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KLASIFIKASI UJARAN KEBENCIAN DI MEDIA SOSIAL APLIKASI X MENGGUNAKAN RANDOM FOREST DAN SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE (SMOTE)

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Penilaian

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Classification is one of the fields of Natural Language Processing that automates the classification of text into one or more appropriate categories based on its content by building a model using training data. This study aims to classify hate speech tweets on application X using Random Forest and Synthetic Minority Oversampling Technique (SMOTE). The Random Forest approach is used because of its ability to handle classification problems on complex datasets and the SMOTE technique to overcome the imbalance of the majority and minority classes in a dataset of 3,300 data, namely 2000 hate tweets and 1300 nothate tweets. The highest accuracy results occurred in the Random Forest + SMOTE algorithm with an accuracy of 89,4%, a precision of 95,0%, a recall of 74,7%, and an f1-score of 83,6%. The influence of the SMOTE technique on the performance results of the Random Forest algorithm in classifying can be seen in the accuracy level of the best model without using SMOTE 81% while the best model using SMOTE 89.4% increased by 10.37%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507002130	T170335	T1703352025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1703352025

Publisher

: Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xv, 73 hlm.; ill.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

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)

Teknik Informatika

Specific Detail Info

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Statement of Responsibility

EM

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