Published: 2023-01-01

Analisis Sentimen Complain dan Bukan Complain pada Twitter Telkomsel dengan SMOTE dan Naïve Bayes

DOI: 10.35870/jtik.v7i1.691

Budi Kurniawan, Achmad Suwarisman, Iis Afriyanti, Aditya Wahyudi, Dedi Dwi Saputra
  • Budi Kurniawan: Universitas Nusamandiri
  • Achmad Suwarisman: Universitas Nusamandiri
  • Iis Afriyanti: Universitas Nusamandiri
  • Aditya Wahyudi: Universitas Nusamandiri
  • Dedi Dwi Saputra: Universitas Nusamandiri
Issue Cover
Article Metrics
Share:

Abstract

This analysis aims to find out the public sentiment towards Telkomsel posted on Indonesian twitter, which makes market research on public opinion very useful. The dataset was taken from Twitter social media in a query Indonesian by crawling method using the RapidMiner application and the result of crawling the data set there were 1000 tweets with sentiment complaints and not complaints. Therefore, from 1000 tweets, preprocessing will be carried out with the SMOTE Upsampling and Naivebayes methods as well as several filtering such as transform case, tokenize, tokenize (by length) stemming filters and stopwords so that the data can stay in words and there is a balance in the sentiment on the dataset. It can be concluded that in the classification of sentiment there is a balance between complaints and non-complaints as many as 581. Where the accuracy rating level is 81.58%, the precision assessment is 86.82% and the recall assessment is 74.87 and the resulting AUC is 0.803.

Keywords

Public Sentiment; Naïve Bayes; SMOTE; Twitter; Text Mining

Peer Review Process

This article has undergone a double-blind peer review process to ensure quality and impartiality.

Indexing Information

Discover where this journal is indexed at our indexing page.

Open Science Badges

This journal supports transparency in research and encourages authors to meet criteria for Open Science Badges.

How to Cite

Kurniawan, B., Suwarisman, A., Afriyanti, I., Wahyudi, A., & Saputra, D. D. (2023). Analisis Sentimen Complain dan Bukan Complain pada Twitter Telkomsel dengan SMOTE dan Naïve Bayes. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 7(1), 106-113. https://doi.org/10.35870/jtik.v7i1.691
Article Information

This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.

  • Issue: Vol. 7 No. 1 (2023)

  • Section: Computer & Communication Science

  • Published: 2023-01-01

AI Research Hub

This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem.

Semantic Scholar Scite Dimensions Connected Papers
Scholarly Connection Platforms
Dimensions Dimensions Connected Papers Connected Papers Scite Scite Google Scholar Google Scholar Semantic Scholar Semantic Scholar Garuda Garuda Scilit Scilit Crossref Crossref BASE BASE Zenodo Zenodo Unpaywall Unpaywall OpenCitations OpenCitations
Author Biographies
Budi Kurniawan, , Universitas Nusamandiri
Google Scholar ResearchGate ORCID

Program Studi Sistem Informasi, Fakultas Teknologi Informasi, Universitas Nusamandiri

Achmad Suwarisman, , Universitas Nusamandiri
Google Scholar ResearchGate ORCID

Program Studi Sistem Informasi, Fakultas Teknologi Informasi, Universitas Nusamandiri

Iis Afriyanti, , Universitas Nusamandiri
Google Scholar ResearchGate ORCID

Program Studi Sistem Informasi, Fakultas Teknologi Informasi, Universitas Nusamandiri

Aditya Wahyudi, , Universitas Nusamandiri
Google Scholar ResearchGate ORCID

Program Studi Sistem Informasi, Fakultas Teknologi Informasi, Universitas Nusamandiri

Dedi Dwi Saputra, , Universitas Nusamandiri
Google Scholar ResearchGate ORCID

Program Studi Sistem Informasi, Fakultas Teknologi Informasi, Universitas Nusamandiri

References
  • Suryono, S., Utami, E. and Luthfi, E.T., 2018. Analisis Sentiment Pada Twitter Dengan Menggunakan Metode Naïve Bayes Classifier. Seminar Nasional GEOTIK 2018.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Cambria, E., Schuller, B., Xia, Y. and Havasi, C., 2013. New avenues in opinion mining and sentiment analysis. IEEE Intelligent systems, 28(2), pp.15-21. DOI: 10.1109/MIS.2013.30.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Liu, B., 2012. Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), pp.1-167.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Deepa, N., Priya, J.S. and Devi, T., 2022. Towards applying internet of things and machine learning for the risk prediction of COVID-19 in pandemic situation using Naive Bayes classifier for improving accuracy. Materials Today: Proceedings. DOI: https://doi.org/10.1016/j.matpr.2022.03.345.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Naraswati, N.P.G., Nooraeni, R., Rosmilda, D.C., Desinta, D., Khairi, F. and Damaiyanti, R., 2021. Analisis Sentimen Publik dari Twitter Tentang Kebijakan Penanganan Covid-19 di Indonesia dengan Naive Bayes Classification. Sistemasi: Jurnal Sistem Informasi, 10(1), pp.222-238. DOI: https://doi.org/10.32520/stmsi.v10i1.1179.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Sari, A. C. et al. 2019. Komunikasi dan media sosial. Reserachgate.net.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Statistic Brain. 2013. Twitter statistics. Available at: http://www.statisticbrain.com/ twitter-statistics.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Olofinlua, T. 2019. Twitter: social communication in the twitter age, Information, Communication & Society, 22(13), pp. 2037–2038. DOI: 10.1080/1369118x.2019.1620824.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Vu, D.H., 2022. Privacy-preserving Naive Bayes classification in semi-fully distributed data model. Computers & Security, 115, p.102630. DOI: https://doi.org/10.1016/j.cose.2022.102630.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Chawla, N.V., Bowyer, K.W., Hall, L.O. and Kegelmeyer, W.P., 2002. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, pp.321-357. DOI: https://doi.org/10.1613/jair.953.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Zhang, A., Yu, H., Zhou, S., Huan, Z. and Yang, X., 2022. Instance weighted SMOTE by indirectly exploring the data distribution. Knowledge-Based Systems, 249, p.108919. doi: https://doi.org/10.1016/j.knosys.2022.108919
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Anandarajan, M., Hill, C. and Nolan, T. 2019 Text Preprocessing BT - Practical Text Analytics: Maximizing the Value of Text Data, in Anandarajan, M., Hill, C., and Nolan, T. (eds). Cham: Springer International Publishing, pp. 45–59. DOI: 10.1007/978-3-319-95663-3_4.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Samuel, N. 2010. Naive Bayes Classifier dan Penggunaannya pada Klasifikasi Dokumen. Bandung: Institut Teknologi Bandung.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Bustami, B., 2013. Penerapan algoritma Naive Bayes untuk mengklasifikasi data nasabah asuransi. TECHSI-Jurnal Teknik Informatika, 5(2). DOI: 10.26555/jifo.v8i1.a2086.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Singh, M., Bhatt, M.W., Bedi, H.S. and Mishra, U., 2020. Performance of bernoulli’s naive bayes classifier in the detection of fake news. Materials Today: Proceedings. DOI: https://doi.org/10.1016/j.matpr.2020.10.896.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Keller, K.L. and Lehmann, D.R., 2006. Brands and branding: Research findings and future priorities. Marketing science, 25(6), pp.740-759. DOI: https://doi.org/10.1287/mksc.1050.0153.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Tanesab, F.I., Sembiring, I. and Purnomo, H.D., 2017. Sentiment analysis model based on Youtube comment using support vector machine. International Journal of Computer Science and Software Engineering, 6(8), p.180.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Gunawan, B., Sastypratiwi, H. and Pratama, E.E., 2018. Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes. JEPIN (Jurnal Edukasi dan Penelitian Informatika), 4(2), pp.113-118. DOI: 10.26418/jp.v4i2.27526.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Sembodo, J.E., Setiawan, E.B. and Baizal, Z.A., 2016, October. Data Crawling Otomatis pada Twitter. In Indonesian Symposium on Computing (Indo-SC) (pp. 11-16). DOI: 10.21108/indosc.2016.111.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Kotsiantis, S.B., Kanellopoulos, D. and Pintelas, P.E., 2006. Data preprocessing for supervised leaning. International journal of computer science, 1(2), pp.111-117.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
License & Copyright
Creative Commons License

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

Authors who publish with this journal agree to the following terms:

1. Copyright Retention and Open Access License

Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

2. Rights Granted Under CC BY 4.0

Under this license, readers are free to:

  • Share — copy and redistribute the material in any medium or format
  • Adapt — remix, transform, and build upon the material for any purpose, including commercial use
  • No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed

3. Attribution Requirements

All uses must include:

  • Proper citation of the original work
  • Link to the Creative Commons license
  • Indication if changes were made to the original work
  • No suggestion that the licensor endorses the user or their use

4. Additional Distribution Rights

Authors may:

  • Deposit the published version in institutional repositories
  • Share through academic social networks
  • Include in books, monographs, or other publications
  • Post on personal or institutional websites

Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.

5. Self-Archiving and Pre-Print Sharing

Authors are encouraged to:

  • Share pre-prints and post-prints online
  • Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
  • Engage in scholarly communication throughout the publication process

6. Open Access Commitment

This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.

Downloads

Similar Articles

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

Most read articles by the same author(s)