PREDIKSI CURAH HUJAN MENGGUNAKAN DEEP LEARNING ALGORITMA RECURRENT NEURAL NETWORK ( RNN ) STUDI KASUS DI KOTA PALEMBANG

DERAL, MUHAMMAD and Affandi, Azhar Kholiq (2024) PREDIKSI CURAH HUJAN MENGGUNAKAN DEEP LEARNING ALGORITMA RECURRENT NEURAL NETWORK ( RNN ) STUDI KASUS DI KOTA PALEMBANG. Undergraduate thesis, Sriwijaya University.

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Abstract

This research aims to address the significant challenges caused by climate change in Palembang City, which has experienced substantial variations in rainfall with significant impacts during the period from 1999 to 2010. This research focuses on developing a predictive model using a Recurrent Neural Network (RNN) to forecast rainfall in Palembang from February 4, 2023 to February 4, 2028. The utilization of data from NASA POWER (2002-2023) provides a strong foundation, involving parameters such as rainfall, air humidity, wind speed, and air pressure. The RNN model is evaluated using the Mean Absolute Error (MAE) and Square metrics to measure its accuracy. This research yields an RNN model with an accuracy rate of 95% and a Mean Absolute Error (MAE) of 4.49, indicating good accuracy in predicting rainfall.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Palembang, Recurrent Neural Network, Curah Hujan, Perubahan Iklim.
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	08-Faculty of Mathematics and Natural Science > 45201-Physics (S1)
Depositing User:	Muhammad Deral
Date Deposited:	14 May 2024 04:59
Last Modified:	14 May 2024 04:59
URI:	http://repository.unsri.ac.id/id/eprint/143928

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