Sentiment Analysis of Public Trust Towards Islamic Boarding School on Social Media Using Machine Learning Method
DOI:
https://doi.org/10.59934/jaiea.v5i1.1424Keywords:
Semicolon Islamic Boarding School, Machine Learning, Public Trust, Sentiment Analysis, Social MedeiaAbstract
This study aims to analyse public sentiment towards Islamic boarding schools on social media using a machine learning approach. A total of 1,905 cleaned comments were collected from two platforms, Twitter (X) and YouTube, and then processed through the CRISP-DM stages, which include business understanding, data preparation, modelling, evaluation, and deployment. Pre-processing steps such as tokenisation, stemming, and labelling were applied to prepare the text data for analysis. The sentiment classification was carried out using five machine learning algorithms: Naïve Bayes, Decision Tree, Neural Network, Support Vector Machine (SVM), and Random Forest. The evaluation results revealed that Random Forest outperformed other models, achieving the highest accuracy (79%), F1-score (79%), precision (80%), and recall (79%), indicating a strong balance in identifying sentiment classes accurately and consistently. Additionally, the research implemented interactive visualisations using Streamlit, enabling the public and stakeholders to understand sentiment trends in a clear, data-driven format. These findings are expected to serve as a strategic foundation for Islamic boarding schools in building a positive image in the digital space and for further development of AI-based opinion monitoring systems.
Downloads
References
A. N. Alfifa, V. Fahira, R. Batubara, K. Sakinah, J. Manik, and R. A. Yoes, “Media Sosial dan Pembentukan Opini Publik (Analisis Studi Kasus
Echo Chamber Pada Interaksi Komentar di Akun Instagram @Turnbackhoaxid Dalam Konteks Post-Truth),” J. Penelit. Ilmu-Ilmu Sos., vol. 2,
no. 6, pp. 162–169, 2025, [Online]. Available: https://ojs.daarulhuda.or.id/index.php/Socius/article/view/1130
Alisia Zahro’atul Baroroh and Abdul Khobir, “Peran Pondok Pesantren dalam Membentuk Karakter Anak Muda di Era Modern,” J. Ekon.
Akuntansi, dan Perpajak., vol. 2, no. 1, pp. 01–13, 2024, doi: 10.61132/jeap.v2i1.721.
S. Rahmatullah, Fahrurrozi, and A. Malik, “COMMUNICATION STRATEGY OF AS-SUNNAH ISLAMIC BOARDING SCHOOL IN
RESTORING ITS IMAGE AFTER IDENTITY CONFLICT IN BAGIK NYAKE,” Wasilatuna J. Komun. dan Penyiaran Islam, vol. 6, no. 02,
pp. 127–142, Oct. 2023, doi: 10.38073/wasilatuna.v6i02.1016.
S. Fanissa, M. A. Fauzi, and S. Adinugroho, “Analisis Sentimen Pariwisata di Kota Malang Menggunakan Metode Naive Bayes dan Seleksi
Fitur Query Expansion Ranking,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 8, pp. 2766–2770, 2018, [Online]. Available: http://jptiik.ub.ac.id
S. Ulum, M. Mispani, J. Jaenullah, and M. Thohir, “The Public Perception of Islamic Education at Wali Songo Islamic Boarding School
Sukajadi Village, Bumiratu Nuban District,” Bull. Pedagog. Res., vol. 1, no. 2, p. 187, Feb. 2021, doi: 10.51278/bpr.v1i2.190.
N. L. P. C. Savitri, R. A. Rahman, R. Venyutzky, and N. A. Rakhmawati, “Analisis Klasifikasi Sentimen Terhadap Sekolah Daring pada Twitter
Menggunakan Supervised Machine Learning,” J. Tek. Inform. dan Sist. Inf., vol. 7, no. 1, pp. 47–58, 2021, doi: 10.28932/jutisi.v7i1.3216.
D. Duei Putri, G. F. Nama, and W. E. Sulistiono, “Analisis Sentimen Kinerja Dewan Perwakilan Rakyat (DPR) Pada Twitter Menggunakan
Metode Naive Bayes Classifier,” J. Inform. dan Tek. Elektro Terap., vol. 10, no. 1, pp. 34–40, 2022, doi: 10.23960/jitet.v10i1.2262.
E. Ditendra, S. Suryani, S. Romelah, M. H. Arsyiddik Tanjung, and M. Sarah, “Perbandingan Algoritma Klasifikasi untuk Analisis Sentimen
Islam Nusantara di Indonesia,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 2, no. 1, pp. 71–77, May 2022, doi:
57152/malcom.v2i1.199.
A. Lathifah, Z. Arham, N. Hasanati, Zulfiandri, and E. Nurmiati, “Cross-Industry Standard Process For Data Mining (CRISP-DM) For
Discovering Association Rules in Graduate Tracer Study Data of Islamic Higher Education Institution,” in 2023 11th International Conference
on Cyber and IT Service Management (CITSM), IEEE, Nov. 2023, pp. 1–6. doi: 10.1109/CITSM60085.2023.10455691.
U. Shafique and H. Qaiser, “A Comparative Study of Data Mining Process Models ( KDD , CRISP-DM and SEMMA ),” Int. J. Innov. Sci. Res.,
vol. 12, no. 1, pp. 217–222, 2014, [Online]. Available: http://www.ijisr.issr-journals.org/
K. Purwandari, R. B. Perdana, J. W. C. Sigalingging, R. Rahutomo, and B. Pardamean, “Automatic Smart Crawling on Twitter for Weather
Information in Indonesia,” Procedia Comput. Sci., vol. 227, pp. 795–804, 2023, doi: 10.1016/j.procs.2023.10.585.
B. Gunawan, H. Pratiwi, Sasty, and E. Pratama, Esyudha, “Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes,”
J. Edukasi dan Penelit. Inform., vol. 2, no. 1, pp. 95–103, 2023.
N. W. A. S. Aprilia and A. R. Isnain, “Analisis Sentimen Terhadap Media Sosial Twitter dengan Kasus Kampanye Anti-Korupsi di Indonesia
Menggunakan Naive Bayes,” J. Media Inform. Budidarma, vol. 8, no. 2, p. 695, 2024, doi: 10.30865/mib.v8i2.7582.
C. A. Sugianto and T. H. Apandi, “Pengaruh Tokenisasi Kata N-Grams Spam SMS Menggunakan Support Vector Machine,” Citisee 2017, pp.
–9, 2017, [Online]. Available: osf.io/69bxn/
L. Havrlant and V. Kreinovich, “A simple probabilistic explanation of term frequency-inverse document frequency (tf-idf) heuristic (and
variations motivated by this explanation),” Int. J. Gen. Syst., vol. 46, no. 1, pp. 27–36, Jan. 2017, doi: 10.1080/03081079.2017.1291635.
A. S. Alammary, “Arabic Questions Classification Using Modified TF-IDF,” IEEE Access, vol. 9, pp. 95109–95122, 2021, doi:
1109/ACCESS.2021.3094115.
B. L. Kumaran and N. V. Ravindran, “NLP based Text Summarization Techniques for News Articles,” in 2024 IEEE International Conference
on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE, Mar. 2024, pp. 1–6. doi:
1109/IATMSI60426.2024.10503001.
A. Hermawan, I. Jowensen, J. Junaedi, and Edy, “Implementasi Text-Mining untuk Analisis Sentimen pada Twitter dengan Algoritma Support
Vector Machine,” JST (Jurnal Sains dan Teknol., vol. 12, no. 1, pp. 129–137, 2023, doi: 10.23887/jstundiksha.v12i1.52358.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Journal of Artificial Intelligence and Engineering Applications (JAIEA)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
