on spatial issues. The journal prioritizes studies that employ rigorous spatial analysis and geospatial technologies—such as GIS and remote sensing—to deepen theoretical and empirical understanding of spatial patterns, processes, and interactions in both physical and human geography. Focus The primary focus of Forum Geografi is on studies that apply Geographic Information Systems (GIS) and/or remote sensing for spatial and regional analysis of physical and human geographic phenomena. Scope - Human and environment interaction - Geomorphology: process and modelling - River basin: process and modelling - GIS and/or Remote sensing-based disaster-related study - Urban, rural, and regional study
Arjuna Subject : Ilmu Bumi dan Planet (semua kategori) - Geologi
Articles 97 Documents
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Effectiveness of Machine and Deep Learning Algorithms in Remote Sensing for Food Crop Mapping: A Systematic Literature Review Riki Ridwana; Muhammad Kamal; Sanjiwana Arjasakusuma
Forum Geografi Vol. 40 No. 2 (2026): August 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.12235

Abstract

The problems of food (in)security, considering rapid population growth, climate change, land degra-dation, and escalating competition for natural resources, are highlighted in the global conversation on sustainable development. Ecologically sound agricultural management and well-informed policy-making depend on accurate and trustworthy maps of food crops. For mapping food crops, remote sensing has become essential, and machine and deep learning algorithms are becoming more and more important. A systematic literature review explores the extent to which these algorithms have been applied in food crop mapping. A comprehensive search across five electronic databases yielded 406 relevant studies, of which 50 articles were chosen after applying inclusion and exclusion criteria. According to the analysis, the best algorithms for mapping food crops are U-Net, value-guided expla-nation model (SGEM), and one-dimensional convolutional neural network (Conv1D). These find-ings provide an organized framework for future research on food crop management and monitoring. Food security and sustainable farming methods depend on accurate and reliable food crop maps, which can be improved by applying state of the art deep learning methods, the study found. By using these algorithms, stakeholders and policymakers can develop data-driven strategies to optimize land use, minimize environmental risks, and enhance global food sustainability.
Optimized Artificial Intelligence-Based Algorithm for Groundwater Potential Mapping in Trenggalek Regency, Indonesia Muhammad Hidayatul Ummah; Diyono Diyono
Forum Geografi Vol. 39 No. 3 (2025): December 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v39i3.12279

Abstract

Groundwater management is essential as more than one-third of the world's population relies on groundwater as a source of freshwater. Exploration of groundwater potential serves as a practical im-plementation to ensure accessibility to freshwater. Therefore, this study developed a machine learning (ML), deep learning (DL), and stacking learning (SL) based model for groundwater potential mapping in Trenggalek Regency, Indonesia. A total of 740 spring locations were used as training data, and 18 variables were considered in the modelling. The eighteen parameters were classified into geological, topographic, land cover, climatological, hydrological, and geophysical factors. We used several algo-rithms, including gradient boosting decision trees (GBDT), random forest (RF), recurrent neural net-work (RNN), convolutional neural network (CNN), SL GBDT-RF, SL CNN-RNN, and SL GBDT-RF-CNN-RNN. This study optimized each basic learning task through hyperparameter fine-tuning using a tree-Parzen structured estimator (TPE) method. Models were evaluated using four metrics: accuracy (Acc), Cohen's kappa (CK), Matthews correlation coefficient (MCC), and receiver operating character-istic (ROC) area under the curve (AUC). Of the seven models generated, SL GBDT-RF achieved the best performance on the test data, with Acc, MCC, CK, and AUC values of 0.957, 0.915, 0.915, and 0.990, respectively. The geological unit parameter has the highest relative contribution rate in all pre-diction models. Based on the best model, the study area is dominated by the low-potential class, accounting for 31.29%. This study contributes to providing a benchmark for the development of groundwater potential prediction using ML, DL, and SL algorithms across various case studies. In addition, this study can be used by concerned stakeholders in sustainable water resource planning, drought disaster management, and the prevention of inappropriate groundwater exploration.
From Farmland to Housing: Assessing the Consequences of Agricultural Land Conversion for Farmers’ Welfare and Food Security Lalu Teguh Suganda; Jawoto Sih Setyono
Forum Geografi Vol. 40 No. 2 (2026): August 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.12300

Abstract

Rapid conversion of agricultural land into housing challenges rural livelihoods and food security in many developing countries; however, its implications for agrarian injustice remain underexplored. This study investigates how unjust agricultural land conversion affects farmers’ welfare and household food insecurity in Kediri Subdistrict, West Lombok, Indonesia. Unjust agricultural land conversion relates to unequal land governance, weak spatial planning enforcement, and socioeconomic effects on smallholders. A convergent mixed-method approach is used to integrate multitemporal spatial analysis using Geographic Information Systems, household survey data from 100 farmers, qualitative interviews, and logistic regression modeling based on the Food Insecurity Experience Scale. The results reveal a substantial transformation of rural landscapes, with approximately 31% of productive agricultural land converted into housing between 2012 and 2024. This process undermines farmers’ livelihoods and intensifies food insecurity across availability, accessibility, and utilization dimensions. Logistic regression identified housing-driven land conversion as the strongest predictor of worsening food insecurity. Older and less-educated farmers exhibited significantly higher vulnerability. The findings demonstrate that farmland conversion is a physical land-use change and structural driver of food insecurity through land dispossession, livelihood erosion, and reduced household resilience. This study clarifies that unjust conversion reflects rapid land transformation driven by housing development, weak spatial planning control, and unequal effects on smallholders who experience livelihood loss without adequate protection or compensation. These findings highlight the need to strengthen spatial planning enforcement, protect sustainable agricultural land, and promote more inclusive land governance to safeguard rural livelihoods and food security.
Indonesia Climate Change: Observations and Future Projections in IPCC AR6 WG I and Beyond Tosiyuki Nakaegawa; Takuro Kobashi
Forum Geografi Vol. 39 No. 3 (2025): December 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v39i3.12760

Abstract

This study aimed to comprehensively review and synthesize results specific to the Indonesian archipelago from the IPCC Sixth Assessment Report (AR6) Working Group I, "The Physical Science Basis." At the heart of the Indo-Pacific maritime continent, Indonesia's climate was undergoing significant anthropogenic alteration owing to complex atmospheric and oceanic processes. The review distilled pertinent observations, trends, and projections, while also offering a detailed analysis of the underlying physical science. The observed changes included a significant rise in tropospheric ozone since the 1990s, a persistent trend towards a La Niña-like state in the Pacific Walker Circulation, accelerated warming of surrounding tropical oceans, and increased multi-decadal variability in the Indonesian Throughflow. The regional water cycle was also intensifying, marked by an increase in rainfall extremes which occurred against a background of powerful climate variability modes such as El Niño-Southern Oscillation and Indian Ocean Dipole, contributing to severe events such as the 2015-2016 drought and fire crisis. Future projections assessed with high confidence pointed towards a decrease in annual mean precipitation with particularly severe drying of up to 30% projected for the summer months over key islands. These multifaceted changes have profound implications for Indonesia's environmental stability, socioeconomic development, and population well-being, underscoring the critical urgency for science-informed, targeted climate adaptation and mitigation strategies.
Integration of Topography Map and Land Use Change Modeling for Sustainable Tourism Development in Merapi Volcano, Indonesia Syamsul Bachri; Sandy Budi Wibowo; Sunardi Sunardi; Franck Lavigne; Sumarmi Sumarmi; Mellinia Regina Heni Prastiwi; A Riyan Rahman Hakiki; Tabita May Hidiyah; Nanda Regita Cahyaning Putri
Forum Geografi Vol. 40 No. 2 (2026): August 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.12924

Abstract

Indonesia, as the country with the highest number of active volcanoes worldwide, faces significant challenges from volcanic hazards. Mount Merapi, one of the most active volcanoes, is surrounded by intensive tourism and residential development, which increase the region’s vulnerability. This study integrates DEMNAS-based topographic analysis and the Land Change Modeler (LCM) with the Multi-Layer Perceptron (MLP)–Markov Chain algorithm to examine land-use dynamics and risks to tourism in the Opak Oyo Watershed. Multi-temporal Landsat imagery (2004, 2014, 2024) was classified using the CART algorithm, achieving an overall accuracy of 94.5% and a Kappa coefficient of 0.928. The results show that between 2014 and 2024, the area of built-up land increased by 47.12 km², while that of forests declined by 127.76 km², indicating strong anthropogenic pressure. The validated LCM model projected that by 2034 built-up land will expand to 228.13 km², increasing by 46.04 km² (3.53%) compared to 2024, while agricultural land is predicted to decrease by 100.14 km² (–7.67%). Forest areas are projected to increase by 90.75 km² (6.95%), reflecting ecological rehabilitation scenarios. Tourism risk analysis shows that a significant number of tourism sites are located within KRB III (a high-risk zone), where projected building expansion overlaps with areas exposed to pyroclastic flows and lahar hazards. The findings highlight that integrating topographic constraints with predictive land-use modeling provides a robust spatial framework for sustainable tourism development in volcanic regions. The approach supports risk-informed zoning, environmentally sensitive land allocation, and long-term spatial planning strategies in Mount Merapi and other hazard-prone landscapes.
Local Climate Zones in Tropical Urban Environments: A Review Jamal Harimudin; Lam Kuok Choy; Frankie Marcus Ata
Forum Geografi Vol. 40 No. 2 (2026): August 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.13119

Abstract

Local climate zones (LCZs) were originally proposed to redefine the rural–urban dichotomy, while providing a standardised framework for urban heat island (UHI) studies worldwide. The UHI phenomenon, characterised by rising land surface temperature (LST), particularly in urban areas, has become a global urban environmental issue. At the same time, experts project that cities in tropical macroclimate regions will experience UHI effects more rapidly. Therefore, this literature review examines the development of the LCZ framework and identifies future research directions to address global climate change, particularly within tropical urban environments. The study employs the PRISMA framework within the Scopus database, identifying 51 documents suitable for further analysis. Descriptive analysis indicates that LCZ studies are distributed across 60 cities, with Nagpur (India) being the most prominent. Meanwhile, the publication frequency fluctuates, with 2024 recording the highest number. A bibliometric analysis of authors revealed that at least 10 of the most productive researchers originated from different countries, institutions and disciplines. In addition, a bibliometric analysis of keywords identified 18 terms with the highest co‑occurrence rates, which constitute the themes of current LCZ research. Remote sensing (RS)-based methods, employing supervised pixel-based analysis with a random forest classifier developed within the WUDAPT project, dominate LCZ mapping approaches, alongside expert-based and geographic information system (GIS)-based methods. Based on a thematic analysis, the applications of LCZ across various fields include the UHI effect and its mitigation, surface temperature monitoring, heat risk, heat stress and heat hazards, thermal comfort, energy consumption and balance, urban climate modelling, urban planning, and other applications such as urban health and air quality. In the future, LCZ in tropical macroclimate regions is likely to become a highly complex field of study, requiring the involvement of multidisciplinary approaches.
Uncovering the Spatial Logic of Tourism Attractions: A Geospatial Analysis of Distribution Patterns and Driving Forces in Luxor, Egypt Moaaz Kabil; Mona Abdel Fattah Abdel Moneim; Heba Mostafa Hassan; Lóránt Dénes Dávid
Forum Geografi Vol. 40 No. 1 (2026): April 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.13279

Abstract

The spatial distribution of tourism attractions plays an important role in shaping visitor travel behaviour, accessibility to tourism locations, and tourist destination management and planning. This study examines the spatial patterns of tourism attractions in Luxor Governorate, Egypt, and the factors influencing these spatial relationships using a variety of geospatial analysis techniques. These techniques include Nearest Neighbour Index (NNI), Standard Deviational Ellipse (SDE), Kernel Density Estimation (KDE), and Local Moran’s I. In addition, a combination of the Analytic Hierarchy Process (AHP) and Geodetector were applied to determine which of sixteen identified factors influenced the distribution of tourist attractions in Luxor. Finally, the spatial relationships between the identified factors and the distribution of tourist attractions were analysed through the use of Multiscale Geographically Weighted Regression (MGWR). The results show that there is a strong clustering of tourism attractions in Luxor within three main hubs: Luxor City (East Bank), Qurna (West Bank), and Esna. The results further indicate that the most influential factors influencing the distribution of tourist attractions in Luxor include regional services centrality, GDP index, proximity to urban centres, tourism workforce localisation, urbanisation level, and environmental quality, respectfully. The implications of this research provide practical applications for developing more sustainable and balanced tourism development strategies in heritage-rich regions such as Luxor.
GIS-Based Ecological Vulnerability Assessment in a Mountainous Region: the Charvak Reservoir, Uzbekistan Khasan Magdiev; Dilbarkhon Fazilova; Mirshodjon Makhmudov; Aziz Kazakov
Forum Geografi Vol. 40 No. 1 (2026): April 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.13377

Abstract

Mountain reservoirs in steep terrain face geomorphic, hydrological, and development pressures, yet vulnerability maps can be hard to interpret in terms of drivers. An ecological vulnerability index (EVI) is developed for the Charvak Reservoir basin (Uzbekistan) from three subindices: topographic–geological risk index (TGRI; slope, lithological resistance, soil organic carbon, and distance to active faults), hydrological–ecological risk index (HERI; shoreline proximity and normalized difference water index (NDWI) trend risk, excluding the trend term in a nearshore band to reduce shoreline artifacts), and climatic–anthropogenic risk index (CARI; trends in the normalized difference vegetation index [NDVI], normalized difference built-up index [NDBI], and land surface temperature [LST], plus proximity to infrastructure). May–August Landsat Collection 2 composites (2000–2023) yield trends; indicators are scaled to 0–1 and aggregated using analytic hierarchy process (AHP) weights. Hotspots cluster on reservoir-facing slopes and access valleys; the subindices separate topographic controls from shoreline and corridor pressures. Changes in 2018–2023 show limited class transitions, whereas a 2030 scenario (extrapolated NDVI/NDBI plus shoreline and infrastructure stressors) increases high–very high vulnerability by ~39 km2 (~3.2 percentage points), mainly in shoreline belts and development corridors. The framework supports zoning and planning in reservoir basins.
Unpacking Gentrification Attributes in Diverse Geographies: A Systematic Literature Review from Indonesia Edi Setiawan; Monsicha Bejrananda; Delik Hudalah; Rita Noviani
Forum Geografi Vol. 40 No. 1 (2026): April 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.13414

Abstract

Gentrification is identified as a transformative process, which involves major social and spatial transformations that take place either rapidly or gradually in various geographical contexts. Whereas many studies have elaborated on gentrification on a global basis, only a few have systematically analyzed its attributes with regard to specific geographic contexts. Consequently, this study aims to analyze the attributes of gentrification within urban, rural and transitional areas in Indonesia through a systematic literature review (SLR). A total of 93 articles were selected from Scopus and Google Scholar based on predefined inclusion and exclusion criteria. The attributes of gentrification were categorized into driving forces, key actors, processes, scales and consequences. The results show that gentrification is not a homogeneous process, as its characteristics vary across spatial contexts and are further influenced by local conditions, actor configurations, and governance mechanisms. It was also connected to other spatial transformation processes, such as studentification, touristification, and central city revitalization through urban renewal and rejuvenation. These insights emphasize the need for context sensitivity to understand the broad process of gentrification. Furthermore, adopting a location-based method in spatial planning and development policy is crucial for reducing vulnerability and avoiding the homogenization of gentrification in policy frameworks.
Opportunities for Optimising the CORS Network in Uzbekistan: Proposals Based on Bibliometric Analysis Dilshoda Norboeva; Rukhiddin Turaev; Aziz Inamov; Khojiakbar Khasanov; Jasur Lapasov; Sanjarbek Safaev
Forum Geografi Vol. 40 No. 2 (2026): August 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.13569

Abstract

This study examines the optimisation of Continuously Operating Reference Station (CORS) networks through a combined bibliometric and spatial analysis approach, with particular reference to Uzbekistan as an illustrative case. A total of 522 publications were reviewed, of which 53 were identified as most relevant for detailed thematic analysis, as they provided insights into global research trends, methodological approaches, and applications in geodetic network design. To demonstrate how these approaches can be applied in practice, a simplified GIS-based spatial analysis was conducted in ArcGIS Pro to evaluate the distribution of existing CORS stations in Uzbekistan. A 40 km geodetic buffer was used as a representative distance derived from commonly reported baseline ranges in the literature (30-70 km). It should be emphasised that such a buffer-based approach only reflects spatial coverage and does not account for positioning accuracy, network geometry, or GNSS error sources. The results indicate uneven spatial coverage, with relatively dense station distribution in central and eastern regions, while western areas remain less represented. This finding is presented as a preliminary spatial observation rather than a validated assessment of network performance. Overall, the study does not aim to define optimal station spacing, or to provide a full technical evaluation, but rather to synthesise existing knowledge and demonstrate how simplified spatial methods can support initial planning. Future work should focus on integrating these insights with empirical GNSS data and advanced optimisation techniques for more robust network design.

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