PENGUJIAN METODE DATA MINING NEURAL NETWORK BACKPROPAGATION UNTUK MEMPREDIKSI CURAH HUJAN DI KOTA PALEMBANG (STUDI KASUS: BADAN METEOROLOGI, KLIMATOLOGI DAN GEOFISIKA, STASIUN KLIMATOLOGI PALEMBANG)

KHOTIMAH, TIARA KHUSNUL and Jambak, Muhammad Ihsan (2022) PENGUJIAN METODE DATA MINING NEURAL NETWORK BACKPROPAGATION UNTUK MEMPREDIKSI CURAH HUJAN DI KOTA PALEMBANG (STUDI KASUS: BADAN METEOROLOGI, KLIMATOLOGI DAN GEOFISIKA, STASIUN KLIMATOLOGI PALEMBANG). Undergraduate thesis, Sriwijaya University.

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Abstract

The Meteorology, Climatology and Geophysics Agency (BMKG) is a Non-Departmental Government Institution (LPND) whose task is to carry out government duties in the fields of meteorology, climatology and geophysics. BMKG is divided into several stations, one of which is the Palembang Climatology Station. Rainfall prediction is very important for BMKG to predict the weather that will occur. In this case, data mining can be an alternative in making predictions to assist BMKG management in the event of damage or errors from the rainfall gauges. Data mining can adopt the Cross-Industry Standard Process for Data Mining (CRISP-DM) method as a research stage and use the Neural Network Bakpropagation algorithm to find the lowest error value with a high level of accuracy. The test is carried out using weather data for 2020-2021 by utilizing the rapidminer application and cross validation as a learning and training technique for the data. The lowest RMSE error value obtained from the test results using the neural network algorithm is 14,018 with k-fold 4, training cycle of 150 iterations (looping), learning rate of 0.1 and momentum of 0.1, so that data mining techniques can be used as one alternative to predict rainfall that will occur in the future.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Data Mining, Neural Network Backpropagation, Prediksi Curah Hujan
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.D343 Data mining. Database searching. Big data.
Divisions:	09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User:	Tiara Khusnul Khotimah
Date Deposited:	04 Aug 2022 04:30
Last Modified:	04 Aug 2022 04:30
URI:	http://repository.unsri.ac.id/id/eprint/75996

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