PERBANDINGAN ALGORITMA SVM, NA�VE BAYES, DAN RANDOM FOREST UNTUK ANALISIS SENTIMEN TWITTER MENJELANG PEMILU 2024

NURIADI, MUHAMAD BAYU (2023) PERBANDINGAN ALGORITMA SVM, NA�VE BAYES, DAN RANDOM FOREST UNTUK ANALISIS SENTIMEN TWITTER MENJELANG PEMILU 2024. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

In the era of rapid development of information and communication technology, online information spreads quickly among the public. In the context of the 2024 elections, it is important to conduct political education to avoid the spread of information that is not true and has the potential to cause division. Therefore, this research uses machine learning algorithms to classify sentiment analysis of positive and negative comments on Twitter. The tweet comment data is taken through the crawling process on Twitter and through preprocessing to get accurate results. SVM, Naive Bayes, and Random Forest algorithms are used in data testing, and the results are displayed in the form of visualizations. The results show that the SVM algorithm has the highest accuracy (89%), followed by Random Forest (85%) and Naive Bayes (84%). Keyword : Sentiment Analysis, Indonesian General Election 2024, Machine Learning, Twitter Dalam era perkembangan teknologi informasi dan komunikasi yang pesat, informasi Online menyebar dengan cepat di kalangan masyarakat. Dalam konteks pelaksanaan Pemilu 2024, penting untuk melakukan pendidikan politik guna menghindari penyebaran informasi yang tidak benar dan berpotensi menyebabkan perpecahan. Oleh karena itu, penelitian ini menggunakan algoritma machine learning untuk mengklasifikasikan analisis sentimen terhadap komentar positif dan negatif di Twitter. Data komentar tweet diambil melalui proses crawling di Twitter dan melalui preprocessing untuk mendapatkan hasil yang akurat. Algoritma SVM, Naive Bayes, dan Random Forest digunakan dalam pengujian data, dan hasilnya ditampilkan dalam bentuk visualisasi. Hasil penelitian menunjukkan bahwa algoritma SVM memiliki akurasi tertinggi (89%), diikuti oleh Random Forest (85%) dan Naive Bayes (84%). Kata Kunci : Analisis Sentimen, Pemilu 2024, Machine Learning, Twitter

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 23 080
NIM/NIDN Creators:	41519010079
Uncontrolled Keywords:	Analisis Sentimen, Pemilu 2024, Machine Learning, Twitter
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 003 Systems/Sistem-sistem 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika 100 Philosophy and Psychology/Filsafat dan Psikologi > 150 Psychology/Psikologi > 154 Subconscious and Altered States and Process/Psikologi Bawah Sadar > 154.6 Sleep Phenomena/Fenomena Tidur > 154.63 Dreams/Mimpi > 154.634 Analysis/Analisis
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	CALVIN PRASETYO
Date Deposited:	15 Sep 2023 08:05
Last Modified:	15 Sep 2023 08:05
URI:	http://repository.mercubuana.ac.id/id/eprint/80964

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