Sentiment Analysis of Digital Ethics in YouTube Islamic Preaching Videos Using Support Vector Machine
Article Sidebar
Main Article Content
Arizka Sabilah Rahmah
Awang Andhyka
Rizky Aditya Nugroho
Abstract
The rapid expansion of Islamic preaching in the digital sphere, particularly through YouTube, calls for a deeper understanding of communication ethics as reflected in user responses. This study analyzes the sentiments expressed in comments on Islamic preaching videos to identify patterns of digital ethics within online communities. The research employs a Support Vector Machine (SVM) classification model with TF-IDF feature representation. Data were collected from YouTube comments and processed through several preprocessing stages, including text cleaning, case normalization, tokenization, stopword removal, and stemming, before being manually labeled into three sentiment categories: positive, negative, and neutral. Testing on 22 data samples shows that the SVM model achieved an accuracy of 77.27%, with the highest performance observed in the neutral category. Misclassification in the positive and negative categories was mainly influenced by data imbalance and linguistic variations commonly found in religious discourse. These findings indicate that SVM combined with TF-IDF is reasonably effective for sentiment analysis in the context of digital Islamic preaching; however, improvements in data balance and the incorporation of contextual features are necessary to enhance classification performance. Overall, this study provides an initial insight into audience response patterns toward digital Islamic preaching and contributes to the development of digital ethics research in Islamic communication studies.
Downloads
Article Details
Copyright (c) 2025 Arizka Sabilah Rahmah, Awang Andhyka, Rizky Aditya Nugroho
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
N. Khotimah, I. Supena, N. Amin, P. A. Putra, dan A. Setyawan, “Analysis of Digital Da’wa Ideology on Social Media: A Case Study of Preaching Actors on Youtube,” Ilmu Dakwah Acad. J. Homilet. Stud., vol. 18, no. 2, hlm. 283–302, Des 2024, doi: 10.15575/idajhs.v18i2.33698.
N. Kumala, “AL-QUR’AN DAN ETIKA DAKWAH VIRTUAL,” J. Dakwah, vol. 21, no. 1, hlm. 59–84, Jan 2021, doi: 10.14421/JD.2112020.5.
A. Hakim, “Etika Komunikasi Dakwah Virtual di Era Digital,” vol. 2, no. 2, 2024.
Firman Maulidna, Khairatul Ulfi, Annisa Mulia, Ahmad Zuhri Ramadhan, dan Muhammad Saleh, “Etika Dakwah di Media Digital : Tantangan dan Solusi,” J. Manaj. Dan Pendidik. Agama Islam, vol. 3, no. 2, hlm. 315–336, Jan 2025, doi: 10.61132/jmpai.v3i2.1005.
H. A. R. Harpizon, R. Kurniawan, I. Iskandar, R. Salambue, E. Budianita, dan F. Syafria, “Analisis Sentimen Komentar Di YouTube Tentang Ceramah Ustadz Abdul Somad Menggunakan Algoritma Naïve Bayes,” vol. 5, no. 1, 2022.
I. Annamoradnejad dan G. Zoghi, “ColBERT: Using BERT Sentence Embedding in Parallel Neural Networks for Computational Humor,” Expert Syst. Appl., vol. 249, hlm. 123685, Sep 2024, doi: 10.1016/j.eswa.2024.123685.
R. S. Al Fathir As, E. Utami, dan A. Dwi Hartono, “Comparison of Sentiment Analysis Methods on Topic Haram of Music In Youtube,” sinkron, vol. 8, no. 4, hlm. 2294–2307, Okt 2023, doi: 10.33395/sinkron.v8i4.12776.
D. A. Fidian, T. N. Fatyanosa, dan A. Ridok, “Analisis Sentimen Terhadap Program Indonesian International Student Mobility Award (IISMA) di Platform X / Twitter Menggunakan TF-IDF dan SVM”.
K. E. V. Sihombing dan M. A. I. Pakereng, “ANALISIS SENTIMEN KOMENTAR YOUTUBE TERHADAP WAWANCARA PRESIDEN PRABOWO MENGGUNAKAN MACHINE LEARNING DAN ORANGE DATA MINING,” vol. 4, no. 4, 2025.
M. L. Putri, V. W. Sutjipto, M. P. Sary, L. Firdausah, dan Y. P. Amarawati, “PENGARUH MEDIA SOSIAL INSTAGRAM TERHADAP INFORMASI PENYEBARAN DAKWAH KEPADA MAHASISWA PROGRAM STUDI ILMU KOMUNIKASI UNIVERSITAS NEGERI JAKARTA,” KINESIK, vol. 8, no. 3, hlm. 284–296, Des 2021, doi: 10.22487/ejk.v8i3.204.
F. Aprianti, H. Sari, Rodiah, dan A. Hawariah, “Analisis Literasi Digital dan Kesiapan Dakwah Digital Mahasiswa Sekolah Tinggi Ilmu Bahasa Arab Makassar,” SETYAKI J. Studi Keagamaan Islam, vol. 2, no. 3, hlm. 64–77, Agu 2024, doi: 10.59966/setyaki.v2i3.1470.
M. N. Ibad, “Strategi Literasi Dakwah Digital di Era Media Sosial TikTok: Tantangan dan Peluang,” Pelita J. Studi Islam Mhs. UII Dalwa, vol. 2, no. 2, hlm. 145–156, Mei 2025, doi: 10.38073/pelita.v2i2.2189.
M. R. Kamal, K. Noviyanto, H. Subhan, dan L. Afiana, “Sentiment Analysis on Social Media and Stakeholders about Negative Issues Among Islamic Boarding School Community In Indonesia,” J. Theol., vol. 35, no. 2, hlm. 261–280, Des 2024, doi: 10.21580/teo.2024.35.2.23164.
Jauharil Maknuni dan Ishaq, “Peningkatan Literasi Digital dan Etika Bermedia Sosial Bagi Santri Dayah Darul Muta’alimin,” BA J. Pengabdi. Kpd. Masy., vol. 2, no. 1, hlm. 18–23, Mei 2024, doi: 10.58477/ba.v2i1.227.
F. A. Huzaidi, F. Maghfiroh, U. N. Ula, dan A. H. Siswanto, “Dimensi Aksiologis Dakwah Dalam Era Digital: Transformasi Nilai Etika Dakwah Dalam Ruang Virtual”.
Z. Arahman, “METODE DAKWAH DIGITAL DALAM KOMUNIKASI ISLAM UNTUK MENGUATKAN PEMAHAMAN KEISLAMAN GENERASI MILENIAL”.
H. Campbell, Digital Religion: Understanding Religious Practice in New Media Worlds. dalam Digital Religion: Understanding Religious Practice in New Media Worlds. Routledge, 2013. [Daring]. Tersedia pada: https://books.google.co.id/books?id=ox4q7T59KikC
Hoover Stewart M., The media and religious authority /. Pennsylvania: University Park, 2016.
danah boyd, The Social Lives of Networked Teens. New Haven: Yale University Press, 2014. doi: doi:10.12987/9780300166439.
Z. Tufekci, Twitter and tear gas: the power and fragility of networked protest. New Haven London: Yale university press, 2017.
D. McQuail, McQuail’s mass communication theory, 6. ed. Los Angeles, Calif.: SAGE, 2010.
Roger Silverstone, Media and Morality: On the Rise of the Mediapolis. [Daring]. Tersedia pada: https://www.wiley.com/Media+and+Morality%3A+On+the+Rise+of+the+Mediapolis-p-9780745657875
C. V. Baccarella, T. F. Wagner, J. H. Kietzmann, dan I. P. McCarthy, “Social media? It’s serious! Understanding the dark side of social media,” Eur. Manag. J., vol. 36, no. 4, hlm. 431–438, 2018, doi: https://doi.org/10.1016/j.emj.2018.07.002.
Bing Liu , University of Illinois, Chicago, Sentiment Analysis Mining Opinions, Sentiments, and Emotions, 2nd Edition. November 2020.
J. Prager, “Open-Domain Question–Answering,” Found. Trends® Inf. Retr., vol. 1, no. 2, hlm. 91–231, 2006, doi: 10.1561/1500000001.
L.-C. Chen, C.-M. Lee, dan M.-Y. Chen, “Exploration of social media for sentiment analysis using deep learning,” Soft Comput., vol. 24, no. 11, hlm. 8187–8197, Jun 2020, doi: 10.1007/s00500-019-04402-8.
D. Tang, F. Wei, N. Yang, M. Zhou, T. Liu, dan B. Qin, “Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification,” dalam Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Baltimore, Maryland: Association for Computational Linguistics, 2014, hlm. 1555–1565. doi: 10.3115/v1/P14-1146.
C. D. Manning, P. Raghavan, dan H. Schütze, Introduction to Information Retrieval, 1 ed. Cambridge University Press, 2008. doi: 10.1017/CBO9780511809071.
I. H. Witten dan E. Frank, “Data mining: practical machine learning tools and techniques with Java implementations,” ACM SIGMOD Rec., vol. 31, no. 1, hlm. 76–77, Mar 2002, doi: 10.1145/507338.507355.
G. Salton dan C. Buckley, “Term-weighting approaches in automatic text retrieval,” Inf. Process. Manag., vol. 24, no. 5, hlm. 513–523, 1988, doi: https://doi.org/10.1016/0306-4573(88)90021-0.
Y. Zhang, R. Jin, dan Z.-H. Zhou, “Understanding bag-of-words model: a statistical framework,” Int. J. Mach. Learn. Cybern., vol. 1, no. 1, hlm. 43–52, Des 2010, doi: 10.1007/s13042-010-0001-0.
P. A. N. Aryanti dan I. B. M. Mahendra, “Analisis Sentimen Opini Berbahasa Indonesia Pada Sosial Media Menggunakan TF-IDF dan Support Vector Machine,” . August, vol. 12, no. 1, 2023.
R. B. Afandi, T. F. Nurdiansyah, A. N. Ramadhani, dan A. P. Sari, “Implementasi Support Vector Machine untuk Analisis Sentimen Aplikasi ‘Mpstore - Super App UMKM,’” J. Inform. Polinema, vol. 10, no. 4, hlm. 565–570, Agu 2024, doi: 10.33795/jip.v10i4.5427.
C. Cortes dan V. Vapnik, “Support-vector networks,” Mach. Learn., vol. 20, no. 3, hlm. 273–297, Sep 1995, doi: 10.1007/BF00994018.
Y. Kim, “Convolutional Neural Networks for Sentence Classification,” dalam Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar: Association for Computational Linguistics, 2014, hlm. 1746–1751. doi: 10.3115/v1/D14-1181.
J. Pennington, R. Socher, dan C. Manning, “Glove: Global Vectors for Word Representation,” dalam Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar: Association for Computational Linguistics, 2014, hlm. 1532–1543. doi: 10.3115/v1/D14-1162.
T. Mikolov, K. Chen, G. Corrado, dan J. Dean, “Efficient Estimation of Word Representations in Vector Space,” 7 September 2013, arXiv: arXiv:1301.3781. doi: 10.48550/arXiv.1301.3781.
A. Vaswani dkk., “Attention Is All You Need,” 2 Agustus 2023, arXiv: arXiv:1706.03762. doi: 10.48550/arXiv.1706.03762.
R. Feldman, “Techniques and applications for sentiment analysis,” Commun. ACM, vol. 56, no. 4, hlm. 82–89, Apr 2013, doi: 10.1145/2436256.2436274.
J. Han, M. Kamber, dan J. Pei, Data Mining: Concepts and Techniques. dalam The Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann, 2011. [Daring]. Tersedia pada: https://books.google.co.id/books?id=pQws07tdpjoC
N. Cristianini dan J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, 2000. [Daring]. Tersedia pada: https://books.google.co.id/books?id=_PXJn_cxv0AC
V. N. Patodkar dan S. I.R, “Twitter as a Corpus for Sentiment Analysis and Opinion Mining,” IJARCCE, vol. 5, no. 12, hlm. 320–322, Des 2016, doi: 10.17148/IJARCCE.2016.51274.
H. Jenkins, Convergence Culture: Where Old and New Media Collide. dalam New York University Press. NYU Press, 2008. [Daring]. Tersedia pada: https://books.google.co.id/books?id=HZgUCgAAQBAJ
K. A. Neuendorf, The Content Analysis Guidebook. SAGE Publications, 2017. [Daring]. Tersedia pada: https://books.google.co.id/books?id=nMA5DQAAQBAJ
S. Livingstone, “Media Literacy and the Challenge of New Information and Communication Technologies,” Commun. Rev., vol. 7, no. 1, hlm. 3–14, Jan 2004, doi: 10.1080/10714420490280152.
D. Jurafsky dan J. H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. dalam Prentice Hall series in artificial intelligence. Pearson Prentice Hall, 2009. [Daring]. Tersedia pada: https://books.google.co.id/books?id=fZmj5UNK8AQC
N. N. Budi Darma Setiawan, “Klasifikasi Emosi pada Komentar YouTube menggunakan Algoritme Support Vector Machine,” J. Pengemb. Teknol. Inf. Dan Ilmu Komput., vol. 7, no. 9, 02 2023, Diakses: 12 Januari 2025. [Daring]. Tersedia pada: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/12783
A. N. Ma’aly, D. Pramesti, A. D. Fathurahman, dan H. Fakhrurroja, “Exploring Sentiment Analysis for the Indonesian Presidential Election Through Online Reviews Using Multi-Label Classification with a Deep Learning Algorithm,” Information, vol. 15, no. 11, hlm. 705, Nov 2024, doi: 10.3390/info15110705.
T. Sukayat, “Da’wah communication in the Contemporary Era: Implementing da’wah ethics on social media,” J. Ilmu Dakwah, vol. 43, no. 2, hlm. 375–390, Des 2023, doi: 10.21580/jid.v43.2.18465.
Z. I. Nuriana, N. Salwa, dan U. S. R. Tulungangung, “Digital Da’wah in the Age of Algorithm: A Narrative Review of Communication, Moderation, and Inclusion”.