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)
DOI:
https://doi.org/10.70746/jstunsada.v13i1.221Keywords:
Sentiment Analysis, Support Vector Machine, Naïve BayesAbstract
In the digital era, the internet has become an inseparable part of everyday life, including the ease of finding information and sharing opinions through social media such as Twitter and Facebook. On these two platforms, users can provide reviews about products, including IndiHome services. The large number of reviews on social media reflects the high level of feelings users have for the service. However, currently PT. Telkom Indonesia does not fully know the opinions and reviews of IndiHome customers on social media, both positive and negative. This study aims to improve understanding of the positive and negative opinions of customers towards IndiHome and to compare the effectiveness of the Support Vector Machine and Naive Bayes algorithms in sentiment analysis. Thus, PT. Telkom Indonesia can take the necessary steps to increase public trust in IndiHome and evaluate the performance of the classification results using the Support Vector Machine and Naïve Bayes methods. The data used in this study amounted to 5000 data, but after the data preparation stage, the remaining 2000 data. From the data that has gone through the preparation stage, there are 638 data with positive sentiment and 1341 data with negative sentiment. The test results on the Support Vector Machine model achieve an accuracy of 91%, while the Naive Bayes model achieves an accuracy of 85%.
References
Aldean, M. Y., Setya Nugraha, N. A., & Paradise. (2022). Analisis Sentimen Masyarakat Terhadap Vaksinasi Covid-19 di Twitter Menggunakan Metode Random Forest Classifier. 5.
Muhammad, F., Maghfur, N. M., & Voutama, A. (2022). Sentiment Analysis Dataset on COVID-19 Variant News (Vol. 4, Nomor 1).
Nur Akbar, M., Ardana, Y., Negeri Alauddin Makassar, I., & Juni, D. (2022). Analisis Sentimen Terhadap Jasa Ekspedisi Pos Indonesia Pada Sosial Media Twitter Menggunakan Naïve Bayes Classifier. JOURNAL SHIFT VOL, 2(2).
Pasek, P., Mahawardana, O., Sasmita, G. A., Agus, P., & Pratama, E. (2022). Analisis Sentimen Berdasarkan Opini dari Media Sosial Twitter terhadap “Figure Pemimpin” Menggunakan Python. Dalam JITTER-Jurnal Ilmiah Teknologi dan Komputer (Vol. 3, Nomor 1).
Petiwi, M. I., Triayudi, A., & Sholihati, I. D. (2022). Analisis Sentimen Gofood Berdasarkan Twitter Menggunakan Metode Naïve Bayes dan Support Vector Machine. Jurnal Media Informatika Budidarma, 6(1), 542. https://doi.org/10.30865/mib.v6i1.3530
Reddy, D., Arifianto, D., & Lusiana, D. (2022). Analisis Sentimen Pada Pelayanan Jaringan Internet Indihome Dengan Metode Multinomial Naïve Bayes Masa Pandemi Covid-19 Sentiment Analysis on Indihome Internet Network Services Using The Multinomial Naïve Bayes Method During The Covid-19 Pandemic. Dalam Jurnal Smart Teknologi (Vol. 3, Nomor 6). http://jurnal.unmuhjember.ac.id/index.php/JST
Sari, F. V., & Wibowo, A. (2019). Analisis Sentimen Pelanggan Toko Online Jd.Id Menggunakan Metode Naïve Bayes Classifier Berbasis Konversi Ikon Emosi. Jurnal Simetris, 10(2).
Yusuf, A. N., Supriyati, E., & Listyorini, T. (2020). Analisis Sentimen Mengenai
Layanan Provider Indihome Berdasarkan Pendapat Pelanggan Melalui Media Sosial Twitter dengan Metode Naïve Bayes Classifier.
Kurniawan, D., & Yasir, D. M. (2022). OPTIMIZATION SENTIMENT ANALYSIS USING CRISP-DM AND NAÏVE BAYES METHODS IMPLEMENTED ON SOCIAL MEDIA. 6, 74–84.
Locarso, G. K. (2022). ANALISIS SENTIMEN REVIEW APLIKASI PEDULILINDUNGI PADA GOOGLE PLAY STORE MENGGUNAKAN NBC. Jurnal Teknik Informatika Kaputama (JTIK), 6(2).
Pratama, A. E., Ariesta, A., & Gata, G. (2022). Analisis Sentimen Masyarakat terhadap Tim Nasional Indonesia pada Piala AFF 2020 Menggunakan Algoritma K-Nearest Neighbors The researcher uses the CrossIndustry Standard Process for Data Mining (CRISP-DM) method and implements the K-Nearest. Jurnal TICOM: Technology of Information and Communication, 10(3), 187–196.
Ramadhani, S. H., & Wahyudin, M. I. (2022). Analisis Sentimen Terhadap Vaksinasi Astra Zeneca pada Twitter Menggunakan Metode Naïve Bayes dan K-NN. Jurnal Teknologi Informasi dan Komunikasi), 6(4), 2022.
Suryati, E., Ari Aldino, A., Penulis Korespondensi, N., & Suryati Submitted, E. (2023). Analisis Sentimen Transportasi Online Menggunakan Ekstraksi Fitur Model
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