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All Journal Indonesian Journal of Geography Geoplanning : Journal of Geomatics and Planning Forum Geografi Edukasi: Jurnal Pendidikan Sosial Horizon: Jurnal Pendidikan Sosial Jurnal Penelitian Pendidikan IPA (JPPIPA) Geodika: Jurnal Kajian Ilmu dan Pendidikan Geografi Sosial Khatulistiwa: Jurnal Pendidikan IPS GeoEco Geosfera Indonesia
Ajun Purwanto
PGRI Pontianak University
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Earth & Planetary Sciences Education Environmental Science Materials Science & Nanotechnology Physics Social Sciences

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Use of HAND Model for Estimating Flood-Prone in Serawai Basins Base on Remote Sensing and Sistem Information Geography Purwanto, Ajun; Andrasmoro, Dony; Eviliyanto, Eviliyanto
Indonesian Journal of Geography Vol 56, No 3 (2024): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijg.89225

Abstract

A river basin's flood-prone mapping is essential for managing flood risks, developing mitigation plans, and developing flood forecasting and warning systems, among other things. This research uses the HAND model to estimate the level of flood-prone and its distribution in watersheds. The method used is survey and image interpretation. The data used is DEM imagery with a resolution of 10 meters. Data analysis uses spatial analysis, which includes elevation, hydrological analysis, fill, flow accumulation, flow direction, flow distance, and minus statistical analysis. The results showed that the Serawai watershed has five classes: very prone, prone, moderate, not prone, and very not prone. The very prone class has an area of 112,213.82 ha (65.41%), including Tontang, Sedaha, Nanga Serawai, Begori, Nanga Lekawai, Surga, Buntut Ponte, and Nanga Segulang village. The prone class has an area of 29,356.65 ha (17.14%), spread across the village of part of Beurgea, part of Nanga Segulang, Nanga Jelundung, and part of Tontang village. The moderate level has an area of 18,971.52 ha (11.08%), spread across Tontang, part of Nanga Jelundung, and part of Baras Nabun village. The area with a not-prone is 7,996.20 ha (4.67%), spread across Baras Nabun and parts of  Nanga Jelundung village. For areas that are very not prone, they have an area of 3,004.20 (1.75%), spread over parts of the villages of Sedaha, parts of Baras Nabun, and Nanga Jelundung. Based on the research results, it can be concluded that the HAND Model is an effective and easy-to-use model for estimating flood-prone areas.
The Use of Sentinel-2A Images to Estimate Potential Flood Risk With A Multi-Index Approach in The Mempawah Watershed Ajun Purwanto; Paiman Paiman; Agus Sudiro
Geosfera Indonesia Vol. 8 No. 1 (2023): GEOSFERA INDONESIA
Publisher : Department of Geography Education, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/geosi.v8i1.37156

Abstract

Natural disasters in Indonesia have become an annual cycle, an example is flooding. This study aims to determine the flood risk potential in the Mempawah watershed and the places likely to be flooded. The method used was a survey and interpretation of secondary data from topographic maps, Sentinel-2A images, and Digital Elevation Model images. Furthermore, the secondary data analysis used includes the Standardized Precipitation Index (SPI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Soil Adjusted Vegetation Index (SAVI), and Inverse Distance Weighted (IDW). The result showed that the Mempawah watershed has high, medium, and low flood risk potential. Areas with high flood potential have an area of 1,511,967 ha, those with medium potential were 2,606,778 ha, and the places with low potential were 12,644,034 ha. The changes in class user's accuracy results reached 90.909%, while those with no change were 83.333%. It was also discovered that when the satellite analysis was > 70%, it was regarded as good. This means that the accuracy of the interpretation results and flood change detection approach was also good.
Comparative Analysis of HAND with TWI Flood-Prone Mapping Models in Data-Scarce Areas Purwanto, Ajun; Andrasmoro, Dony; Eviliyanto, Eviliyanto
Indonesian Journal of Geography Vol 57, No 2 (2025): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijg.99160

Abstract

Flood is one of the most frequent natural disasters in Indonesia and worldwide. Therefore, this study aimed to compare and evaluate flood-prone mapping model using Height Above Nearest Drainage (HAND) and Topographic Wetness Index (TWI) model in data-scarce areas. HAND and TWI models were used to estimate flood-prone level, with field survey and image interpretation as primary methodologies. The data used was Digital Elevation Model (DEM) imagery with a resolution of 10 meters, incorporating elevation, slope, and hydrological parameters namely flow accumulation, direction, and distance. The mapping flood-prone areas were categorized as very prone, prone, moderate, not prone, and very not prone. The results showed that there were differences between HAND and TWI models in terms of area and percentage. The differences in flood inundation characteristics produced by HAND model were mainly due to variations in elevation and proximity to drainage channels. In contrast, TWI model focused on topography, soil moisture, and runoff potential. The differences between the two models also emphasized the importance of terrain characteristics in model predictions. The comparable predictive ability of HAND and TWI models presents an alterReceived: 2024-08-15 Revised: 2 024-09-12Accepted: 2025-03-22 Published: 2025-05-26   
Height Above Nearest Drainage (HAND) as a Model for Rapid Flood Inundation Mapping Based on Remote Sensing and Geographic Information Systems in the Kapuas Sintang Sub Watershed Ajun Purwanto; Paiman
Jurnal Penelitian Pendidikan IPA Vol 9 No 8 (2023): August
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i8.3037

Abstract

This study aims to map the flood inundation and the extent of the inundation in the study area using the HAND model. The data used in this study is DEM. The DEM is used to generate a hydrologic framework, including flow accumulation, drainage network, flow direction, elevation, and flow distance. The method used in this study is the HAND descriptor. The analysis in this study used spatial hydrological analysis and hypsometric analysis using zonal statistical tables in ArcGIS. Based on the results of the analysis of height above the nearest drainage it is known that the Kapuas Sintang sub-watershed has five classes of inundation, namely very high inundation, high inundation, moderate inundation, low inundation, and no inundation. Very high, high, and moderate inundation classes are spread over three sub-districts, namely Sintang, Dedai, and Tempunak sections. Sintang District has the widest distribution, followed by Dedai District and Tempunak District is the narrowest. Prediction of inundation area and flood area with HAND can be used to improve the new mapping model without involving additional data sources. The HAND model is a nice and simple tool that is useful for inundation studies as well as in inundation area prediction.
Utilization of Deep Learning for Mapping Land Use Change Base on Geographic Information System: A Case Study of Liquefaction Ajun Purwanto; Paiman
Jurnal Penelitian Pendidikan IPA Vol 9 No 10 (2023): October
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i10.5032

Abstract

This study aims to extract buildings and roads and determine the extent of changes before and after the liquefaction disaster. The research method used is automatic extraction. The data used are Google Earth images for 2017 and 2018. The data analysis technique uses the Deep Learning Geography Information System. The results showed that the extraction results of the built-up area were 23.61 ha and the undeveloped area was 147.53 ha. The total length of the road before the liquefaction disaster occurred was 35.50 km. The extraction result after the liquefaction disaster was that the area built up was 1.20 ha, while the buildings lost due to the disaster were 22.41 ha. The total road length prior to the liquefaction disaster was 35.50 km, only 11.20 km of roads were lost, 24.30 km. Deep Learning in Geographic Information Systems (GIS) is proliferating and has many advantages in all aspects of life, including technology, geography, health, education, social life, and disasters.
Carbon Stocks Estimation Using the Stock Difference Method of Various Land Use Systems Based on Geospatial in Kualan Watershed Ajun Purwanto; Sulha
Jurnal Penelitian Pendidikan IPA Vol 10 No 11 (2024): November
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i11.6818

Abstract

Indonesia controls 75%-80% of the world's carbon stocks, so the amount of carbon stocks must be utilized optimally. This study aims to determine carbon stocks, potential emissions, and economic value of carbon stocks in each land use. The method used is secondary data analysis and field checking. The data collected were Sentinel 2A acquisitions in 2020 and 2022, Digital Elevation Model (DEM), and land use land cover in 2020-2023. Data analysis used SNAP and ArcGIS 10.8. The tool used for data analysis is spatial analysis map algebra. The results showed mixed dryland agriculture has the most extensive carbon stock, at 2,614,178 tons/ha, with potential emissions of 9,585,320 tons/ha. The most minor carbon stock is in mining land use, which is 0 tons/ha with potential emissions of 0 tons/ha. The highest C02 value in USD is the forest land use group. In the Secondary Dryland Forest, Secondary Swamp Forest, and Plantation Forest groups, it is 17,517,400.50 USD, while the lowest is mining land use, which is 0 USD. Overall, the CO2 value of land use in the study area is 34,246,314.45 USD. Integrating remote sensing data analysis and field surveys in geospatial technology is one of the new approaches to studying carbon stocks and CO2 emissions in topsoil from various land uses. By utilizing geospatial technology, efforts to estimate carbon stocks on the surface are easier and faster.
ANALYSIS OF THE CYCLONE WIND HAZARD LEVEL BASED ON REMOTE SENSING AND GIS IN PONTIANAK CITY Sudiro, Agus; Rosanti, Rosanti; Purwanto, Ajun
GeoEco Vol 11, No 2 (2025): GeoEco July 2025
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ge.v11i2.95552

Abstract

Over the past century, numerous cyclones have affected several countries, resulting in significant economic losses. This study uses GIS to determine the Danger Level of Tornadoes and the distribution of areas affected by tornadoes in Pontianak City in 2024. The method used is secondary data analysis. The data analysis technique used is a weighted, tiered quantitative analysis employing the Analytical Hierarchy Process (AHP) approach and the Overlay function in ArcGIS 10.8, utilising the Weighted Overlay spatial analysis tool. The data were rainfall, ground surface temperature, slope, and land cover. The four parameters were made into a map, each weighted using the AHP method. The results showed that the level of danger of tornadoes in Pontianak City in 2024, using GIS and Remote Sensing, has four classes: very low, low, medium, and high. Very low class has an area of 223.86 ha (2%), low class 3510.52 ha (35%), medium class 6262.04 ha (62%) and high class (151.71 ha (1%). Most of the classes of tornado hazard levels are medium. The distribution of this class is mostly in West Pontianak, Pontianak Kota, and South Pontianak sub-districts. The lower class is mostly located in Southeast and North Pontianak.
PADDY FIELD LAND CHANGE ANALYSIS BASED ON GEOGRAPHY INFORMATION SYSTEM AND REMOTE SENSING IN THE KUBU RAYA DISTRICT Ekawati, Ekawati; Rizieq, Rahmatullah; Bancin, Hardi Dominikus; Ellyta, Ellyta; Purwanto, Ajun
GeoEco Vol 11, No 1 (2025): GeoEco January 2025
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ge.v11i1.95991

Abstract

Rice plays an important role in ensuring food security worldwide, especially in Southeast Asia and Indonesia. This study aims to determine the changes in paddy field area and spatial distribution of changes in 2019-2024 using Geographic Information Systems and Remote Sensing in Kuburaya Regency. The data used in this study include the area of paddy fields in Kubu Raya Regency in 2019-2024, an Indonesia Biomass Image, and a Topographic map. The data obtained were then processed using ArcGIS Pro software. Data analysis used a geoprocessing tool with the clip, Intersect, and Dissolve tools and Spatial Analysis using the Map Algebra Tool. The study results showed that overall changes in paddy field area from 2019-2023 were 9036,88 ha. Of the fourteen villages in the Teluk Pakedai sub-district, eight villages experienced an increase in area. The villages are Sungai Nibung, Kuala Karang, Seruat Satu, Tanjung Bunga, Teluk Pakedai Hulu, Madura, Sungai Nipah, and Teluk Pakedai Satu. Overall, the eight villages experienced an increase in the paddy field area of 578.94 ha or 21% of the total area in 2024. Two villages, namely Sungai Nibung and Kuala Karang experienced a 100% increase from 2019. The Geographic Information System and Remote Sensing provide convenience in monitoring and analyzing changes in land use, in this case, changes in the area of paddy fields. Therefore, in the future, this technology will be more effective in its use, because with this technology the data will be easy to update.
GEOGRAPHICAL INFORMATION SYSTEM BASED COVID-19 VULNERABILITY MAPPING IN PONTIANAK REGENCY WEST KALIMANTAN Purwanto, Ajun; Nugraha, Yoga Prasetya Adi
GeoEco Vol 9, No 1 (2023): GeoEco January 2023
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ge.v9i1.60660

Abstract

2020 was the start of the toughest year for the world community in general, especially Indonesia in the health sector. The outbreak of the Covid-19 case in various regions requires clear and accurate information in efforts to deal with this pandemic. This study aims to spatially map the vulnerability of COVID-19 based on Geographic Information Systems (GIS). The method d used is a weighted overlay. The data used are population (P), population density (PD), elderly (EP), school students (SS), and hospital beds (HB). The analysis s used is a spatial analysis using ArcGIS 10.8. The results of the study show that North Pontianak District has a very high vulnerability to COVID-19. West Pontianak District and Pontianak City have high vulnerability. South and East Pontianak sub-districts have moderate vulnerability. For Pontianak Tenggara District, the level of vulnerability to COVID-19 is very low. The vulnerability to COVID is very low in Southeast Pontianak District because it has the smallest population, low density, few elderly people, few school-age children, and lots of hospital beds. An information system is an effective tool in conveying information on the spatial distribution of the level of vulnerability of Covid-19 in an area, so that steps and handling policies can be  taken according to existing priorities.
ANALYSIS OF MATERIAL LOSS DUE TO FLOOD DISASTER IN THE SUB-DISTRICT OF SILAT HULU, KAPUAS HULU REGENCY Nurfitri, Holifah; Rintia, Florensia; Francista, Francista; Rianingsih, Ayu; Ramdhania, Nurul; Purwanto, Ajun; Andrasmoro, Dony; Eviliyanto, Eviliyanto
GeoEco Vol 9, No 1 (2023): GeoEco January 2023
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ge.v9i1.62423

Abstract

A flood is a frequent disaster during the rainy season and causes many losses, good materials, treasure objects, and casualties. The study aims to know the loss of materials caused by disasters that flood Bandang in the Silat Hulu Sub-district. The method of research used is descriptive quantitative. Collected data is secondary data, including damaged buildings, facilities and infrastructure, and land. The method analysis used is descriptive. The study results show that The Silat Hulu Sub-district experienced twice the floods successively. The first occurred on 18-19 October 2021 and 22-23 October 2021. A total of 11 villages of 14 villages experience a flood. Consequently, the flood and loss of materials, including dozens of house inhabitants of Village Selangkai and Entebi, were damaged, collapsed, and swept away. A total of 1,813 families were evacuated, and the total loss consequence flood around Rp. 20,000,000.00-30,000,000.00.
Co-Authors Adella Rahayu Pangestu Agus Sudiro Agus Sudiro, Agus Arif Rohman Ayu Rianingsih Ayu Rianingsih Bancin, Hardi Dominikus Dony Andrasmoro Ekawati Ekawati Ellyta Ellyta Eviliyanto Eviliyanto, Eviliyanto Fera Fera Fitriani Dwi Susanti Florensia Rentia Florensia Rintia Francista Francista Francista Francista Francista, Francista Galuh Bayuardi Holifah Nurfitri Holifah Nurfitri Mohd Hairy Ibrahim Nurfitri, Holifah Nurul Ramdhania Paiman Paiman Paiman Rahmatullah Rizieq Ramdhania, Nurul Rentia, Florensia Rianingsih, Ayu Rika Angela Rintia, Florensia Rosanti, Rosanti Rosanti, Rosanti RR. Ella Evrita Hestiandari Rustam -, Rustam Sulha Wiwik Cahyaningrum Yoga Prasetya Adi Nugraha Yoga Prasetya Adi Nugraha, Yoga Prasetya Adi