Analisis Perbandingan Sentimen Pengguna Twitter Terhadap Layanan Salah Satu Provider Internet Di Indonesia Menggunakan Metode Klasifikasi

Della Puspita Sari; Budiman; Nur Alamsyah

Della Puspita Sari Universitas Informatika dan Bisnis Indonesia
Budiman Universitas Informatika dan Bisnis Indonesia
Nur Alamsyah Universitas Informatika dan Bisnis Indonesia

Keywords: smote, support vector machine, naïve bayes, random forest, decision tree

Abstract

The Internet is needed for everyday life, whereas in Indonesia there are many internet service providers, one of which is indihome. Sentiment analysis itself aims to classify a text into Negative, Positive and Neutral classes. On the twitter platform, there are many reviews about internet providers, one of which is indihome, because of poor service or just to appreciate the services provided. Based on the calculation of the results obtained 71.1% negative, 21.1% positive and 7.7% neutral. The data obtained is not balanced, therefore the classification process is assisted using Smote. The results of the comparison of the four methods used are Support Vector Machine, Naïve Bayes, Random forest, Decision tree. From the overall comparison, the highest accuracy without smote or using smote is Support Vector Machine with an accuracy level of 89% AUC level of 89% if using smote gets 93% accuracy and 97% AUC level with 80% training data and 20% testing.

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References

P. R. Utami, “ANALISIS PERBANDINGAN QUALITY OF SERVICE JARINGAN INTERNET BERBASIS WIRELESS PADA LAYANAN INTERNET SERVICE PROVIDER (ISP) INDIHOME DAN FIRST MEDIA,” vol. 25, no. 2, 2020.

S. Velichety, “Quantifying the impacts of online fake news on the equity value of social media platforms – Evidence from Twitter,” Int. J. Inf. Manag., 2022.

A. Diana and M. A. P. Kurniawan, “Decision Support System For Selection Of Internet Service Provider (ISP) With Analytical Hierarchy Process (AHP) And Simple Additive Weighting (SAW) Methods,” vol. 4, no. 2, 2022.

A. Puspasari et al., “The Effect Of Service Quality Perception And Company Image On Customer Satisfaction And Their Impact On Customer Loyalty Indihome,” vol. 03, no. 02, 2022.

S. F. Pane, R. Prastya, A. G. Putrada, N. Alamsyah, and M. N. Fauzan, “Reevaluating Synthesizing Sentiment Analysis on COVID-19 Fake News Detection using Spark Dataframe,” in 2022 International Conference on Information Technology Systems and Innovation (ICITSI), 2022, pp. 269–274. doi: 10.1109/ICITSI56531.2022.9970773.

S. H. Suarsa, A. D. Anggraeni, and R. F. Aritonang, “IndiHome Customer Loyalty in Bandung: Service Quality and Price,” Bus. Manag. Res., vol. 220.

D. R. Kusuma, S. Syofian, and L. N. Afifa, “ANALISIS SENTIMEN TANGGAPAN PELANGGAN INDIHOME DI PLATFORM SOSIAL MEDIA FACEBOOK DAN TWITTER MENGGUNAKAN SUPPORT VECTOR MESIN DAN PENDEKATAN KLASIFIKASI NAÏVE BAYES (STUDI KASUS: PT. TELKOM INDONESIA),” no. 1, 2023.

A. G. Putrada, N. Alamsyah, S. F. Pane, and M. Nurkamal Fauzan, “Feature Importance on Text Analysis for a Novel Indonesian Movie Recommender System,” in 2023 11th International Conference on Information and Communication Technology (ICoICT), 2023, pp. 34–39. doi: 10.1109/ICoICT58202.2023.10262504.

N. Alamsyah, Saparudin, and A. P. Kurniati, “A Novel Airfare Dataset To Predict Travel Agent Profits Based On Dynamic Pricing,” in 2023 11th International Conference on Information and Communication Technology (ICoICT), 2023, pp. 575–581. doi: 10.1109/ICoICT58202.2023.10262694.

S. Winduwati, “Pemanfaatan Media Sosial Instagram Pada Online Shop @ivoree.id dalam Memasarkan Produk,” vol. 5, no. 1, 2021.

S. F. Pane, A. G. Putrada, N. Alamsyah, and M. N. Fauzan, “A PSO-GBR Solution for Association Rule Optimization on Supermarket Sales,” in 2022 Seventh International Conference on Informatics and Computing (ICIC), 2022, pp. 1–6. doi: 10.1109/ICIC56845.2022.10007001.

I. T. Julianto, D. Kurniadi, M. R. Nashrulloh, and A. Mulyani, “TWITTER SOCIAL MEDIA SENTIMENT ANALYSIS AGAINST BITCOIN CRYPTOCURRENCY TRENDS USING RAPIDMINER,” J. Tek. Inform. Jutif, vol. 3, no. 5, pp. 1183–1187, Oct. 2022, doi: 10.20884/1.jutif.2022.3.5.289.

A. S. Alhassun and M. A. Rassam, “A Combined Text-Based and Metadata-Based Deep-Learning Framework for the Detection of Spam Accounts on the Social Media Platform Twitter,” Processes, vol. 10, no. 3, p. 439, Feb. 2022, doi: 10.3390/pr10030439.

A. Halim and Andri Safuwan, “ANALISIS SENTIMEN OPINI WARGANET TWITTER TERHADAP TES SCREENING GENOSE PENDETEKSI VIRUS COVID-19 MENGGUNAKAN METODE NAÏVE BAYES BERBASIS PARTICLE SWARM OPTIMIZATION,” J. Inform. Teknol. Dan Sains, vol. 5, no. 1, pp. 170–178, Feb. 2023, doi: 10.51401/jinteks.v5i1.2229.

R. N. Aziza, T. S. Ardanti, E. Yosrita, and R. F. Ningrum, “Pembangunan Aplikasi Chatbot Informasi Akademik berbasis Cosine Similarity dan Library Sastrawi Stemmer (Studi Kasus: Teknik Informatika IT PLN),” vol. 3, 2022.

S. Akuma, T. Lubem, and I. T. Adom, “Comparing Bag of Words and TF-IDF with different models for hate speech detection from live tweets,” Int. J. Inf. Technol., vol. 14, no. 7, pp. 3629–3635, Dec. 2022, doi: 10.1007/s41870-022-01096-4.

T. Pronk, D. Molenaar, R. W. Wiers, and J. Murre, “Methods to split cognitive task data for estimating split-half reliability: A comprehensive review and systematic assessment,” Psychon. Bull. Rev., vol. 29, no. 1, pp. 44–54, Feb. 2022, doi: 10.3758/s13423-021-01948-3.

R. Fahlapi et al., “ANALISA SENTIMEN VAKSINASI COVID-19 DENGAN METODE SUPPORT VECTOR MACHINE DAN NAÏVE BAYES BERBASIS TEKNIK SMOTE,” J. Inform. Kaputama JIK, vol. 6, no. 1, pp. 57–63, Jan. 2022, doi: 10.59697/jik.v6i1.136.

S. S. Bagui, D. Mink, S. C. Bagui, and S. Subramaniam, “Determining Resampling Ratios Using BSMOTE and SVM-SMOTE for Identifying Rare Attacks in Imbalanced Cybersecurity Data,” Computers, vol. 12, no. 10, p. 204, Oct. 2023, doi: 10.3390/computers12100204.

D. A. Vonega, A. Fadila, and D. E. Kurniawan, “Analisis Sentimen Twitter Terhadap Opini Publik Atas Isu Pencalonan Puan Maharani dalam PILPRES 2024,” J. Appl. Inform. Comput., vol. 6, no. 2, pp. 129–135, Nov. 2022, doi: 10.30871/jaic.v6i2.4300.