PEMODELAN SPASIAL PENGARUH PERUBAHAN TUTUPAN LAHAN TERHADAP ZONA NILAI TANAH DI SWK GEDEBAGE, KOTA BANDUNG

• Main Author: Falih Kuswandi, Rifqi
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/65449
• Institution: Institut Teknologi Bandung

The construction of a primary center in East Bandung has been planned for a long time. Starting with the Gedebage and Ujungberung Regional Studies of Bandung City (2001) to the latest, Bandung City RDTR 2015-2035. There are several new infrastructure developments. Whereas in 2015, Gedebage District had the widest area of rice fields in Bandung (Gedebage District Statistics, 2015). This study identifies the relationship between land cover changes to the Land Value Zone (ZNT) and the f actors that influence it. The analysis used is the analysis of land cover map image processing in 2007, 2017 and 2021; analysis of the effect of land cover changes on ZNT; and analysis of factors that influence ZNT using multiple regression analysis. From the results of the study, it was found that the classification of paddy field cover experienced the most significant decrease in area from 9.53 km2 in 2007 and 4.44 km2 in 2017 to 3.41 km2 in 2021. Referring to the Cramer's V value of the chi square test, the relationship between land use with ZNT in 2021 is the highest with 0.644 compared to land cover with ZNT in 2007 which is 0.4163 and in 2021 it is 0.3263. Factors affecting ZNT are proximity to terminals and stations, local roads, arterial roads, rivers and land use areas for industrial use, waste management, trade and services, transportation, and agriculture. This factor is the result of multiple regression analysis with MSI where the independent variable is able to explain the dependent variable by 4 7.38%. However, if the Summarecon area is excluded from the study area, the factors that influence ZNT are proximity to stations, rivers, arterial roads, and transportation land uses, industrial designation areas, waste management, trade and services, and agriculture. This independent variable is able to explain the dependent variable of 53.75%.