Skripsi

KNOWLEDGE DISCOVERY DALAM KOMPARASI ALGORITMA ENSEMBLE BOOSTING PADA KLASIFIKASI KUALITAS AIR

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Good water quality is one of the leading indicators in supporting the life of living things. However, pollution from industrial, domestic, and agricultural waste has resulted in a decline in water quality, which has the potential to cause environmental and health problems. Rapid technological advancements have made machine learning algorithms a viable alternative for classifying water quality. This research aims to evaluate and compare the effectiveness of four ensemble boosting algorithms — AdaBoost, Gradient Boosting, XGBoost, and CatBoost — in identifying water quality. This research process was conducted using the Knowledge Discovery in Databases (KDD) approach as its methodological framework, which involves a series of stages, including data selection, pre-processing, transformation, data mining, and evaluation. From this study, it can be seen that the XGBoost model provides the most optimal performance with an accuracy value of 99.04% and precision, recall, and F1-score values of 99%. Followed by CatBoost, which has results almost equivalent to XGBoost, namely an accuracy of 98.76% and 99% on the precision, recall, and F1-score metrics. Meanwhile, the following positions are occupied by Gradient Boosting and AdaBoost.

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Detail Information

Series Title

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Call Number

T1895992025

Publisher

Indralaya : Prodi Sistem Informasi, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xvi, 74 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

006.350 7

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Prodi Sistem Informasi
Algoritma Ensemble Boosting--Analisis Data

Specific Detail Info

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MI

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