Lake Rawa Pening, in Semarang Regency, is one of the super lakes of revitalization priority. Lake revitalization is an activity to restore the natural function of the lake as a water reservoir through lake dredging, cleaning of invasive alien plants, and land use planning. This makes land use and land cover information in Lake Rawa Pening useful for formulating policy strategies related to revitalization. This study will discuss land cover mapping in Lake Rawa Pening. Mapping using Landsat 9 Imagery and machine learning on Google Earth Engine (GEE). Machine Learning used in this study is CART and RF. The research result shows that the land cover map with the best accuracy is obtained from machine learning RF with an overall accuracy of around 0.78, while CART machine learning is approximately 0.76. The overall accuracy values for CART and RF are not much different because they are both decision tree-based machine learning. This research needs to be developed using cloud masking, comparing image transformations, and comparing its predecessor data, namely Landsat 8. This is useful for providing representative land cover data as the basis for the policy of revitalizing Lake Rawa Pening.
Informasi penutup lahan merupakan data yang sangat penting dalam pengelolaan Daerah Aliran Sungai (DAS). Tantangan dalam penyediaan informasi penutup lahan di DAS Kreo adalah tutupan awan dan cangkupan areanya yang cukup luas. Hadirnya platform pengolahan data spasial berbasis cloud yaitu Google Earth Engine (GEE) bisa menjawab tantangan tersebut. Oleh karena itu penelitian ini bertujuan untuk memetakan penutup lahan di DAS Kreo menggunakan klasifikasi berbasis machine learning pada GEE. Proses pemetaan penutup lahan di DAS Kreo menggunakan citra satelit Landsat 8 dan DEM SRTM. Input data yang digunakan antara lain band 1 sampai 7 pada citra Landsat 8, transformasi NDVI dan NDBI serta nilai elevasi dari DEM SRTM. Adapun tahun yang dipilih adalah tahun 2015 dan 2020 dengan machine learning yang diujikan meliputi CART, Random forest dan Voting SVM. Hasil penelitian ini menunjukkan bahwa machine learning yang terbaik dalam memetakan penutup lahan di DAS Kreo adalah Random forest. Penelitian ini masih terdapat banyak keterbatasan terutama kelas penutup lahan yang dipetakan.
The relocation of the capital city of Indonesia to Sepaku District, North Penajam Paser Regency, East Kalimantan will have an impact on all existing aspects. Land cover in Sepaku District has decreased from year to year. The area of vegetated land cover in Sepaku District is assumed to be decreasing due to the relocation of the New Capital City (NCC) to the area, which will have a negative impact on the environment. Therefore, this study aims to determine the level of land cover change in Sepaku District and discuss how the condition of land cover in Sepaku District after the construction of the New Capital City (NCC). The method in this study uses GIS (Geographical Information System) analysis and descriptive analysis. The data used are land cover data from 2009 to 2019 to find out changes in land cover and population data from BPS to project the population. Based on the results of the analysis of land cover data from 2009 to 2019 there was a change from scrubland to industrial forest plantations of around 26000 ha. In addition, there was no significant change in the area of built-up land. However, the results of population projections show that Sepaku Sub-district will experience a significant increase in built-up land cover after the construction of the New Capital City (NCC).
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