PENERAPAN METODE GENERALIZED LINEAR MODEL DENGAN MISSING VALUE IMPUTATION, DISKRITISASI DATA, DAN SMOTE UNTUK KLASIFIKASI KEJADIAN HUJAN (Studi Kasus: Data Cuaca Harian Kota Palembang Tahun 2018-2023)

AGUSTINA, PRANSISKA YOLANDA and Zayanti, Des Alwine and Amran, Ali (2024) PENERAPAN METODE GENERALIZED LINEAR MODEL DENGAN MISSING VALUE IMPUTATION, DISKRITISASI DATA, DAN SMOTE UNTUK KLASIFIKASI KEJADIAN HUJAN (Studi Kasus: Data Cuaca Harian Kota Palembang Tahun 2018-2023). Undergraduate thesis, Sriwijaya University.

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Abstract

Palembang City often faces erratic weather especially during the rainy season, which can cause flooding, disrupt daily activities, and damage infrastructure. So by classifying rainfall events can anticipate the impacts that occur due to rain and by utilizing weather data, the Palembang City government can be more aware of natural threats, protect the local economy, and maintain a sustainable city environment. The event data used for classification has missing data and imbalanced data. To overcome missing data, KNN (k = 5) is used and data discretization is done to convert continuous data into discrete data. Furthermore, imbalanced data is overcome with SMOTE. Thus, the accuracy on imbalanced data is 72.603%, precision 68.864% and recall 92.611%. Furthermore, on balanced data, the accuracy is 72.192%, precision 74.224%, and recall 76.601%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	KNN, SMOTE, Hujan
Subjects:	Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics > QA279.C663 Response surfaces (Statistics)
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Pransiska Yolanda Agustina
Date Deposited:	22 Jan 2025 06:28
Last Modified:	22 Jan 2025 06:28
URI:	http://repository.unsri.ac.id/id/eprint/166069

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