INTEGRASI TEKNOLOGI PENGINDERAAN JAUH DAN SISTEM INFORMASI GEOGRAFIS UNTUK MENGANALISIS FAKTOR YANG MEMPENGARUHI SEBARAN AREA BANJIR (STUDI KASUS : DKI JAKARTA)

• Main Author: Navrazzilova, Zhevina
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/65166
• Institution: Institut Teknologi Bandung

The strategic location of DKI Jakarta Province in the Indonesian Archipelago and the State Capital's existence make Jakarta the center of almost all government activities, technology development, and other industries. However, Jakarta itself is a province with the highest population density level in Indonesia according to the Central Statistics Agency in 2020, which is 16882 people/m2 and with a very high Human Development Index category, so Jakarta is growing rapidly. The development of Jakarta is also accompanied by negative impacts, namely the lack of green open land, high use of groundwater, and building loads. Along with the time and development of Jakarta, these negative impacts have made the issue of flooding in Jakarta familiar, be it floodwaters or tidal flooding in coastal areas, needless to say, this issue needs to be followed up so as not to interfere with existing activities in DKI Jakarta considering that Jakarta is the center of government. This final project was made to analyze the relationship between factors that affect the distribution of flooded areas in Jakarta using land cover change data in 2014 and 2021, digital elevation model (DEM) obtained from Lidar and DEMNAS data, land subsidence data, and the distribution of flooded areas in Jakarta with integrating of Remote Sensing Technology and Geographic Information Systems. As the results, analysis of the relationship using the overlay analysis method resulted in 50% of the flooded DKI Jakarta area being affected by land cover changes, 50.9% of the flooded DKI Jakarta area being affected by land subsidence, and 78.78% of the flooded DKI Jakarta area being influenced by slopes. While the analysis using linear regression found that the residuals from the results of linear regression were not normally distributed, can be concluded that the data used in this study could not be analyzed using linear regression.