Skripsi

IMPLEMENTASI METODE ADAPTIVE BOOSTING (ADABOOST) DALAM KLASIFIKASI KEJADIAN HUJAN DENGAN DATA HILANG DI KOTA PALEMBANG

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Classifying rainfall events can be challenging when the available data is incomplete or contains missing values. Several imputation methods can be used to address missing data, such as the mean imputation method and the K-Nearest Neighbors Imputation method. For classification purposes, many techniques can be utilized, one of which is the Adaptive Boosting (AdaBoost) method. AdaBoost is designed to improve the performance of a single prediction model by using a weighting mechanism. This study aims to implement the AdaBoost method to classify rainfall events in Palembang City during the years 2018–2023 by applying the mean and KNN imputation methods to handle missing data. The application of the mean imputation method resulted in an accuracy of 74.25%, precision of 72.15%, recall of 87.44%, and an F1-score of 79.06%. On the other hand, the KNN Imputation method produced an accuracy of 75.21%, precision of 73.56%, recall of 86.45%, and an F1-score of 79.5%. The results of this study indicate that the AdaBoost method, when used to predict rainfall events and combined with the KNN imputation method for handling missing data, performs better than when using the mean imputation method. Keywords: Missing Data, Mean Imputation, KNN Imputation, and AdaBoost

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Detail Information

Series Title

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Call Number

T1911612025

Publisher

Indralaya : Prodi Ilmu Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Sriwijaya., 2025

Collation

xv, 237 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

006.310 7

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Prodi Ilmu Matematika
Algoritma AdaBoost

Specific Detail Info

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Statement of Responsibility

TUTI

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