EVALUASI PERFORMA NAIVE BAYES DAN SVM DALAM ANALISIS SENTIMEN KENDARAAN LISTRIK DI MEDIA SOSIAL TWITTER

HENDRAWAN, GIGIH NUR (2024) EVALUASI PERFORMA NAIVE BAYES DAN SVM DALAM ANALISIS SENTIMEN KENDARAAN LISTRIK DI MEDIA SOSIAL TWITTER. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

The research aims to find out the comparison of the performance levels of accuracy, recall and precision of the Naïve Bayes algorithm and the Support Vector Machine (SVM) in classifying public sentiment on social media Twitter towards electric vehicles. The type of research used is quantitative research that refers to research approaches by collecting data that can be measured numerically or using statistical methods to analyze the data. Methods used include data collection, labelling, preprocessing, TF-IDF grinding, modeling with Naïve Bayes algorithm and Support Vector Machine (SVM) to the process of data visualization and analysis. The tool used to collect data from Twitter is Web Crawling. The research results show that the SVM model outperforms Naive Bayes with a significant overall accuracy of 95.79%, compared to Naive Bayes' recorded accuracy of 87.39%. Keywords : Sentiment Analysis, Suppor vector Machine, Naïve Bayes, electric vehicle Penelitian ini bertujuan untuk mengetahui hasil perbandingan performa tingkat akurasi, recall dan precission dari Algoritma Naïve Bayes dan Support Vector Machine (SVM) dalam mengklasifikasikan sentimen masyarakat pada media sosial Twitter terhadap kendaraan listrik. Jenis penelitian yang dipergunakan adalah penelitian kuantitatif yang mengacu pada pendekatan penelitian dengan cara mengumpulkan data yang dapat diukur secara numerik atau menggunakan metode statistik untuk menganalisis data tersebut. Metode yang digunakan yaitu pengumpulan data, labelling, preprocessing, pembobotan TF-IDF, Pemodelan dengan Algoritma Naïve Bayes dan Support Vector Machine (SVM) hingga proses visualisasi dan analisis data. Alat yang digunakan untuk pengumpulan data dari Twitter yaitu Web Crawling . Hasil penelitian menunjukkan bahwa model SVM mengungguli Naive Bayes dengan akurasi keseluruhan yang signifikan yaitu 95.79%, berbanding dengan akurasi Naive Bayes yang tercatat sebesar 87.39%. Kata Kunci : Analisi Sentimen, Suppor vector Machine, Naïve Bayes, kendaraan listrik

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 24 080
NIM/NIDN Creators:	41518120005
Uncontrolled Keywords:	Analisi Sentimen, Suppor vector Machine, Naïve Bayes, kendaraan listrik
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 > 006 Special Computer Methods/Metode Komputer Tertentu > 006.7 Multimedia Systems/Sistem-sistem Multimedia > 006.75 Social Multimedia/Multimedia Social > 006.754 Online Social Network/Situs Jejaring Sosial, Sosial Media 300 Social Science/Ilmu-ilmu Sosial > 380 Commerce, Communications, Transportation (Perdagangan, Komunikasi, Transportasi) > 388 Ground Transportation/Transportasi Jalan Raya > 388.3 Vehicular Transportation/Transportasi kendaraan 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 530 Physics/Fisika > 537 Electricity/Fisika Listrik > 537.6 Electrodinamics, Electric Current/Elektrodinamik, Arus Listrik
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	khalimah
Date Deposited:	04 Apr 2024 03:46
Last Modified:	04 Apr 2024 03:46
URI:	http://repository.mercubuana.ac.id/id/eprint/87923

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