Perbandingan Efektivitas Nae Bayes dan SVM dalam Menganalisis Sentimen Kebencanaan di Youtube

 Tarissa Aura Azzahra (Universitas Dian Nuswantoro, Semarang, Indonesia)
 (*)Nurul Anisa Sri Winarsih Mail (Universitas Dian Nuswantoro, Semarang, Indonesia)
 Galuh Wilujeng Saraswati (Universitas Dian Nuswantoro, Semarang, Indonesia)
 Filmada Ocky Saputra (Universitas Dian Nuswantoro, Semarang, Indonesia)
 Muhammad Syaifur Rohman (Universitas Dian Nuswantoro, Semarang, Indonesia)
 Danny Oka Ratmana (Universitas Dian Nuswantoro, Semarang, Indonesia)
 Ricardus Anggi Pramunendar (Universitas Dian Nuswantoro, Semarang, Indonesia)
 Guruh Fajar Shidik (Universitas Dian Nuswantoro, Semarang, Indonesia)

(*) Corresponding Author

Submitted: December 18, 2023; Published: January 10, 2024


Advancements in the field of Natural Language Processing (NLP) have opened significant opportunities in sentiment analysis, particularly in the context of disaster response. In today's digital era, YouTube has emerged as a primary source for the public to acquire information regarding critical events. This study explores and compares two dominant sentiment analysis techniques, namely Naive Bayes and Support Vector Machine (SVM). It utilizes YouTube comment data related to natural disasters to test the effectiveness of these algorithms in identifying and classifying public sentiment as neutral, positive, or negative. The process involves collecting comment data, pre-processing the data, and applying Term-Frequency-Inverse Document Frequency (TF-IDF) weighting to prepare the data for analysis. Subsequently, the performance of both models is evaluated based on metrics such as accuracy, precision, recall, and F1 score. The results indicate that while both algorithms have their strengths and weaknesses, SVM tends to show better performance in sentiment classification, especially in terms of accuracy and precision, with an accuracy result of 92% and precision of 89% for negative predictions and 94% for positive predictions. On the other hand, Naive Bayes only achieved an accuracy of 79% and a precision of 91% for negative predictions and 73% for positive predictions. This study provides significant insights into the application of machine learning algorithms in sentiment analysis.


Sentiment Analysis; Naive Bayes; Support Vector Machine; Youtube; Disaster

Full Text:


Article Metrics

Abstract view : 233 times
PDF - 108 times


W. Maharani, Sentiment Analysis during Jakarta Flood for Emergency Responses and Situational Awareness in Disaster Management using BERT, 2020 8th Int. Conf. Inf. Commun. Technol. ICoICT 2020, 2020, doi: 10.1109/ICoICT49345.2020.9166407.

S. Z. Hassan, K. Ahmad, A. Al-Fuqaha, and N. Conci, Sentiment analysis from images of natural disasters, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11752 LNCS, no. October, pp. 104113, 2019, doi: 10.1007/978-3-030-30645-8_10.

S. S. L. Dang-xuan, Social Media and Political Communication - A Social Media Analytics Social media and political communication : a social media analytics framework, no. January, 2014, doi: 10.1007/s13278-012-0079-3.

A. Tripathy, A. Agrawal, and S. K. Rath, Classification of Sentiment Reviews using N-gram Machine Learning Approach Classification of sentiment reviews using n-gram machine learning approach, Expert Syst. Appl., vol. 57, no. March, pp. 117126, 2016, doi: 10.1016/j.eswa.2016.03.028.

Herianto, Penerapan Text-Mining Untuk Mengidentifikasi, vol. VIII, no. 2, pp. 3644, 2019.

B. Pranata and Susanti, Support Vector Machine untuk Sentiment Analysis Bakal Calon Presiden Republik Indonesia 2024, Indones. J. Comput. Sci., vol. 12, no. 3, pp. 13351349, 2023, doi: 10.33022/ijcs.v12i3.3231.

P. Aditiya, U. Enri, and I. Maulana, Analisis Sentimen Ulasan Pengguna Aplikasi Myim3 Pada Situs Google Play Menggunakan Support Vector Machine, vol. 9, no. 4, pp. 10201028, 2022, doi: 10.30865/jurikom.v9i4.4673.

C. Rahmawati and P. Sukmasetya, Sentimen Analisis Opini Masyarakat Terhadap Kebijakan Kominfo atas Pemblokiran Situs non-PSE pada Media Sosial Twitter, vol. 9, no. 5, pp. 13931400, 2022, doi: 10.30865/jurikom.v9i5.4950.

R. Rachman and R. N. Handayani, Klasifikasi Algoritma Naive Bayes Dalam Memprediksi Tingkat Kelancaran Pembayaran Sewa Teras UMKM, J. Inform., vol. 8, no. 2, pp. 111122, 2021, doi: 10.31294/ji.v8i2.10494.

R. Johnson, Effective Use of Word Order for Text Categorization with Convolutional Neural Networks, pp. 103112, 2015.

I. F. Yuliati, S. Wulandary, and P. R. Sihombing, Penerapan Metode Support Vector Machine (SVM) dan Backpropagation Neural Network (BPNN) dalam Pengklasifikasian Pasangan Usia Subur di Jawa Barat, J. Stat. dan Apl., vol. 4, no. 1, pp. 2334, 2020.

R. Syahputra, G. J. Yanris, and D. Irmayani, SVM and Nave Bayes Algorithm Comparison for User Sentiment Analysis on Twitter, vol. 7, no. 2, pp. 671678, 2022.

M. A. Z. Larasati, N. A. S. Winarsih, M. S. Rohman, and G. W. Saraswati, Penerapan Metode K-Means Clustering Dalam Menganalisis Sentimen Masyarakat Terhadap K-Popers Pada Twitter, Progresif J. Ilm. Komput., vol. 18, no. 2, p. 201, 2022, doi: 10.35889/progresif.v18i2.877.

R. Singh and A. Tiwari, Youtube Comments Sentiment Analysis, Int. J. Sci. Res. Eng. Manag. (IJSREM, no. May, p. 5, 2021, [Online]. Available:

M. Z. Asghar, S. Ahmad, A. Marwat, and F. M. Kundi, Sentiment Analysis on YouTube: A Brief Survey, no. September, 2015, [Online]. Available:

D. T. Nguyen et al., Robust Classification of Crisis-Related Data on Social Networks Using Convolutional Neural Networks, no. Icwsm, pp. 632635, 2017.

H. Henderi, Preprocessing data untuk sistem peramalan tingkat kedisiplinan mahasiswa, no. May, 2020, doi: 10.33050/icit.v3i2.70.

J. Nasional, S. Informasi, E. Yudi, and R. Wicaksana, Analisis Sentimen Twitter untuk Menilai Opini Terhadap Perusahaan Publik Menggunakan Algoritma Deep Neural Network, vol. 02, pp. 108118, 2021.

J. Sayadi, L. Wikarsa, M. Comp, T. Suwanto, and S. Kom, Search Engine Twitter Terhadap Isu Politik Menggunakan Metode TF-IDF dan Search Engine Twitter Terhadap Isu Politik Menggunakan Metode TF - IDF dan Vector Space Model, no. August 2016, 2018.

D. P. Fajrina, N. Amalita, and A. Salma, Sentiment Analysis of TikTok Application on Twitter using The Nave Bayes Classifier Algorithm, vol. 1, pp. 392398, 2023.

M. R. Nurhusen, J. Indra, and K. A. Baihaqi, Analisis Sentimen Pengguna Twitter Terhadap Kenaikan Harga Bahan Bakar Minyak ( BBM ) Menggunakan Metode Logistic Regression, vol. 7, pp. 276282, 2023, doi: 10.30865/mib.v7i1.5491.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Perbandingan Efektivitas Na´ve Bayes dan SVM dalam Menganalisis Sentimen Kebencanaan di Youtube


  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

STMIK Budi Darma
Secretariat: Sisingamangaraja No. 338 Telp 061-7875998

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.