Skripsi

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

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407002775	T143928	T1439282024	Central Library (References)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1439282024

Publisher

Inderalaya : Prodi Ilmu Fisika, Fakultas Matematika dan Ilmu Pengetahuan Alam., 2024

Collation

xiii, 42 hlm.; ilus.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

519.207

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Prodi Ilmu Fisika
Teori Kemungkinan--Prediksi Curah Hujan

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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