Sentiment classification of interfaith marriage ban using Naive Bayes Classifier method
Keywords:
interfaith marriage, sentiment, Instagram, naive bayes
Abstract
Interfaith marriage is still a controversial issue in multicultural Indonesian society. The provisions of Law Number 1 of 1974 which prohibit interfaith marriages have triggered various responses in society. In 2023, the Supreme Court (MA) decided to prohibit religious courts from registering interfaith marriages. This further strengthened the controversy and sparked various reactions from the public. Platforms like Instagram have become a forum for people to express their various sentiments regarding this issue, ranging from support, rejection, to questions and doubts. This research classifies 1000 Instagram comments collected from five news social media accounts. These comments were labeled manually by an expert who works as an Indonesian language lecturer, so they were divided into 500 positive comments and 500 negative comments. After going through the text preprocessing process and TF-IDF weighting, the Naïve Bayes Classifier method succeeded in achieving the highest level of accuracy of 76% by using 10% of test data from the dataset to classify public sentiment towards the ban on interfaith marriage in Instagram comments.
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References
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[4] A. Amri, “Perkawinan Beda Agama Menurut Hukum Positif dan Hukum Islam,” Media Syari’ah Wahana Kaji. Huk. Islam dan Pranata Sos., vol. 22, no. 1, pp. 48–64, May 2020, doi: 10.22373/jms.v22i1.6719.
[5] K. A. Nugraha, “Analisis Sentimen Berbasis Emoticon pada Komentar Instagram Bahasa Indonesia Menggunakan Naïve Bayes,” J. Tek. Inform. dan Sist. Inf., vol. 7, no. 3, pp. 715–721, Dec. 2021, doi: 10.28932/jutisi.v7i3.4094.
[6] A. Muhaddisi, B. N. Prastowo, and D. U. Kusumaning Putri, “Sentiment Analysis With Sarcasm Detection On Politician’s Instagram,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 15, no. 4, pp. 349–358, Oct. 2021, doi: 10.22146/ijccs.66375.
[7] R. L. Musyarofah, E. Utami, and S. Raharjo, “Analisis Komentar Potensial pada Social Commerce Instagram Menggunakan TF-IDF,” J. Eksplora Inform., vol. 9, no. 2, pp. 130–139, Mar. 2020, doi: 10.30864/eksplora.v9i2.360.
[8] F. A. Wenando, R. Hayami, Soni, A. Fitria, and D. F. Shifana, “Sentimen Analisis Masyarakt terhadap Kasus Penembakan Brigadir J Menggunakan Algoritma Naïve Bayes Classifier,” J. CoSciTech (Computer Sci. Inf. Technol., vol. 4, no. 2, pp. 484–490, Sep. 2023, doi: 10.37859/coscitech.v4i2.5686.
[9] D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” J. Sains Komput. Inform., vol. 5, no. 2, pp. 697–711, 2021, doi: 10.30645/j-sakti.v5i2.369.
[10] N. P. G. Naraswati, D. C. Rosmilda, D. Desinta, F. Khairi, R. Damaiyanti, and R. Nooraeni, “Analisis Sentimen Publik dari Twitter Tentang Kebijakan Penanganan Covid-19 di Indonesia dengan Naive Bayes Classification,” Sist. J. Sist. Inf., vol. 10, no. 1, pp. 222–238, Jan. 2021, doi: 10.32520/stmsi.v10i1.1179.
[11] A. Anggara, S. Widiono, A. T. Hidayat, and Sutarman, “Analysis of Netizen Comments Sentiment on Public Official Statements on Instagram Social Media Accounts,” Int. J. Adv. Data Inf. Syst., vol. 3, no. 2, pp. 87–97, Nov. 2022, doi: 10.25008/ijadis.v3i2.1244.
[12] M. Muslimin and V. Lusiana, “Analisis Sentimen Terhadap Kenaikan Harga Bahan Pokok Menggunakan Metode Naive Bayes Classifier,” J. Media Inform. Budidarma, vol. 7, no. 3, pp. 1200–1209, 2023, doi: 10.30865/mib.v7i3.6418.
[13] F. Syofiani, S. Alam, and M. I. S. S., “Analisis Sentimen Penilaian Masyarakat Terhadap Childfree Berdasarkan Komentar di Youtube Menggunakan Algoritma Naïve Bayes,” J. Teknol. Inform. dan Komput., vol. 9, no. 2, pp. 688–703, Sep. 2023, doi: 10.37012/JTIK.V9I2.1661.
[14] S. W. Ritonga, Yusra, M. Fikry, and E. P. Cynthia, “Klasifikasi Sentimen Masyarakat di Twitter terhadap Ganjar Pranowo dengan Metode Naïve Bayes Classifier,” Build. Informatics, Technol. Sci., vol. 5, no. 1, pp. 134–143, Jun. 2023, doi: 10.47065/bits.v5i1.3535.
[15] P. Pandunata, C. K. Ananta, and Y. Nurdiansyah, “Analisis Sentimen Opini Publik Terhadap Pekan Olahraga Nasional Pada Instagram Menggunakan Metode Naïve Bayes Classififer,” INFORMAL Informatics J., vol. 7, no. 2, pp. 146–156, Aug. 2022, doi: 10.19184/isj.v7i2.33928.
[16] Z. Firmansyah and N. F. Puspitasari, “Analisis Sentimen Masyarakat Terhadap Vaksinasi COVID-19 Berdasarkan Opini Pada Twitter Menggunakan Algoritma Naive Bayes,” J. Tek. Inform., vol. 14, no. 2, pp. 171–178, Oct. 2021, doi: 10.15408/JTI.V14I2.24024.
[17] A. Prananda, E. Haerani, M. Fikry, and F. Yanto, “Perbandingan Metode Naive Bayes Classifier dan Support Vector Machine dalam Analisis Sentimen Terhadap Pemilihan Presiden 2024,” Krea-TIF J. Tek. Inform., vol. 11, no. 2, pp. 84–94, Dec. 2023, doi: 10.32832/KREA-TIF.V11I2.15364.
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[19] E. B. Susanto, P. A. Christianto, M. R. Maulana, and S. W. Binabar, “Analisis Kinerja Algoritma Naïve Bayes Pada Dataset Sentimen Masyarakat Aplikasi NEWSAKPOLE Samsat Jawa Tengah,” J. CoSciTech (Computer Sci. Inf. Technol., vol. 3, no. 3, pp. 234–241, Dec. 2022, doi: 10.37859/coscitech.v3i3.4343.
[20] S. H. A. Samsudin, N. M. Sabri, N. Isa, and U. F. M. Bahrin, “Sentiment Analysis on Acceptance of New Normal in COVID-19 Pandemic using Naïve Bayes Algorithm,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 9, pp. 581–588, Dec. 2022, doi: 10.14569/IJACSA.2022.0130968.
[21] S. Saepudin, S. Widiastuti, and C. Irawan, “Analisis Sentimen Ulasan Platform Media Sosial Menggunakan Algoritma Naïve Bayes Classifier,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 12, no. 2, pp. 236–243, Jul. 2023, doi: 10.32736/sisfokom.v12i2.1650.
[22] M. D. Nurmalasari, Kusrini, and Sudarmawan, “Komparasi Algoritma Naive Bayes dan K-Nearest Neighbor untuk Membangun Pengetahuan Diagnosa Penyakit Diabetes,” J. Komtika (Komputasi dan Inform., vol. 5, no. 1, pp. 52–59, Jul. 2021, doi: 10.31603/komtika.v5i1.5140.
[23] M. Yusran, S. Siswanto, and A. Islamiyati, “Comparison of Multinomial Naïve Bayes and Bernoulli Naïve Bayes on Sentiment Analysis of Kurikulum Merdeka with Query Expansion Ranking,” Sist. J. Sist. Inf., vol. 13, no. 1, pp. 96–106, Jan. 2024, doi: 10.32520/STMSI.V13I1.3187.
Published
2024-04-28
How to Cite
Muhammad Rizki Syafapri, Elin Haerani, Iwan Iskandar, & Liza Afriyanti. (2024). Sentiment classification of interfaith marriage ban using Naive Bayes Classifier method. Jurnal CoSciTech (Computer Science and Information Technology), 5(1), 10-18. https://doi.org/10.37859/coscitech.v5i1.6889
Section
Articles
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