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All Journal International Journal of Public Health Science (IJPHS) Indonesian Journal of Geography Geo Media: Majalah Ilmiah dan Informasi Kegeografian Media Komunikasi Geografi Jurnal Geografi : Media Informasi Pengembangan dan Profesi Kegeografian Jurnal Teknosains Geoplanning : Journal of Geomatics and Planning Majalah Geografi Indonesia JTSL (Jurnal Tanah dan Sumberdaya Lahan) Jurnal Ketahanan Nasional JURNAL AGROTEKNOLOGI Forum Geografi GEA, Jurnal Pendidikan Geografi Jurnal Fisika FLUX Journal of Applied Geospatial Information Jurnal Geografi Forest and Society International Journal of Remote Sensing and Earth Sciences (IJReSES) JAGAT (Jurnal Geografi Aplikasi dan Teknologi) Jurnal Geosains dan Remote Sensing (JGRS) geoedusains: Jurnal Pendidikan Geografi GEOGRAPHIA: Jurnal Pendidikan dan Penelitian Geografi JKKI : Jurnal Kedokteran dan Kesehatan Indonesia Proceeding ISETH (International Summit on Science, Technology, and Humanity) Jurnal Manusia dan Lingkungan Aerospace Engineering IJES : Indonesian Journal of Environmental Sustainability.
Prima Widayani
Magister Penginderaan Jauh, Fakultas Geografi, Universitas Gadjah Mada
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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Journal : Jurnal Geografi

 1 
ASSESSMENT AND COMPARISON OF MACHINE LEARNING ALGORITHM CAPABILITY IN SPATIAL MODELING OF DENGUE FEVER VULNERABILITY BASED ON LANDSAT IMAGE 8 OLI/TIRS Rahmat Azul Mizan; Prima Widayani; Nur Mohammad Farda
JURNAL GEOGRAFI Vol 13, No 2 (2021): JURNAL GEOGRAFI
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jg.v13i2.21019

Abstract

The spread of dengue fever in Indonesia has become a major health problem. Spatial modeling for the distribution of dengue fever vulnerability is an important step to support the planning and mitigation of dengue fever in Indonesia. This study aims to assess and compare the capability of two machine learning algorithms to create a spatial model of dengue fever vulnerability. The research was conducted in Baubau City, Southeast Sulawesi Province by taking 129 cases that occurred from 2015 to February 2016. In this study, the model was created using R software and machine learning algorithms including support vector machine (SVM) and random forest (RF). The six modeling variables involved include land use/cover, BLFEI, NDVI, LST, rainfall and humidity extracted from Landsat 8 OLI/TIRS imagery as well as BMKG (Meteorological, Climatological, and Geophysical Agency of Indonesia) and BWS climate data. The model's capability was assessed using the Area Under Curve-Receiver Operating Characteristic (AUC-ROC) curve. The results of the research show that both algorithms provide excellent model accuracy with AUC values of 1 for SVM and 0.997 for RF with SVM as the best algorithm for modeling dengue fever in Baubau City.Keywords: Machine Learning, Vulnerability, Dengue Fever, Landsat 8 Image
Land-Cover Change Detection in Batur Catchment Area Using Remote Sensing Ni Kadek Oki Febrianti; Projo Danoedoro; Prima Widayani
JURNAL GEOGRAFI Vol 15, No 1 (2023): JURNAL GEOGRAFI
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jg.v15i1.32670

Abstract

Land cover information is an essential aspect in the planning and management of earth modeling and understanding. Land cover changes impact the physical and social environment, such as hydrological conditions and ecological systems. This study aimed to identify spatial differences in the land cover of the Batur catchment area from 2015-2021 by using a remote sensing approach to describe the existing land-cover site and to detect its changes. The methods used in this study are a combination of the vegetation index and a supervised classification maximum likelihood algorithm with Landsat 8 OLI/TIRS in 2015 and 2021. Furthermore, the Change Detection Feature, identified from two image periods in 2015-2021 and processed, is used to detect changes in land cover. The accuracy assessment utilized QuickBird imagery recorded in 2015; field survey data were taken in 2021. The results showed that between 2015 to 2021, built-up area, bare land, shrubs, and lake have increased by 102,66% (306,01 ha), 27,95% (452,25 ha), 15,20% (215,72 ha) and 4,05 % (62,73 ha) while dryland forest and dry-dry-field have decreased by -25,84% (-606,29 ha) and -14.59% (-430,42 ha), respectively. The overall accuracy of the multispectral classification results in 2015 and 2021 was 82,63% and 89,57%.Keywords: Land-Cover Change; Batur; Catchment Area; Remote Sensing 
Land-Cover Change Detection in Batur Catchment Area Using Remote Sensing Febrianti, Ni Kadek Oki; Danoedoro, Projo; Widayani, Prima
JURNAL GEOGRAFI Vol. 15 No. 1 (2023): JURNAL GEOGRAFI
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jg.v15i1.32670

Abstract

Land cover information is an essential aspect in the planning and management of earth modeling and understanding. Land cover changes impact the physical and social environment, such as hydrological conditions and ecological systems. This study aimed to identify spatial differences in the land cover of the Batur catchment area from 2015-2021 by using a remote sensing approach to describe the existing land-cover site and to detect its changes. The methods used in this study are a combination of the vegetation index and a supervised classification maximum likelihood algorithm with Landsat 8 OLI/TIRS in 2015 and 2021. Furthermore, the Change Detection Feature, identified from two image periods in 2015-2021 and processed, is used to detect changes in land cover. The accuracy assessment utilized QuickBird imagery recorded in 2015; field survey data were taken in 2021. The results showed that between 2015 to 2021, built-up area, bare land, shrubs, and lake have increased by 102,66% (306,01 ha), 27,95% (452,25 ha), 15,20% (215,72 ha) and 4,05 % (62,73 ha) while dryland forest and dry-dry-field have decreased by -25,84% (-606,29 ha) and -14.59% (-430,42 ha), respectively. The overall accuracy of the multispectral classification results in 2015 and 2021 was 82,63% and 89,57%.Keywords: Land-Cover Change; Batur; Catchment Area; Remote Sensing 
MODIS Satellite Imagery for Monitoring Carbon Sequestration Potential and Its Drivers in Jambi Province, Indonesia Widayani, Prima; Arrafi, Muhammad
JURNAL GEOGRAFI Vol. 17 No. 1 (2025): JURNAL GEOGRAFI
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jg.v17i1.62343

Abstract

Jambi Province is a province in Indonesia whose land use is dominated by forests and plantations. Threats to land conversion and forest fires in the region have reduced vegetation and will threaten carbon absorption in the future. This study aims to map and assess the potential for carbon absorption and triggering factors by evaluating the spatiotemporal Net Primary Productivity (NPP) pattern to estimate Jambi Province's carbon absorption. This study uses remote sensing data to obtain NPP values ​​and several variables that will be assessed for their influence on NPP. MODIS satellite imagery is used to obtain NPP data, forest cover, Normalized Data Vegetation Index (NDVI) and Land Surface Temperature (LST). Shuttle Radar Topography Map (SRTM) imagery obtains topography and slope data. Population data in the form of the Human Development Index, total population and population in urban areas were obtained from the Central Statistics Agency of Jambi Province. The average NPP value 2003 in Jambi Province was 0.911 kgC/m/year, then the average NPP decreased to 0.754 kgC/m/year in 2023. Based on statistical analysis, there is a correlation between NPP and NDVI, slope, and topography.
Co-Authors Achmad Fadhilah Achmad Fadilah Ade Febri Sandhini P Agatha Andriantari Agus Joko Pitoyo Akmal Hafiudzan Andung Bayu Sekaranom Arief Wicaksono Arrafi, Muhammad Bagus Wiratmoko Bowo Susilo Dewi Miska Indrawati Dyah Kusuma, Dyah Edi Suharyadi Erika Yuliantari Fadilah, Achmad Fathilda, Intan Khaeruli Febrianti, Ni Kadek Oki Ghosh, Kapil Hamim Zaky Hadibasyir Hari Kusnanto Hidayatullah, Faqih Huwaida Nur Salsabila Indrawati, Dewi Miska Ira Nurmala Hani Irawan, Irfan Zaki Irfan Zaki Irawan Irfan Zaki Irawan Irsan, Laode Muhamad Iswari Nur Hidayati Kapil Ghosh Kusbaryanto Mahendra, Auzaie Ihza Mizan, Rahmat azul Muhammad Arrafi Muhammad Kamal Muhammad Kamal Muhammad Minan Chusni Muhammad Sufwandika Wijaya Muhammad Sufwandika Wijaya Muhammad Sufwandika Wijaya Murti Budi Santosa, Sigit Heru Ni Kadek Oki Febrianti Nur Mohammad Farda Nur Mohammad Farda Nurbandi, Wahyu Nurhadi, Muhammad Nurul Astuti, Nurul Nurweni, Susi Nurwita Mustika Sari Projo Danoedoro Projo Danoedoro Projo Danoedoro R. Suharyadi Ramadhan Pasca Wijaya Rina Febriany Sandy Budi Wibowo Sanjiwana Arjasakusuma Sanjiwana Arjasakusuma, Sanjiwana Santosa, Sigit Herumurti Budi Seandrasto Abi Kharis Wardhani Shandra S Pertiwi Sigit Heru Murti Sitti Rahmah Umniyati Sudaryatno Sudaryatno Sugeng Juwono Mardihusodo Suherningtyas, Ika Afianita Totok Gunawan Totok Wahyu Wibowo Tri Wulandari Kesetyaningsih Ulfa Aulia Syamsuri Vandam Caesariadi Bramdito Wahyu Nurbandi Wiratmoko, Bagus Wirayuda, I Kade Alfian Kusuma Zahrotunisa, Siti