Classification of Bank Syariah Indonesia (BSI) Customer Sentiments on Twitter Using Naive Bayes Algorithm

Muhammad Alfarisi; Munawir Munawir; Samsuddin Samsuddin

Penulis

Muhammad Alfarisi Universitas Serambi Mekkah Penulis
Munawir Munawir Universitas Serambi Mekkah Penulis
Samsuddin Samsuddin Universitas Serambi Mekkah Penulis

Kata Kunci:

Tweets, Bank Syariah Indonesia, Analisis Media Sosial, Naïve Bayes Classifier, Sentiment Analysis

Abstrak

Bank Syariah Indonesia (BSI) is an active topic of conversation on Twitter, but customer sentiment patterns towards the bank's services have not been quantitatively analyzed. This study performs positive and negative sentiment classification on 24,401 Indonesian tweets collected on May 17, 2023. The preprocessing stage includes text cleaning, nonstandard word normalization, stopword removal, and stemming with the Sastrawi library. The data was labeled based on the affection dictionary and verified manually. Text representation is done with word frequency-based unigram-bigram method using CountVectorizer, then trained using Multinomial Naive Bayes algorithm. Evaluation of the model against test data resulted in an accuracy of 94%, with precision, recall, and F1-score of 93% each. Words that commonly appear in positive sentiments include easy and fast service, while negative sentiments are dominated by the words error and maintenance. These results show that the Naive Bayes-based approach and word frequency representation are effective for rapid analysis of public opinion towards BSI on social media.

Referensi

I. Amelia, A. M. Adinda, and I. Santoso, “Analisis Sentimen Opini Publik Terhadap Pengambil Alihan Tmii Oleh Pemerintah Dengan Algoritma Naïve Bayes,” J. IKRAITH-INFORMATIKA, vol. 7, no. 2, pp. 142–148, 2023. [Online]. Available: https://journals.upi-yai.ac.id/index.php/ikraith-informatika/issue/archive

D. Puspitasari, S. S. Al Khautsar, and W. P. Mustika, “Algoritma Naïve Bayes Untuk Memprediksi Kredit Macet Pada Koperasi Simpan Pinjam,” J. Informatika Upgris, vol. 4, no. 2, 2019. [Online]. Available: https://doi.org/10.26877/jiu.v4i2.2919

T. Arifin and S. Syalwah, “Prediksi Keberhasilan Immunotherapy Pada Penyakit Kutil Dengan Menggunakan Algoritma Naïve Bayes,” J. Responsif: Riset Sains dan Informatika, vol. 2, no. 1, pp. 38–43, 2020. [Online]. Available: https://doi.org/10.51977/jti.v2i1.177

E. Karyadiputra, “Analisis Algoritma Naive Bayes Untuk Klasifikasi Status Kesejahteraan Rumah Tangga Keluarga Binaan Sosial,” Technologia: J. Ilm., vol. 7, no. 4, pp. 199–208, 2016. [Online]. Available: https://doi.org/10.31602/tji.v7i4.653

I. Novitasari, T. B. Kurniawan, D. A. Dewi, and Misinem, “Analisis Sentimen Masyarakat Terhadap Tweet Ruang Guru Menggunakan Algoritma Naive Bayes Classifier (NBC),” J. Mantik, vol. 6, no. 3, pp. 2685–4236, 2022.

C. Supriyanto and I. N. Dewi, “Klasifikasi Teks Pesan Spam Menggunakan Algoritma Naïve Bayes,” Simantik 2013, pp. 156–160, 2013.

M. Y. Haffandi, E. Haerani, F. Syafria, and L. Oktavia, “Klasifikasi Penyakit Paru-Paru Dengan Menggunakan Metode Naïve Bayes Classifier,” J. Tek. Inf. dan Komputer (Tekinkom), vol. 5, no. 2, p. 176, 2022. [Online]. Available: https://doi.org/10.37600/tekinkom.v5i2.649

A. M. Rahat, A. Kahir, and A. K. M. Masum, “Comparison of Naive Bayes and SVM Algorithm Based on Sentiment Analysis Using Review Dataset,” in Proc. 8th Int. Conf. on System Modeling and Advancement in Research Trends (SMART), 2019, pp. 266–270. [Online]. Available: https://doi.org/10.1109/SMART46866.2019.911751

P. P. M. Surya and B. Subbulakshmi, “Sentimental Analysis Using Naive Bayes Classifier,” in Proc. Int. Conf. on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), 2019, pp. 1–5. [Online]. Available: https://doi.org/10.1109/ViTECoN.2019.8899618

S. D. Prasetyo, S. S. Hilabi, and F. Nurapriani, “Analisis Sentimen Relokasi Ibukota Nusantara Menggunakan Algoritma Naïve Bayes Dan KNN,” J. KomtekInfo, vol. 10, pp. 1–7, 2023. [Online]. Available: https://doi.org/10.35134/komtekinfo.v10i1.330

A. V. Sudiantoro and E. Zuliarso, “Analisis Sentimen Twitter Menggunakan Text Mining Dengan Algoritma Naïve Bayes Classifier,” Pros. SINTAK, pp. 398–401, 2018.

A. R. Isnain, N. S. Marga, and D. Alita, “Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm,” IJCCS (Indonesian J. Comput. and Cybern. Syst.), vol. 15, no. 1, p. 55, 2021. [Online]. Available: https://doi.org/10.22146/ijccs.60718

F. Ratnawati, “Implementasi Algoritma Naive Bayes Terhadap Analisis Sentimen Opini Film Pada Twitter,” INOVTEK Polbeng - Seri Informatika, vol. 3, no. 1, p. 50, 2018. [Online]. Available: https://doi.org/10.35314/isi.v3i1.335

W. A. Prabowo and C. Wiguna, “Sistem Informasi UMKM Bengkel Berbasis Web Menggunakan Metode SCRUM,” J. Media Inform. Budidarma, vol. 5, no. 1, p. 149, 2021. [Online]. Available: https://doi.org/10.30865/mib.v5i1.2604

S. Suryono and E. T. Luthfi, “Analisis Sentimen Pada Twitter Dengan Menggunakan Metode Naïve Bayes Classifier,” Jnanaloka, pp. 81–86, 2021. [Online]. Available: https://doi.org/10.36802/jnanaloka.2020.v1-no2-81-86