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All Journal MAJALAH ILMIAH GLOBE Jurnal Geografi The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Septianto Aldiansyah
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Earth & Planetary Sciences Education Environmental Science Social Sciences

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PEMETAAN KERENTANAN COVID-19 DI KABUPATEN KONAWE Septianto Aldiansyah; Mangapul Parlindungan; Rudi Parluhutan
JURNAL GEOGRAFI Vol 10 No 2 (2021)
Publisher : Jurusan Geografi Fakultas Ilmu Sosial Universitas Negeri Padang

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Abstract

Konawe Regency is one of the regencies in Southeast Sulawesi Province that has a high number of confirmed Covid-19 cases. The Covid-19 vulnerability mapping is carried out to facilitate decision making and formulate effective policies to minimize the spread and transmission of the disease. This study aims to examine the parameters that affect the spread and transmission of Covid-19 and the level of susceptibility to Covid-19. The method used is a Chi Square-based Geographic Information System (GIS). The parameters used include Population, Population Density, Elderly Age, Distance from Activity Centers, Distance from Road and Distance from Covid-19 Referral Hospital. The results showed that the population, old age and distance from the Covid-19 referral hospital had an effect on the spread and transmission of Covid-19 with a significance value of 0.000. The most vulnerable areas are in Wawotobi sub-district and Unaaha sub-district with an area of ​​127.68 km2 (5.38%). Areas with a medium level of vulnerability are 1606.57 km2 (67.74%) and low are 637.40 km2 (26.88%). There needs to be an increase in awareness of the use of masks, washing hands, maintaining distance and avoiding crowds because these activities can increase the chance of spreading and transmitting Covid-19.
SPATIAL TEMPORAL MAPPING OF VEGETATION COVER INDICES USING SENTINEL-2 MULTISPECTRAL INSTRUMENT IN UNAAHA CITY Septianto Aldiansyah; Duwi Setiyo Wigati Ningsih; Risna
Majalah Ilmiah Globe Vol. 26 No. 1 (2024): GLOBE VOL 26 TAHUN 2024
Publisher : Badan Informasi Geospasial

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Vegetation cover in urban areas contributes to providing livable ecosystem services for humans. As urbanization continues to expand, vegetation cover in urban areas will change rapidly. This research aims to monitor changes in vegetation cover in the last 5 years in Unaaha City using Google Earth Engine. This study also explores vegetation index algorithms such as NDVI, EVI, SAVI, and MSARVI. The research results show that forest vegetation cover continues to decline, followed by an increase in built-up land with an average change of 1,021 ha or the equivalent of 55% of the total area. This research also found that the NDVI algorithm had the best average accuracy with a value of OA=82.54%, followed by MSARVI=73.23%, SAVI=69.52%, and EVI=63.62%. This makes the NDVI method have good accuracy requirements for identifying vegetation compared to other vegetation index algorithms.
COMPARISON OF MACHINE LEARNING ALGORITHMS FOR LAND USE AND LAND COVER ANALYSIS USING GOOGLE EARTH ENGINE (CASE STUDY: WANGGU WATERSHED) Septianto Aldiansyah; Randi Adrian Saputra
International Journal of Remote Sensing and Earth Sciences Vol. 19 No. 2 (2022)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2022.v19.a3803

Abstract

Human population growth and land use and land cover (LULC) change have always developed side by side. Considering selection of a good Machine Learning (ML) classifier algorithm is needed considering the high estimation of LULC maps based on remote sensing. This study aims to produce a LULC classification of Landsat-8 and Sentinel-2 images by comparing the accuracy performance of three ML algorithms, namely: Classification and Regression Tree (CART), Random Forest (RF), and Support Vector Machine (SVM). Dataset comparison ratios were also explored to find the LULC classification results with the best accuracy. Sentinel-2 is better than Landsat-8 regarding Overall Accuracy (OA) and Coefficient Kappa. The comparison ratio of the training and testing datasets with a good level of accuracy is 70:30 on both images with the average OA Landsat-8 and Sentinel-2 being 92.09% and 94.21%, respectively. The RF algorithm outperforms CART and SVM in both types of satellite imagery. The mean OA of the CART, RF, and SVM classifiers was 92.03%, 94.74%, 83.54% on Landsat-8, 93.14%, 96.15%, and 93.34% on Sentinel-2, respectively.
Co-Authors Duwi Setiyo Wigati Ningsih Mangapul Parlindungan Randi Adrian Saputra Risna Rudi Parluhutan