PERBANDINGAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) DAN NAIVE BAYES PADA ANALISIS SENTIMEN KURSUS ONLINE

PRAGUSTONO, MOHAMMAD RIZKI (2024) PERBANDINGAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) DAN NAIVE BAYES PADA ANALISIS SENTIMEN KURSUS ONLINE. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Udemy is an online learning platform that provides thousands of courses in a variety of subjects and taught by independent instructors. This research aims to compare two classification algorithms, namely Support Vector Machine (SVM) and Naive Bayes, in analyzing the sentiment of an online course called Udemy from the opinions of Twitter social media users. Data collection included 1509 tweets which were then preprocessed and weighted using the TF-IDF method. The data is divided into 80% training data and 20% test data, then classified using the Support Vector Machine (SVM) and Naive Bayes algorithms. The research results show that the SVM model is superior when compared to Naive Bayes. The SVM model has a significant overall accuracy of 77%, while Naive Bayes has an accuracy of 69%. Kata kunci: Sentiment Analysis, Udemy, Twitter, TF-IDF, Support Vector Machine, Naive Bayes. Udemy adalah platform pembelajaran online yang menyediakan ribuan kursus dalam berbagai subjek dan diajarkan oleh instruktur independen. Penelitian ini bertujuan untuk membandingkan dua algoritma klasifikasi, yaitu Support Vector Machine (SVM) dan Naive Bayes, dalam menganalisis sentimen kursus online bernama Udemy dari opini pengguna media sosial Twitter. Pengumpulan data meliputi 1509 tweet kemudian dilakukan preprocessing dan dibobotkan melalui metode TF-IDF. Data dibagi menjadi 80% data latih dan 20% data uji, lalu diklasifikasikan menggunakan algoritma Support Vector Machine (SVM) dan Naive Bayes. Hasil penelitian menunjukkan bahwa model SVM lebih unggul jika dibandingkan dengan Naive Bayes. Model SVM memiliki akurasi keseluruhan yang signifikan yaitu 77%, sedangkan Naive Bayes memiliki akurasi sebesar 69%. Kata kunci: Analisis Sentimen, Udemy, Twitter, TF-IDF, Support Vector Machine, Naive Bayes.

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 24 108
Call Number:	SIK/15/24/074
NIM/NIDN Creators:	41520010222
Uncontrolled Keywords:	Analisis Sentimen, Udemy, Twitter, TF-IDF, Support Vector Machine, Naive Bayes.
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 > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika 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 > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.31 Machine Learning/Pembelajaran Mesin 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	khalimah
Date Deposited:	16 Jul 2024 01:50
Last Modified:	16 Jul 2024 01:50
URI:	http://repository.mercubuana.ac.id/id/eprint/89561

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