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IMPLEMENTASI DEEP LEARNING MENGGUNAKAN ARSITEKTUR LONG SHORT TERM MEMORY(LSTM) UNTUK PREDIKSI CURAH HUJAN KOTA MALANG

Rizki, Muhammad (2019) IMPLEMENTASI DEEP LEARNING MENGGUNAKAN ARSITEKTUR LONG SHORT TERM MEMORY(LSTM) UNTUK PREDIKSI CURAH HUJAN KOTA MALANG. Undergraduate (S1) thesis, University of Muhammadiyah Malang.

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Abstract

The weather in Indonesia does not always run normally or in accordance with the season, the weather often changes suddenly at any time because there are factors that affect the decrease and increase in rainfall. weather forecasts are needed and very useful if the various parties because it can be a reference for various circles to undergo their daily activities. The study was conducted using Deep Learning method because of some previous research using Deep Learning in different cases able to produce accuracy above 85%. Deep learning is a network consisting of several layers. The layers are derived from a collection of nodes. The architecture used is Long Short Term Memory (LSTM) because in previous studies using LSTM in different case got good result that is small generated RMSE. LSTM has a structure like chains and structures in each cell there are 3 gates of forget gate, input gate, and output gate. Therefore, the calculations performed more complex plus the Deep Learning is expected to get more accurate results. The data used is the rainfall data of Malang city that comes from BMKG.

Item Type: Thesis (Undergraduate (S1))
Student ID: 201310370311008
Thesis Advisors: Setio Basuki (0714028403), Yufis Azhar (0728088701)
Keywords: Regression, Deep Learning, Long Short Term Memory (LSTM)
Subjects: Z Bibliography. Library Science. Information Resources > ZA Information resources
Divisions: Faculty of Engineering > Department of Informatics (55201)
Depositing User: Sulistyaningsih Sulistyaningsih
Date Deposited: 03 Sep 2019 03:27
Last Modified: 13 Jan 2020 02:23
URI : http://eprints.umm.ac.id/id/eprint/53028

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