PERBANDINGAN KINERJA ALGORITMA NAIVE BAYES DAN SUPPORT VECTOR MACHINES DALAM KLASIFIKASI ULASAN RESTORAN

Article Sidebar

PDF
Published: Apr 20, 2023
Keywords:
Performance Comparison, Restaurant Reviews, Naive Bayes, Support Vector Machine
Dimensions
Altmetrics
Statistics
Read Counter : 13
Download : 8
Crossmark/ Data Version

Main Article Content

Vaness Chen

Abstract

The aim of this research is to compare the performance between the two algorithms, naive Bayes and support vector machine (SVM), in the context of this research, what is discussed is the classification of restaurant reviews. These two algorithms are fairly popular choices in the field of machine learning because of their ability to classify and predict data accurately. The dataset in this research was obtained from the Kaggle website, where positive reviews were given a value of 1 and negative reviews were given a value of 0. Through this experimental experiment, the algorithm that obtained the greatest classification accuracy was Support Vector Machine at 77.89%, while Naive Bayes only obtained a classification accuracy of 72.86%. This comparative classification analysis includes evaluation of metrics such as accuracy, precision, recall, f1-score, and support. It is hoped that the results of this research can provide valuable insights for the selection of both classification algorithms for the task of restaurant review classification.

Article Details

Issue
Vol. 18 No. 1 (2023): Jurnal Komputer dan Informatika
Section
Articles
Creative Commons License

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

Menawarkan akses terbuka

References

[1] Retno Sari, “Analisis Sentimen Review Restoran Menggunakan Algoritma Naïve Bayes Berbasis Particle Swarm Optimization”, Jurnal Informatika, vol. 6, no. 1, Apr 2019, pp. 23-28.

[2] K N. Reddy, Dr. B. I. Reddy, “Restaurant Review Classification Using Naïve Bayes Model”, Journal of University of Shanghai for Science and Technology, vol. 23, no. 8, Aug 2021, pp. 646-656.

[3] L. Gunawan, M. S. Anggreainy, L. Wihan, Santy, G. Y. Lesmana, and S. Yusuf, “Support vector machine based emotional analysis of restaurant reviews”, Procedia Computer Science, vol. 216, pp. 479–484, Jan. 2023.

[4] B. S. Amalia, Y. Umaidah, and R. Mayasari, “Analisis Sentimen Review Pelanggan Restoran Menggunakan Algoritma Support Vector Machine Dan K-Nearest Neighbor,” SITEKIN: Jurnal Sains, Teknologi dan Industri, vol. 19, no. 1, pp. 28–34, Nov. 2021.

[5] Y. Julianto, D. H. Setiabudi, and S. Rostianingsih, “Analisis Sentimen Ulasan Restoran Menggunakan Metode Support Vector Machine,” Jurnal Infra, vol. 10, no. 1, pp. 1–7, Jan. 2022

[6] M. Adnan, R. Sarno and K. R. Sungkono, "Sentiment Analysis of Restaurant Review with Classification Approach in the Decision Tree-J48 Algorithm," 2019 International Seminar on Application for Technology of Information and Communication (iSemantic), Semarang, Indonesia, 2019, pp. 121-126.

[7] R. A. Laksono, K. R. Sungkono, R. Sarno and C. S. Wahyuni, "Sentiment Analysis of Restaurant Customer Reviews on TripAdvisor using Naïve Bayes," 2019 12th International Conference on Information & Communication Technology and System (ICTS), Surabaya, Indonesia, 2019, pp. 49-54.

[8] W. Parasati, F. A. Bachtiar, dan N. Y. Setiawan, “Analisis Sentimen Berbasis Aspek pada Ulasan Pelanggan Restoran Bakso President Malang dengan Metode Naive Bayes Classifier”, J-PTIIK, vol. 4, no. 4, pp. 1090–1099, Jun 2020.

[9] M. M. Hamad, M. A. Salih, and R. A. Jaleel, “Sentiment Analysis of Restaurant Reviews in Social Media using Naïve Bayes,” Applications of Modelling and Simulation, vol. 5, no. 0, pp. 166–172, Aug 2021,

[10] A. Krishna, V. Akhilesh, A. Aich, and C. Hegde, “Sentiment Analysis of Restaurant Reviews Using Machine Learning Techniques,” Lecture Notes in Electrical Engineering, pp. 687–696, Apr 2019

[11] M. Govindarajan, “Sentiment Analysis of Restaurant Reviews Using Hybrid Classification Method,” 2014.

[12] K. Zahoor, N. Z. Bawany and S. Hamid, "Sentiment Analysis and Classification of Restaurant Reviews using Machine Learning," 2020 21st International Arab Conference on Information Technology (ACIT), Giza, Egypt, 2020, pp. 1-6.

[13] J. C. Sugitomo, N. Kevin, N. Jannatri and D. Suhartono, "Sentiment Analysis using SVM and Naïve Bayes Classifiers on Restaurant Review Dataset," 2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI), Jakarta, Indonesia, 2021, pp. 100-108.

[14] D. Kumar, Gopesh, A. Choubey, and P. Singh, “Restaurant Review Classification and Analysis”, Journal of Engineering Sciences, vol. 11, pp. 169-179. Aug 2020.

[15] P. Cheewaprakobkit, “Comparing SVM and Naïve Bayes Classifi ers for Online Reviews Based on Aspect-Based Sentiment Analysis,” vol. 7, no. 1, Jun 2018.

[16] L. Z. Nihayatin and S. Sutikno, “Perbandingan Uji Homogenitas Runtun Data Curah Hujan Sebagai Pra-Pemrosesan Kajian Perubahan Iklim,” Jurnal Sains dan Seni ITS, vol. 2, no. 2, pp. D255–D259, Sep. 2013

[17] F. -J. Yang, "An Implementation of Naive Bayes Classifier", 2018 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 2018, pp. 301-306

[18] Q. Wang, "Support Vector Machine Algorithm in Machine Learning", 2022 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China, 2022, pp. 750-756

[19] P. Bafna, D. Pramod and A. Vaidya, "Document clustering: TF-IDF approach", 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, India, 2016, pp. 61-66

[20] R. Yacouby and D. Axman, “Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models,” ACLWeb, pp. 79-91, Nov. 01, 2020.

[21] Xue Ying, “An Overview of Overfitting and its Solutions”, IOP Publishing Ltd, pp. 1-6, China, 2019.