Model Prediksi Kenaikan Permukaan Air Laut Menggunakan Data Satelit  Altimery Jason-1 dengan pendekatan Algoritma Long-Short Term  Memory (Studi Kasus: Teluk Jakarta)

Amien Rais; Della Ayu Lestari; Willdan Aprizal Arifin

doi:10.32663/georaf.v7i2.3203

Authors

Amien Rais Universitas Pendidikan Indonesia
Della Ayu Lestari Universitas Pendidikan Indonesia
Willdan Aprizal Arifin Universitas Pendidikan Indonesia

DOI:

https://doi.org/10.32663/georaf.v7i2.3203

Keywords:

Keywords: Forcasting, Sea Level Rise, Jakarta Bay, Long-Short Term Memory

Abstract

The capital city of Jakarta is the area with the highest population density in Indonesia with a population density of 16,937 people/sq km. Topographically, DKI Jakarta is located in the lowlands and is vulnerable to natural disasters, especially sea level rise. Data on sea level rise records show The trend of sea level rise is clearly visible in this tide gauge record from 1984 to 2004, at a rate of about 10mm/year. This certainly needs special attention to find out how much sea level rise will be so that it can be used as a coastal reference in making Jakarta regional policies. One way to find out the rate of sea level rise is by forecasting. In modeling time series forcing requires a model that can accommodate the time interval and the variables involved in the calculation. Each variable has a value depending on its past value and also on other past value variables. Therefore, we use the Long Short-Term Memory (LSTM) algorithm for forecasting sea level rise in Jakarta Bay. We use data from the last 30 years to model sea level rise in Jakarta Bay. The results show that there will be a maximum increase of 140 centimeters in 2040 with a maximum area of 6144.2 ha.

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