Comparison of Classification Algorithms with Bag of Words Feature in Sentiment Analysis

Authors

  • Fenilinas Adi Artanto Department of Informatics, Faculty of Engineering and Computer Science, Universitas Muhammadiyah Pekajangan Pekalongan

DOI:

https://doi.org/10.32736/sisfokom.v14i3.2426

Keywords:

Analysis Sentiment, Anomaly Tren, Bag of Words, Google Play Store

Abstract

The rapid growth of digital culture, especially on social media platforms, has led to the emergence of unique viral phenomena characterized by unconventional humor and illogical logic such as the Italian brainroot anomaly. Although there have been many studies on sentiment analysis, there is still a lack of studies focusing on cultural sentiment such as humor in the Italian brainroot anomaly. This study provides an overview of user sentiment analysis of the game “Hantu Tung Tung Tung Sahur 3D,” a culturally viral application anomaly italian brainroot among young people on the Google Play Store during the month of Ramadan. User reviews were collected through web scraping, and data preprocessing involved tokenization, stopword removal, lowercase, stemming, and filtering to prepare the text for analysis. Feature extraction was performed using the Bag of Words method. This study compares the performance of four widely used classification algorithms—Support Vector Machine (SVM), Naïve Bayes, Decision Tree (C4.5), and Random Forest—implemented through Orange Data Mining software, with evaluation based on K-Fold Cross Validation. The novelty of this study lies in its focus on sentiment analysis in a unique and culturally viral digital context, as well as a comparative evaluation of classification algorithms specifically on this dataset. The results show that the Random Forest algorithm achieves the highest Area Under the Curve (AUC) score of 0.529, outperforming Naïve Bayes (0.504), SVM (0.503), and Decision Tree (0.498). These findings provide new insights into the suitability of ensemble methods such as Random Forest for sentiment analysis in specific digital phenomena, highlighting its potential for more reliable sentiment classification in similar contexts.

References

N. Zarawaki, “Apa Itu Tren Anomali Brainrot? Ada Meme Tung Tung Sahur!,” IDN Times, Apr. 25, 2025. Accessed: May 05, 2025. [Online]. Available: https://www.idntimes.com/life/inspiration/nisa-zarawaki/apa-itu-tren-anomali-brainrot?page=all

Z. Hardiansyah, “Daftar Nama Anomali TikTok yang Lagi Viral, Ada Tung Tung Tung Sahur,” kompas.com, Apr. 24, 2025. [Online]. Available: https://tekno.kompas.com/read/2025/04/24/12350027/daftar-nama-anomali-tiktok-yang-lagi-viral-ada-tung-tung-tung-sahur?page=all

M. A. Budi, “Ini Asal Muasal Fenomena Meme Viral Tung Tung Sahur, dari Tradisi Ramadan ke Tren Digital,” Radar Jember, Jember, May 07, 2025. [Online]. Available: https://radarjember.jawapos.com/nasional/795980586/ini-asal-muasal-fenomena-meme-viral-tung-tung-sahur-dari-tradisi-ramadan-ke-tren-digital

C. Rosanti, F. A. Artanto, and R. E. Saputra, “Perception of user opinions towards sharia mobile banking applications in Indonesia based on comments on Google Play Store,” Serambi, vol. 7, no. 1, pp. 97–108, 2025, doi: https://doi.org/10.36407/serambi.v7i1.1486.

F. A. Artanto, “Analisis Sentimen Opini Publik terhadap Fenomena Bunuh Diri Mahasiswa di Twitter Menggunakan Algoritma Naive Bayes,” Satesi J. Sains Teknol. dan Sist. Inf., vol. 4, no. 1, pp. 70–76, 2024, doi: 10.54259/satesi.v4i1.2908.

I. Rosyadi, H. H. Kusumawardani, F. A. Artanto, A. A. A. Hardani, and F. Nafilaturrosyidah, “Clustering K-Means Dalam Pengelompokan Penjualan Produk Pada RTO Group,” Teknomatika, vol. 13, no. 02, pp. 55–60, 2023, [Online]. Available: http://ojs.palcomtech.ac.id/index.php/teknomatika/article/view/618/439

H. H. Kusumawardani, I. Rosyadi, F. A. Artanto, F. I. Arzha, and N. A. Rachmayani, “Analisis Decision Tree dalam Pengaruh Digital Marketing terhadap Penerimaan Siswa Baru,” Remik, vol. 6, no. April, pp. 225–231, 2022.

A. Fatkhudin, M. Y. Febrianto, F. A. Artanto, M. W. N. Hadinata, and R. Fahlevi, “Algoritma Decision Tree C.45 dalam analisa kelulusan mahasiswa Program Studi Manajemen Informatika UMPP,” J. Ilm. Ilmu Komput. Fak. Ilmu Komput. Univ. Al Asyariah Mandar, vol. 8, no. 2, pp. 83–86, 2022.

C. Rosanti, F. A. Artanto, and R. E. Saputra, “Analisis Sentiment Pengguna Aplikasi Mobile Banking Pada Bank Syariah Dengan Support Vector Regression,” J. Pendidik. dan Teknol. Indones., vol. 4, no. 8, pp. 341–347, 2024, doi: https://doi.org/10.52436/1.jpti.460.

F. A. Artanto, “Support Vector Machine Berbasis Particle Swarm Optimization Pada Analisis Sentimen Anggota KPPS,” J. FASILKOM (teknologi Inf. dan ILmu KOMputer), vol. 14, no. 1, pp. 75–79, 2024, doi: https://doi.org/10.37859/jf.v14i1.6795.

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.

A. Fatkhudin, F. A. Artanto, N. A. Safli, and D. Wibowo, “Decision Tree Berbasis SMOTE dalam Analisis Sentimen Penggunaan Artificial Intelligence untuk Skripsi,” Remik Ris. dan E-Jurnal Manaj. Inform. Komput., vol. 8, no. April, pp. 494–505, 2024, doi: 10.33395/remik.v8i2.13531.

F. A. Artanto, “Implementasi Algoritma Random Forest dan Model Bag of Words Dalam Analisis Sentimen Mengenai E-Materai,” Satesi J. Sains Teknol. dan Sist. Inf., vol. 4, no. 2, pp. 139–145, 2024, doi: 10.54259/satesi.v4i2.3240.

D. Krisnandi, R. N. Ambarwati, A. Y. Asih, A. Ardiansyah, and H. F. Pardede, “Analisis Komentar Cyberbullying Terhadap Kata Yang Mengandung Toksisitas Dan Agresi Menggunakan Bag of Words dan TF-IDF Dengan Klasifikasi SVM,” J. Linguist. Komputasional, vol. 6, no. 2, pp. 36–41, 2023, doi: 10.26418/jlk.v6i2.85.

E. Subowo, F. Adi Artanto, I. Putri, and W. Umaedi, “BLTSM untuk analisis sentimen berbasis aspek pada aplikasi belanja online dengan cicilan,” J. Fasilkom, vol. XII, no. Ii, pp. 132–140, 2022.

M. F. I. Haq, I. Rosyadi, M. Nasir, and A. Khambali, “Sentiment Analisis Ulasan Aplikasi Livin Pada Google Play Store,” J. Surya Inform., vol. 14, no. 1, pp. 24–29, 2024.

D. A. Putri and D. A. Muthia, “Implementasi Metode Lexicon Based dan Support Vector Machine Pada Analisis Sentimen Ulasan Pengguna ChatGPT,” IJCIT (Indonesian J. Comput. Inf. Technol., vol. 9, no. 2, pp. 80–86, 2024.

A. Wahyuningtyas, I. S. Sitanggang, and H. Khotimah, “Deteksi Spam pada Twitter Menggunakan Algoritme Naïve Bayes,” J. Ilmu Komput. dan Agri-Informatika, vol. 7, no. 1, pp. 31–40, 2020, doi: 10.29244/jika.7.1.31-40.

S. Alfaris and Kusnawi, “Komparasi Metode KNN dan Naive Bayes Terhadap Analisis Sentimen Pengguna Aplikasi Shopee,” Indones. J. Comput. Sci., vol. 12, no. 5, pp. 2766–2776, 2023, doi: 10.33022/ijcs.v12i5.3304.

R. Isnaeni, Sudarmin, and R. Zulkifli, “Analisis Support Vector Regression (SVR) Dengan Kernel Radial Basis Function (RBF) Untuk Memprediksi Laju Inflasi Di Indonesia,” VARIANSI J. Stat. Its Appl. Teach. Res., vol. 4, no. 1, pp. 30–38, 2022, doi: 10.35580/variansiunm13.

Normah, B. Rifai, S. Vambudi, and R. Maulana, “Analisa Sentimen Perkembangan Vtuber Dengan Metode Support Vector Machine Berbasis SMOTE,” J. Tek. Komput. AMIK BSI, vol. 8, no. 2, pp. 174–180, 2022, doi: 10.31294/jtk.v4i2.

R. Apriani and D. Gustian, “Analisis Sentimen dengan Naïve Bayes Terhadap Komentar Aplikasi Tokopedia,” J. Rekayasa Teknol. Nusa Putra, vol. 6, no. 1, pp. 54–62, 2019, [Online]. Available: https://rekayasa.nusaputra.ac.id/article/view/86

A. Fatkhudin, A. Khambali, and F. A. Artanto, “Decision Tree Dalam Mengklasifikasi Mata Kuliah Terhadap Pemahaman Sistem Pemasaran,” J. Ilm. Ilmu Komput. Fak. …, vol. 7, no. 2, pp. 52–55, 2021, [Online]. Available: http://ejournal.fikom-unasman.ac.id/index.php/jikom/article/view/204

I. Afdhal, R. Kurniawan, I. Iskandar, R. Salambue, E. Budianita, and F. Syafria, “Penerapan Algoritma Random Forest Untuk Analisis Sentimen Komentar Di YouTube Tentang Islamofobia,” J. Nas. Komputasi dan Teknol. Inf., vol. 5, no. 1, pp. 122–130, 2022, [Online]. Available: http://ojs.serambimekkah.ac.id/jnkti/article/view/4004/pdf

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Published

2025-07-28

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