Sentiment Analysis of Indonesian Society Toward the Launch of iPhone 16 Using Naive Bayes, Random Forest, and KNN Algorithms

Christopher Ezra Manurung; Hendra Mayatopani

doi:10.53697/jkomitek.v5i1.2219

Authors

Christopher Ezra Manurung Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Pradita
Hendra Mayatopani Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Pradita

DOI:

https://doi.org/10.53697/jkomitek.v5i1.2219

Keywords:

iPhone 16, Tweet, Naive Bayes Classifier, Random Forest, KNN

Abstract

The development of smartphone technology, especially involving global brands like Apple, always attracts the attention of the world, including Indonesia. Every time Apple launches a new product, the public's response, particularly in Indonesia, often appears in the form of tweets on the social media platform Twitter, now known as X, which can be analyzed to reflect public views. This phenomenon presents an opportunity to understand how products are received in today's market. The dataset used in this study was obtained from tweets or comments from the Indonesian public between October and November 2024. The study found that 51.49% of the tweets fell into the positive sentiment category, 28.15% were neutral, and 20.35% were negative. Accuracy evaluation using three algorithms showed that Random Forest had the highest accuracy at 72.4%, followed by KNN with an accuracy of 66.9%, and Naïve Bayes with an accuracy of 66.3%. The results of this study indicate that the majority of the Indonesian public showed a positive sentiment toward the launch of the iPhone 16, reflecting high enthusiasm for the product. Furthermore, the Random Forest algorithm proved to be more effective in sentiment classification compared to KNN and Naïve Bayes, with higher accuracy.

References

Adepu Rajesh, M. R., & Hiwarkar, D. T. (2023). Exploring preprocessing techniques for natural language text: A comprehensive study using Python code. International Journal of Engineering Technology and Management Sciences, 7(5), 390-399.

Aliyah, N. A., Azzahra, N. N., Putri, N. A. I., & Rakhmawati, N. N. A. (2024). Analisis sentimen Twitter terhadap tren penyebaran informasi pelaku kejahatan menggunakan algoritma Naives Bayes. Bridge, 2(2), 85-97. https://doi.org/10.62951/bridge.v2i2.63

Atmaja, R. M. R. W. P. K., & Yustanti, W. (2021). Analisis sentimen customer review aplikasi ruang guru dengan metode BERT (Bidirectional Encoder Representations from Transformers). Analisis Sentimen Customer Review Aplikasi Ruang Guru Dengan Metode BERT (Bidirectional Encoder Representations From Transformers), 2(3).

Bourequat, W., & Mourad, H. (2021). Sentiment analysis approach for analyzing iPhone release using support vector machine. International Journal of Advances in Data and Information Systems, 2(1), 36–44. https://doi.org/10.25008/ijadis.v2i1.1216

Chen, X., Liu, Y., & Gong, H. (2021). Apple Inc. Strategic Marketing Analysis and Evaluation. Advances in Economics, Business and Management Research/Advances in Economics, Business and Management Research. https://doi.org/10.2991/assehr.k.211209.499

Cholil, S. R., Handayani, T., Prathivi, R., & Ardianita, T. (2021). Implementasi Algoritma Klasifikasi K-Nearest Neighbor (KNN) untuk klasifikasi seleksi penerima beasiswa. IJCIT (Indonesian Journal on Computer and Information Technology), 6(2). https://doi.org/10.31294/ijcit.v6i2.10438

Dzulhijjah, A. N., & Anraeni, S. (2021). Klasifikasi kematangan citra labu siam menggunakan metode KNN (K-Nearest Neighbor) dengan ekstraksi fitur HSV (Hue, Saturation, Value). Buletin Sistem Informasi dan Teknologi Islam, 2(2), 103–110.

Farhi, F., Jeljeli, R., Zahra, A., Saidani, S., & Feguiri, L. (2023). Factors behind virtual assistance usage among iPhone users: Theory of reasoned action. International Journal of Interactive Mobile Technologies, 17(2), 42–61.

Fauzianto, R. A., & Supatman. (2023). Analisis sentimen opini masyarakat terhadap tech winter pada Twitter menggunakan natural language processing. Jurnal Syntax Admiration, 3(9), 1577–1585. https://doi.org/10.46799/jsa.v3i9.909

Fitriyah, N., Warsito, B., & Maruddani, D. A. I. (2020). Analisis sentimen GoJek pada media sosial Twitter dengan klasifikasi support vector machine (SVM). Jurnal Gaussian, 9(3), 376–390. https://doi.org/10.14710/j.gauss.v9i3.28932

Giovani, A. P., Ardiansyah, A., Haryanti, T., Kurniawati, L., & Gata, W. (2020). Analisis sentimen aplikasi Ruang Guru di Twitter menggunakan algoritma klasifikasi. Jurnal Teknoinfo, 14(2), 115. https://doi.org/10.33365/jti.v14i2.679

Hakim, B. (2021). Analisa sentimen data text preprocessing pada data mining dengan menggunakan machine learning. JBASE - Journal of Business and Audit Information Systems, 4(2). https://doi.org/10.30813/jbase.v4i2.3000

Hidayah, L., & Rosadi, M. I. (2024). Penerapan algoritma Random Forest untuk memprediksi jumlah santri baru. Jurnal Informatika Dan Teknik Elektro Terapan, 12(3S1), 5237. https://doi.org/10.23960/jitet.v12i3s1.5237

Kartika, L. G. S., Utama, P. K. L., Budiastawa, I. D. G., & Rinartha, K. (2023). Comparison of the sentiment analysis model's code complexity and processing time. Sinkron, 8(1), 109–118. https://doi.org/10.33395/sinkron.v8i1.11894

Nugroho, N. A., & Amrullah, N. A. (2023). EVALUASI KINERJA ALGORITMA K-NN MENGGUNAKAN K-FOLD CROSS VALIDATION PADA DATA DEBITUR KSP GALIH MANUNGGAL. Jurnal Informatika Teknologi Dan Sains (Jinteks), 5(2), 294–300. https://doi.org/10.51401/jinteks.v5i2.2506

Prayogo, A., Fauziah, F., & Winarsih, W. (2023). PERBANDINGAN ALGORITMA NAÏVE BAYES DAN K-NEAREST NEIGHBOR PADA KLASIFIKASI JUDUL ARTIKEL PADA JURNAL ILMIAH. JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika), 8(4), 1327–1338. https://doi.org/10.29100/jipi.v8i4.4141

Rifaldi, D., Fadlil, N. A., & Herman, N. (2023). Teknik preprocessing pada text mining menggunakan data tweet 'Mental Health.' Decode Jurnal Pendidikan Teknologi Informasi, 3(2), 161–171. https://doi.org/10.51454/decode.v3i2.131

Sherin, M. J., & Kartheeban, K. (2019). Sentiment scoring and performance metrics Examination of various supervised classifiers. International Journal of Innovative Technology and Exploring Engineering, 9(2S2), 1120–1126. https://doi.org/10.35940/ijitee.b1111.1292s219

Simatupang, M. S., & Purba, H. (2023). THE IMPACT SOCIAL MEDIA MARKETING, SOCIAL INTERACTIVITY AND PERCEIVED QUALITY OF BRAND LOYALTY ON IPHONE USERS. Jurnal Riset Manajemen Dan Akuntansi, 3(1), 125–136.

Tantyoko, H., Sari, D. K., & Wijaya, A. R. (2023). Prediksi potensial gempa bumi Indonesia menggunakan metode random forest dan feature selection. Indonesia Journal Information System, 6(2), 83-89.

Wulandari, N. F., Haerani, N. E., Fikry, N. M., & Budianita, N. E. (2023). Analisis sentimen larangan penggunaan obat sirup menggunakan algoritma naive bayes classifier. Jurnal CoSciTech (Computer Science and Information Technology), 4(1), 88–96.