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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)

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Penilaian

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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%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507000957	T166069	T1660692024	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1660692024

Publisher

Indralaya : Prodi Ilmu Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Sriwijaya., 2024

Collation

viii, 41 hlm.; ill.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

510.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Matematika

Specific Detail Info

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Statement of Responsibility

EM

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