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All Journal Indonesian Journal of Geography Edukasi: Jurnal Pendidikan Sosial Horizon: Jurnal Pendidikan Sosial Jurnal Penelitian Pendidikan IPA (JPPIPA) Geodika: Jurnal Kajian Ilmu dan Pendidikan Geografi Jurnal Inovasi Pendidikan dan Pengajaran GeoEco Geosfera Indonesia
Paiman
IKIP PGRI Pontianak
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 Other

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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.
USING SENTINEL-2 IMAGE FOR MANGROVE HEALTH ANALYSIS IN BAKAU KECIL VILLAGE, MEMPAWAH DISTRICT, WEST KALIMANTAN Ajun Purwanto; Paiman Paiman
GeoEco Vol 10, No 2 (2024): GeoEco July 2024
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ge.v10i2.72707

Abstract

This study aims to determine 1) The index of mangrove plant vegetation density, and 2) the state of the mangrove plants in the village of Bakau Kecil. Transforming the NDVI was the method employed in this study. The canopy density model can be applied using NDVI. The degree of vegetation canopy density was correlated with the intensity of greenness. The outcomes demonstrated that NDVI values ranged from -1 to 0.32, indicating sparse vegetation density, 0.33 to 0.42, indicating medium density values, and 0.43 to 1, indicating dense density values. One can categorize the condition of the mangrove vegetation based on the NDVI index value, which is shown above. Based on a vegetation index value of 0.43 - 1, which indicates very good health, mangrove vegetation can be considered to be in excellent condition. The mangrove vegetation is in good health (vegetation value 0.33-0.42, Moderate), and the vegetation is in poor health (vegetation value -1-0.32, Rare), according to the vegetation index. Mangrove health is very good, with a pixel area percentage of 68.88 percent; good health has a pixel area percentage of 23.98 percent; and poor health has a pixel area percentage of 7.14 percent. 
Co-Authors Agus Sudiro Ajun Purwanto Ananda Yuniar Dony Andrasmoro Eviliyanto Eviliyanto, Eviliyanto Norsidi Norsidi Pitalis Mawardi B Rustam -, Rustam Suherdiyanto Suherdiyanto