A Comparison Support Vector Machine, Logistic Regression And Naïve Bayes For Classification Sentimen Analisys user Mobile App

(1) * Kiki Ahmad Baihaqi Mail (Universitas Kristen Satya Wacana, Indonesia)
(2) Iwan Setyawan Mail (Universitas Kristen Satya Wacana, Indonesia)
(3) Danny Manongga Mail (Universitas Kristen Satya Wacana, Indonesia)
(4) Hendryanto Dwi Purnomo Mail (Universitas Kristen Satya Wacana, Indonesia)
(5) Hendry Hendry Mail (Universitas Kristen Satya Wacana, Indonesia)
(6) Ahmad Fauzi Mail (Universitas Buana Perjuangan Karawang, Indonesia)
(7) Aprilia Hananto Mail (Universitas Buana Perjuangan Karawang, Indonesia)
*corresponding author


Data is the most important thing, the use of data can be useful to get an evaluation from the user of a system or application that is built based on mobile. Not only, the assessment or acceptance results of mobile applications during the trial stage are considered important, assessments and comments from direct users are also important things that can be input for mobile application developers. Data mining, or known in English as data mining, is the answer to the process of retrieving data on any media. In this research, data mining is carried out on the media mobile application download service provider Google Playstore, which provides data in the form of comments and ratings. After scraping the data and obtaining the latest data parameters determined by the latest 2000 comments, the data is pre-processed by removing the emot icon character and eliminating unneeded variables so that the data obtained can be processed to the next stage, namely classification based on ratings and sentiment comments. The algorithms used or compared in this research are Support Vector machine, logistic regression and naïve bayes which are known to be reliable in data mining processing. In this research, the accuracy results are 88% for SVM, 90.5% for Logistic Regression and 91% for naïve bayes.


Data Mining Scraping Naïve Bayes Support Verctor Machine Logistic Regression




Article metrics

10.29099/ijair.v7i1.962 Abstract views : 308 | PDF views : 67




Full Text



T. Li et al., “Smartphone App Usage Analysis: Datasets, Methods, and Applications,” IEEE Communications Surveys and Tutorials, vol. 24, no. 2, pp. 937–966, 2022, doi: 10.1109/COMST.2022.3163176.

M. Aniche, E. Maziero, R. Durelli, and V. H. S. Durelli, “The Effectiveness of Supervised Machine Learning Algorithms in Predicting Software Refactoring,” IEEE Transactions on Software Engineering, vol. 48, no. 4, pp. 1432–1450, Apr. 2022, doi: 10.1109/TSE.2020.3021736.

M. Raza, N. D. Jayasinghe, and M. M. A. Muslam, “A Comprehensive Review on Email Spam Classification using Machine Learning Algorithms,” in International Conference on Information Networking, IEEE Computer Society, Jan. 2021, pp. 327–332. doi: 10.1109/ICOIN50884.2021.9334020.

A. H. Espejel and F. J. Cantu-Ortiz, “Data Mining Techniques to Build A Recommender System,” in Proceedings - 2021 International Symposium on Computer Science and Intelligent Controls, ISCSIC 2021, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 217–221. doi: 10.1109/ISCSIC54682.2021.00047.

P. Pierleoni, L. Palma, A. Belli, S. Raggiunto, and L. Sabbatini, “Supervised Regression Learning for Maintenance-related Data,” in 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), IEEE, Sep. 2022, pp. 1–6. doi: 10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927904.

T. T. Chikohora and E. Chikohora, “An Algorithm for Selecting a Data Mining Technique,” in 2021 3rd International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2021, Institute of Electrical and Electronics Engineers Inc., 2021. doi: 10.1109/IMITEC52926.2021.9714525.

RVS Technical Campus, IEEE Aerospace and Electronic Systems Society, and Institute of Electrical and Electronics Engineers, “Data Analysis by Web Scraping using Python,” Proceedings of the Third International Conference on Electronics Communication and Aerospace Technology [ICECA 2019], 2019.

N. Narayani, P. Kumar, and D. Kumar, “Web Scraping & Automation Bot Using Python : Using Python to automate all the tasks,” in 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), IEEE, Dec. 2022, pp. 1343–1346. doi: 10.1109/ICAC3N56670.2022.10074375.

V. Desai and D. H A, “A Hybrid Approach to Data Pre-processing Methods,” in 2020 IEEE International Conference for Innovation in Technology (INOCON), IEEE, Nov. 2020, pp. 1–4. doi: 10.1109/INOCON50539.2020.9298378.

H. S. Obaid, S. A. Dheyab, and S. S. Sabry, “The Impact of Data Pre-Processing Techniques and Dimensionality Reduction on the Accuracy of Machine Learning,” in 2019 9th Annual Information Technology, Electromechanical Engineering and Microelectronics Conference (IEMECON), IEEE, Mar. 2019, pp. 279–283. doi: 10.1109/IEMECONX.2019.8877011.

E. U. Chye, E. I. Glinkin, and A. V. Levenets, “Measurement Data Classification in Information and Measuring Systems,” in 2019 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), IEEE, Mar. 2019, pp. 1–5. doi: 10.1109/ICIEAM.2019.8742926.

M. Zheng, “The Classification and Classification of Big Data Based on the Internet of Things,” in 2022 IEEE 2nd International Conference on Mobile Networks and Wireless Communications (ICMNWC), IEEE, Dec. 2022, pp. 1–5. doi: 10.1109/ICMNWC56175.2022.10031772.

Z. Aung, I. S. Mihailov, and Y. T. Aung, “Models and Data Mining Algorithms for Solving Classification Problems,” in 2019 1st International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA), IEEE, Nov. 2019, pp. 532–536. doi: 10.1109/SUMMA48161.2019.8947555.

X. Zou, Y. Hu, Z. Tian, and K. Shen, “Logistic Regression Model Optimization and Case Analysis,” in 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT), IEEE, Oct. 2019, pp. 135–139. doi: 10.1109/ICCSNT47585.2019.8962457.

I. C. Juanatas and R. A. Juanatas, “Predictive Data Analytics using Logistic Regression for Licensure Examination Performance,” in 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), IEEE, Dec. 2019, pp. 251–255. doi: 10.1109/ICCIKE47802.2019.9004386.

A. Tariq et al., “Modelling, mapping and monitoring of forest cover changes, using support vector machine, kernel logistic regression and naive bayes tree models with optical remote sensing data,” Heliyon, vol. 9, no. 2, Feb. 2023, doi: 10.1016/j.heliyon.2023.e13212.

W. D. Herlambang, K. A. Laksitowening, and I. Asror, “Prediction of Graduation with Naïve Bayes Algorithm and Principal Component Analysis (PCA) on Time Series Data,” in 2021 9th International Conference on Information and Communication Technology (ICoICT), IEEE, Aug. 2021, pp. 645–649. doi: 10.1109/ICoICT52021.2021.9527443.

M. C. Kirana, M. Fani, T. S. Kartikasari, and M. Nashrullah, “Downtime Data Classification Using Naïve Bayes Algorithm on 2008 ESEC Engine,” in 2020 3rd International Conference on Applied Engineering (ICAE), IEEE, Oct. 2020, pp. 1–6. doi: 10.1109/ICAE50557.2020.9350377.

J. Huang, J. Zhou, and L. Zheng, “Support Vector Machine Classification Algorithm Based on Relief-F Feature Weighting,” in 2020 International Conference on Computer Engineering and Application (ICCEA), IEEE, Mar. 2020, pp. 547–553. doi: 10.1109/ICCEA50009.2020.00121.

D. van Herwerden, J. W. O’Brien, P. M. Choi, K. V. Thomas, P. J. Schoenmakers, and S. Samanipour, “Naive Bayes classification model for isotopologue detection in LC-HRMS data,” Chemometrics and Intelligent Laboratory Systems, vol. 223, Apr. 2022, doi: 10.1016/j.chemolab.2022.104515.

A. Rojas and G. J. Dolecek, “Evaluation of Supervised Machine Learning Classification Algorithms for Fingerprint Recognition,” in 2021 Global Congress on Electrical Engineering (GC-ElecEng), IEEE, Dec. 2021, pp. 1–4. doi: 10.1109/GC-ElecEng52322.2021.9788164.

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


International Journal Of Artificial Intelligence Research

Organized by: Departemen Teknik Informatika
Published by: STMIK Dharma Wacana
Jl. Kenanga No.03 Mulyojati 16C Metro Barat Kota Metro Lampung
phone. +62725-7850671
Fax. +62725-7850671

Email: jurnal.ijair@gmail.com

View IJAIR Statcounter

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