KLASIFIKASI INTENSITAS CURAH HUJAN DI KOTA PALEMBANG DENGAN MENGGUNAKAN ALGORITMA DECISION TREE (C4.5)(STUDI KASUS: BMKG STASIUN KLIMATOLOGI PALEMBANG)

ANANDA, SICILLIA RIZKI and Jambak, Muhammad Ihsan and Bardadi, Ali (2023) KLASIFIKASI INTENSITAS CURAH HUJAN DI KOTA PALEMBANG DENGAN MENGGUNAKAN ALGORITMA DECISION TREE (C4.5)(STUDI KASUS: BMKG STASIUN KLIMATOLOGI PALEMBANG). Undergraduate thesis, Sriwijaya University.

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Abstract

Based on its astronomical position, Indonesia experiences two seasons, the dry and the rainy season. Erratic seasonal changes will affect activities in various sectors. Therefore, it is important to know what factors that influence changes in seasonal patterns in the dry and the rainy seasons classification. The availability of data on weather factors provided by the BMKG will certainly support extracting information from this data. In classifying the dry and rainy seasons based on weather factors, a data mining method is used, namely classification. Decision tree (C4.5) is the algorithm used in classifying seasons in Palembang City. Tests were executed using weather data from January 1, 2019 to September 30, 2022 using cross validation in training and testing models through the RapidMiner application. From the test results using the confusion matrix, the classification result with the highest accuracy value is 75.68%. The nine factors that influence the classification of seasons are date, Tavg, Tx, RH_avg, ddd_car, Tn, ss, RR, and ff_x. Wherein the attribute date (time of occurrence) is the most important and main attribute or factor in determining the classification of the dry and the rainy seasons.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Data Mining, Klasifikasi, Decision Tree (C4.5), Musim, Cuaca, RapidMiner
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User:	Sicillia Rizki Ananda
Date Deposited:	25 Jul 2023 04:15
Last Modified:	25 Jul 2023 04:15
URI:	http://repository.unsri.ac.id/id/eprint/121317

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