Analisis Sentimen Pengguna X Terhadap Isu Adzan Menjadi Running Text Menggunakan Algoritma K-Nearest Neighbors (KNN)
- Arya Rifaldi Baharudin Universitas Pelita Bangsa, Bekasi, Indonesia
- Sufajar Butsianto Universitas Pelita Bangsa, Bekasi, Indonesia
-
Asep Supriyanto
*
Universitas Pelita Bangsa, Bekasi,
Indonesia
(*) Corresponding Author
Abstract
Sentiment analysis has become an important method for understanding public opinion on social and religious issues. This study aims to analyze user sentiment regarding the issue of the adzan presented in a running text format using the K-Nearest Neighbors (K-NN) algorithm. The adzan as running text on national television occurred during Pope Francis's mass at Gelora Bung Karno Stadium (GBK) on September 5, 2024. The Ministry of Religious Affairs (Kemenag) advised that the Maghrib adzan, usually broadcast on national television, be replaced with running text. This recommendation was made to facilitate the live broadcast of the mass attended by Christian congregants and to honor the worship without disruption. Some parties, such as the Indonesian Ulema Council (MUI) and the General Chairman of PP Persis, stated that replacing the adzan with running text does not violate Islamic law, while Minister of Communication and Information Budi Arie Setiadi mentioned that the change is merely a suggestion. The research findings indicate that the K-NN algorithm involves several stages, including data collection and labeling, text processing, feature extraction using TF-IDF, and splitting the data into 80% for Training and 20% for Testing. Based on the test results, the K-NN model detected 10 positive sentiments and 168 negative sentiments, indicating a tendency for Twitter users to express more negative sentiments. Analysis using a Confusion matrix shows that this model achieved an accuracy rate of 88%, indicating good performance in sentiment classification.
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References
A. Yoga Pratama et al., “Analisis Sentimen Media Sosial Twitter Dengan Algoritma K-Nearest Neighbor Dan Seleksi Fitur Chi-Square (Kasus Omnibus Law Cipta Kerja),” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 897–910, 2021.
E. Ditendra, Suryani, S. Romelah, M. H. A. Tanjung, and M. Sarah, “Comparison of Classification Algorithms for Sentiment Analysis ofIslam Nusantara in Indonesia,” Indones. J. Mach. Learn. Comput. Sci., vol. 2, no. 1, pp. 71–77, 2022.
A. D. A. Putra and S. Juanita, “Analisis Sentimen Pada Ulasan Pengguna Aplikasi Bibit Dan Bareksa Dengan Algoritma KNN,” J. Tek. Inform. dan Sist. Inf., vol. 8, no. 2, pp. 636–646, 2021.
F. Upa, Nurhalifah, and S. Profesional Makassar, “Analisis Sentimen Pengguna Twiter Menggunakan Metode SVM-KNN Analysis of Twiter User Sentiment Using SVM-KNN Method,” Nusant. Hasana J., vol. 3, no. 12, pp. 57–64, 2024.
M. Kartika, S. Saepudin, and D. Gustian, “Analisis Sentimen Dampak Covid-19 Terhadap Pembatalan Keberangkatan Ibadah Haji Pada Tahun 2020,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 964–972, 2021, [Online]. Available: https://tunasbangsa.ac.id/ejurnal/index.php/jsakti
G. V. A. Putri, M. E. P. Damanik, K. Hendiko, H. B. P. Simarmata, and A. M. Husein, “Analisis Sinyal Ekg Untuk Deteksi Gangguan Jantung Pada Mahasiswa Dengan Gangguan Tidur Menggunakan Algoritma K-Nearest Neighbor (KNN),” Dinamik, vol. 30, no. 2, pp. 361–368, 2025, [Online]. Available: https://www.unisbank.ac.id/ojs/index.php/fti1/article/view/10256
A. Irzan Busrayan, “Analisis Sentimen Pelanggan Terhadap Aplikasi Wondr By Bni Menggunakan Naiyes Bayes, Support Vector Machine (Svm), Dan K-Nearest Neighbor (Knn),” J. Comput. Sci. Inf. Technol., vol. 2, no. 2, pp. 263–274, 2025.
M. E. Siregar, S. Dermawan, and A. A. Hisyam, “Perbandingan Kinerja Naive Bayes dan KNN dalam Analisis Sentimen Komentar X dengan dan tanpa Text Preprocessing ( Studi Kasus : Danantara ),” JITET (Jurnal Inform. dan Tek. Elektro Ter.), vol. 13, no. 3, pp. 2830–7062, 2021, [Online]. Available: https://www.researchgate.net/publication/357456859_Perbandingan_Naive_Bayes_SVM_dan_k-NN_untuk_Analisis_Sentimen_Gadget_Berbasis_Aspek
C. N. Mayasari, M. A. Soeleman, and Pujiono, “Deteksi Pornografi pada Karakter Animasi 2D dengan KNN (K-Nearest Neighbors) Menggunakan Fitur HSV,” J. Penelit. Dan Pengabdi. Masy., vol. 2, no. 8, pp. 1236–1250, 2022.
S. Chairunnissa Deliva Akbar, D. Agus Salim, R. Rahmad, A. Khomsi, and R. Sovia, “Perbandingan Kinerja Pca-Knn Dan Lda Dalam Klasifikasi Jenis Kupu-Kupu: Analisis Akurasi Dan Efektivitas,” JATI (Jurnal Mhs. Tek. Inform.), vol. 9, no. 3, pp. 5014–5022, 2025, doi: 10.36040/jati.v9i3.13863.
E. A. Cahyono, Sutomo, and A. Harsono, “Literatur Review: Panduan Penulisan dan Penyusunan,” J. Keperawatan, 2019.
E. N. Hamdana and M. B. I. Alfahmi, “Pengembangan Sistem Analisis Sentimen Berbasis Java Pada Data Twitter Terhadap Omnibus Law Menggunakan Algoritma Naïve Bayes dan K-Nearst Neighbor (K-NN),” J. Inform. Polinema, vol. 7, no. 2, pp. 79–84, 2021, doi: 10.33795/jip.v7i2.688.
F. N. Rahman and S. Lestari, “Analisis Sentimen Masyarakat Terhadap Pemerintah Di Era Kabinet Joko Widodo berdasarkan Sosial Media X Menggunakan Naïve Bayes Dan K-Nearest Neighbor (KNN),” J. Inf. Technol. Comput. Sci., vol. 7, no. 5, pp. 1537–1544, 2024.
F. V. Sari and A. Wibowo, “Analisis Sentimen Pelanggan Toko Online Jd.Id Menggunakan Metode Naïve Bayes Classifier Berbasis Konversi Ikon Emosi,” J. SIMETRIS, vol. 10, no. 2, pp. 681–686, 2019.
B. M. Nasution, A. Waladi, U. Khaira, and P. E. P. Utomo, “Analisis Prediktif Tren Pendidikan di Indonesia Menggunakan KNN: Studi Kasus Data Pendidikan 2021-2023,” Educ. Libr. J., vol. 1, no. 2, pp. 1–12, 2025, [Online]. Available: https://online-journal.unja.ac.id/edlib/article/view/41111/19918
U. S. Putri, T. Taryo, and A. Hindasyah, “Analisis Pemetaan Kemampuan Akademik Mahasiswa Program Studi Teknik Informatika Dengan Metode Naive Bayes Classifier Dan K-Nearest Neighbor (KNN) (Studi Kasus: Universitas Pamulang),” J. Sains dan Teknol., vol. 7, no. 1, pp. 24–31, 2025.
R. Puspita and A. Widodo, “Perbandingan Metode KNN, Decision Tree, dan Naïve Bayes Terhadap Analisis Sentimen Pengguna Layanan BPJS,” J. Inform. Univ. Pamulang, vol. 5, no. 4, p. 646, 2021, doi: 10.32493/informatika.v5i4.7622.
I. Yurni, A. Rahman, and A. Pratama, “Analisis Kelayakan Model K-Nearest Neighbor dalam Penentuan Klasifikasi Guru berprestasi,” SISFO J. Ilm. Sist. Inf., vol. 9, no. 1, pp. 22–37, 2025.
Yuyun, N. Hidayah, and S. Sahibu, “Algoritma Multinomial Naïve Bayes Untuk Klasifikasi Sentimen Pemerintah Terhadap Penanganan Covid-19 Menggunakan Data Twitter,” J. RESTI, vol. 5, no. 4, pp. 820–826, 2021, doi: 10.29207/resti.v5i4.3146.
F. Irwannia and A. H. Lubis, “Analisis Sentimen Produk Berdasarkan Review Pelanggan Shopee Menggunakan KNN Sentiment Analysis of Shopee Product Reviews Using the K- Nearest Neighbors ( KNN ) Algorithm,” INCODING J. Inform. Comput. Sci. Eng., vol. 5, no. 2, pp. 239–249, 2025, [Online]. Available: https://journal.mahesacenter.org/index.php/incoding/article/view/865/pdf
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