Twitter Sentiment Classification towards Telecommunication Provider Users in Indonesia

Authors

  • Fernanda Mulya Syah Putra Teknik Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
  • Sindhu Rakasiwi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro
  • Noval Ariyanto Teknik Informatika, Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

DOI:

https://doi.org/10.30871/jaic.v9i2.9143

Keywords:

Internet, Naive Bayes, Sentimen, Support Vector Machine, Twitter

Abstract

Internet services have become essential for communication and information sharing. Nowadays, daily activities are conducted through the internet. This study aims to gain a better understanding of the components that influence user perception and satisfaction using textual, sentiment, and statistical analysis techniques. By applying machine learning algorithms such as Naïve Bayes and Support Vector Machine (SVM), this research analyzes customer perceptions of telecommunication service providers in Indonesia. The dataset consists of 300 tweets obtained from the Kaggle platform. The objective is to identify elements that affect customer satisfaction, particularly those related to network stability and service quality. Data preprocessing is carried out using methods such as case folding, normalization, stemming, and stopword removal to enhance sentiment analysis model performance. The results show that SVM outperforms Naïve Bayes in precision and recall, achieving an accuracy of 90% compared to Naïve Bayes' 87%. This demonstrates SVM's ability to classify positive and negative sentiments more accurately. Common topics found in the analysis include customer satisfaction with network stability and affordable pricing, while dissatisfaction arises from poor connectivity and slow customer service response. These findings provide valuable insights for service providers to improve service quality and enhance customer satisfaction. Real-time sentiment analysis using machine learning has great potential, and this study highlights how telecommunication companies can leverage strategic recommendations to improve service quality and retain customers.

Downloads

Download data is not yet available.

References

[1] Z. Wang, Y. Fan, H. Lv, S. Deng, H. Xie, L. Zhanget al., "The gap between self-rated health information literacy and internet health information-seeking ability for patients with chronic diseases in rural communities: cross-sectional study", Journal of Medical Internet Research, vol. 24, no. 1, p. e26308, 2022. https://doi.org/10.2196/26308

[2] J. Akanni, И. Г.А., R. Alao, & C. Thomas, "Assessment of internet service provided using umts operators at the university of ilorin main campus", Nigerian Journal of Technology, vol. 39, no. 2, p. 500-505, 2020. https://doi.org/10.4314/njt.v39i2.20

[3] D. Cahyati and R. Nurlinda, "Evaluasi kesuksesan sistem informasi aplikasi mobile collection dengan pendekatan model delone dan mclean", Jurnal Doktor Manajemen (JDM), vol. 6, no. 2, p. 149, 2023. https://doi.org/10.22441/jdm.v6i2.20248

[4] D. Ginting, F. Sutrisno, E. Yudhistyra, R. Astuti, & H. Hartanto, "Analisis faktor-faktor yang mempengaruhi kepuasan pengguna serta dampaknya terhadap loyalitas pengguna aplikasi mybca", Media Informatika, vol. 22, no. 3, p. 147-159, 2024. https://doi.org/10.37595/mediainfo.v22i3.205

[5] I. Fitriyani and C. Hendriyani, "Implementasi customer data management dalam meningkatkan retensi pelanggan indihome di pt telkom", ICIT Journal, vol. 7, no. 2, p. 168-176, 2021. https://doi.org/10.33050/icit.v7i2.1645

[6] S. Saleh and S. Saha, "Customer retention and churn prediction in the telecommunication industry: a case study on a danish university", SN Applied Sciences, vol. 5, no. 7, 2023. https://doi.org/10.1007/s42452-023-05389-6

[7] M. Khalik, W. Mohammad, Z. Zilfana, & O. Themba, "The influence of service personalization, customer satisfaction, and customer retention in the telecommunications industry on data-driven marketing", West Science Information System and Technology, vol. 1, no. 02, p. 55-62, 2023. https://doi.org/10.58812/wsist.v1i02.476

[8] S. Saleh and S. Saha, "Customer retention and churn prediction in the telecommunication industry: a case study on a danish university", SN Applied Sciences, vol. 5, no. 7, 2023. https://doi.org/10.1007/s42452-023-05389-6

[9] M. M. Ulkhaq and M. P. Br. Barus, “Analisis Kepuasan Pelanggan dengan Menggunakan SERVQUAL: Studi Kasus Layanan IndiHome PT. Telekomunikasi Indonesia, Tbk, Regional 1 Sumatera”, j. sist. manaj. ind., vol. 1, no. 2, pp. 61–67, Dec. 2017, https://doi.org/10.30656/jsmi.v1i2.365

[10] N. Ariyanto, H. A. Azies, and M. Akrom, Trans., “Ensemble Stacking of Machine Learning Approach for Predicting Corrosion Inhibitor Performance of Pyridazine Compounds”, International Journal of Advances in Data and Information Systems, vol. 5, no. 2, pp. 198–215, Nov. 2024, https://doi.org/10.59395/ijadis.v5i2.1346

[11] E. Kaya, X. Dong, Y. Suhara, S. Balcısoy, B. Bozkaya, & A. Pentland, "Behavioral attributes and financial churn prediction", EPJ Data Science, vol. 7, no. 1, 2018. https://doi.org/10.1140/epjds/s13688-018-0165-5

[12] N. Rezki, S. Thamrin, & S. Siswanto, "Sentiment analysis of merdeka belajar kampus merdeka policy using support vector machine with word2vec", BAREKENG: Jurnal Ilmu Matematika Dan Terapan, vol. 17, no. 1, p. 0481-0486, 2023. https://doi.org/10.30598/barekengvol17iss1pp0481-0486

[13] J. Munggaran, A. Alhafidz, M. Taqy, D. Agustini, & M. Munawir, "Sentiment analysis of twitter users’ opinion data regarding the use of chatgpt in education", Journal of Computer Engineering, Electronics and Information Technology, vol. 2, no. 2, p. 75-88, 2023. https://doi.org/10.17509/coelite.v2i2.59645

[14] M. Ahmad, S. Aftab, & I. Ali, "Sentiment analysis of tweets using svm", International Journal of Computer Applications, vol. 177, no. 5, p. 25-29, 2017. https://doi.org/10.5120/ijca2017915758

[15] H. Jadia, "Comparative analysis of sentiment analysis techniques: svm, logistic regression, and tf-idf feature extraction", International Research Journal of Modernization in Engineering Technology and Science, 2023. https://doi.org/10.56726/irjmets45265

[16] R. Setiabudi, N. Iswari, & A. Rusli, "Enhancing text classification performance by preprocessing misspelled words in indonesian language", TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 19, no. 4, p. 1234, 2021. https://doi.org/10.12928/telkomnika.v19i4.20369

[17] A. Indriani and M. Muslim, "Svm optimization based on pso and adaboost to increasing accuracy of ckd diagnosis", Lontar Komputer: Jurnal Ilmiah Teknologi Informasi, p. 119, 2019. https://doi.org/10.24843/lkjiti.2019.v10.i02.p06

[18] S. Rajabana, S. Sukri, & M. Munawaroh, "Penerapan metode pembelajaran mesin berbasis fuzzy logic untuk prediksi kualitas layanan jaringan iot (internet of things)", Jurnal Informatika Universitas Pamulang, vol. 8, no. 2, p. 270-278, 2023. https://doi.org/10.32493/informatika.v8i2.30572

Downloads

Published

2025-03-17

How to Cite

[1]
F. M. Syah Putra, S. Rakasiwi, and N. Ariyanto, “Twitter Sentiment Classification towards Telecommunication Provider Users in Indonesia”, JAIC, vol. 9, no. 2, pp. 314–321, Mar. 2025.

Issue

Section

Articles

Most read articles by the same author(s)

Similar Articles

1 2 3 4 5 > >> 

You may also start an advanced similarity search for this article.