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All Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Mauridhy Hery Purnomo
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Computer Science & IT Education Engineering

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Comparison of K-NN, SVM, and Random Forest Algorithm for Detecting Hoax on Indonesian Election 2024 Indra; Agus Umar Hamdani; Suci Setiawati; Zena Dwi Mentari; Mauridhy Hery Purnomo
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.76079

Abstract

During the year 2022, The Indonesian National Police (POLRI) received 113 reports related to the spread of hoax news related to 2024 Indonesian Election (PEMILU). There are still relatively few hoax detection tools that already exist in Indonesia. This research creates a system that can detect hoax news in Indonesian tweets about the Indonesian Election (PEMILU) 2024 by comparing three methods, namely K-NN, SVM, and Random Forest. The process of labeling (create model) using validation on ground truth data, namely cekfakta.tempo, cekfakta.kompas, and turnbackhoax.id. In this research, we also check the differences between different types of distance measurements in applying the K-NN algorithm. The method used for feature extraction in this research is TF-IDF. The results of experiments show that the highest accuracy results are obtained using the SVM and K-NN algorithms with distance measurements using Euclidean Distance, which is 86.36%. The best precision value is obtained using the K-NN algorithm with distance measurements using Manhattan Distance, which is 86.95%.
Co-Authors Agus Umar Hamdani Indra Suci Setiawati Zena Dwi Mentari