Analisis Algoritma Random Forest dan Kombinasi Indeks Spektral untuk Identifikasi Lahan Terbangun (Kasus Kota Surakarta) - Rahmi, Ardia Tiara; Pratiwi, Kholis Hapsari; Deni, Delista Putri (STIKOM Tunas Bangsa Pematangsiantar, 2023)
In an effort to realize one of the objectives of the Sustainable Development Goals (SDGs) program goal 11 concerning Sustainable Cities and Settlements, controlling the intensity of urban built-up land in Indonesia really needs attention. One effort to monitor the condition of changes in built-up land that is easy, fast, cheap and efficient is to use the results of remote sensing data processing. Technological developments in the field of remote sensing are currently leading to processing based on Big Data and cloud computing, one of which is GEE (Google Earth Engine). Google Earth Engine. Identification of built-up land was carried out using the guided classification method of Machine Learning Random Forest and a combination of Spectral Index algorithms consisting of a combination of Urban Index (UI), Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI) algorithms. The object segmentation process on the results of the combined spectral index is carried out using the Otsu thresholding method. The results showed that the built-up area from the results of identification using the Random Forest algorithm and the spectral index combination algorithm in 2019 to 2023 has increased the area of land, which is shown in the 2019-2023 built-up land map. The accuracy test using the confusion matrix showed that the results of identification using a combination of spectral indices obtained OA and Kappa values which were included in the medium to high category, namely 98.69% and 0.969. So that this method can then continue to be used to monitor the pattern of development of built-up land in Surakarta City.