PERFORMANCE COMPARISON OF NAIVE BAYES, SUPPORT VECTOR MACHINE AND RANDOM FOREST ALGORITHMS FOR APPLE VISION PRO SENTIMENT ANALYSIS

Rangga Rizky Pratama; Ryan Randy Suryono

doi:10.52436/1.jutif.2025.6.1.4035

Rangga Rizky Pratama Sistem Informasi, Fakultas Teknik dan Ilmu Komputer, Universitas Teknokrat Indonesia
Ryan Randy Suryono Sistem Informasi, Fakultas Teknik dan Ilmu Komputer, Universitas Teknokrat Indonesia

DOI: https://doi.org/10.52436/1.jutif.2025.6.1.4035

Keywords: Apple Vision Pro, Naive Bayes, Random Forest, Sentimen Analisys, Support Vector Machine

Abstract

With the development of spatial computing devices, there arises a need to analyze consumer opinions about products such as the Apple Vision Pro (AVP), a technology that combines augmented reality (AR) and virtual reality (VR). This study aims to analyze consumer opinions on the Apple Vision Pro by utilizing data from the social media platform X. Three algorithms—Random Forest, Support Vector Machine (SVM), and Naïve Bayes—are used in text categorization to identify sentiment trends. Data was collected through a crawling process, resulting in 3,753 entries. After preprocessing and labeling, 2,609 clean data points were obtained, with 1,618 classified as negative and 991 as positive. In sentiment analysis, Random Forest delivered the best performance with an accuracy of 83%, followed by SVM at 80%, and Naïve Bayes at 75%. These results indicate that the Random Forest algorithm is more effective in sentiment categorization related to Apple Vision Pro. This study provides significant contributions to companies in understanding public perceptions and crafting more precise data-driven marketing strategies.

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References

N. Thakur, “MonkeyPox2022Tweets: A Large-Scale Twitter Dataset on the 2022 Monkeypox Outbreak, Findings from Analysis of Tweets, and Open Research Questions,” Infect Dis Rep, vol. 14, no. 6, pp. 855–883, Dec. 2022, doi: 10.3390/idr14060087.

J. López Díez, “Metaverso: Año Uno. La presentación en vídeo sobre Meta de Mark Zuckerberg (octubre 2021) en el contexto de los estudios previos y prospectivos sobre metaversos,” Pensar la Publicidad. Revista Internacional de Investigaciones Publicitarias, vol. 15, no. 2, pp. 299–303, Dec. 2021, doi: 10.5209/pepu.79224.

S. Stieglitz and L. Dang-Xuan, “Analisis Sentimen Twitter (X) Menggunakan Social Network Analysis Terhadap Kasus Pembunuhan Vin,” Journal of Management Information Systems, vol. 29, no. 4, pp. 217–248, Apr. 2024, doi: 10.2753/MIS0742-1222290408.

S. T. Yun, S. K. Olsen, K. C. Quigley, M. A. Cannady, and A. Hartry, “A Review of Augmented Reality for Informal Science Learning: Supporting Design of Intergenerational Group Learning,” Visitor Studies, vol. 26, no. 1, pp. 1–23, 2023, doi: 10.1080/10645578.2022.2075205.

E. Waisberg et al., “Apple Vision Pro and the advancement of medical education with extended reality,” Can Med Educ J, Aug. 2023, doi: 10.36834/cmej.77634.

E. R. Lidinillah, T. Rohana, and A. R. Juwita, “Analisis sentimen twitter terhadap steam menggunakan algoritma logistic regression dan support vector machine,” TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika, vol. 10, no. 2, pp. 154–164, Jul. 2023, doi: 10.37373/tekno.v10i2.440.

R. N. Ikhsani and F. F. Abdulloh, “Optimasi SVM dan Decision Tree Menggunakan SMOTE Untuk Mengklasifikasi Sentimen Masyarakat Mengenai Pinjaman Online,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 7, no. 4, p. 1667, Oct. 2023, doi: 10.30865/mib.v7i4.6809.

I. Athiyyah Rahma and L. Hulliyyatus Suadaa, “PENERAPAN TEXT AUGMENTATION UNTUK MENGATASI DATA YANG TIDAK SEIMBANG PADA KLASIFIKASI TEKS BERBAHASA INDONESIA STUDI KASUS: DETEKSI JUDUL CLICKBAIT DAN KOMENTAR HATE SPEECH PADA BERITA ONLINE,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 10, no. 6, pp. 1329–1340, 2023, doi: 10.25126/jtiik.2023107325.

A. Felicia Watratan, A. B. Puspita, D. Moeis, S. Informasi, and S. Profesional Makassar, “Implementasi Algoritma Naive Bayes Untuk Memprediksi Tingkat Penyebaran Covid-19 Di Indonesia,” 2020. [Online]. Available: http://journal.isas.or.id/index.php/JACOST

A. Rahman Isnain, A. Indra Sakti, D. Alita, and N. Satya Marga, “SENTIMEN ANALISIS PUBLIK TERHADAP KEBIJAKAN LOCKDOWN PEMERINTAH JAKARTA MENGGUNAKAN ALGORITMA SVM,” JDMSI, vol. 2, no. 1, pp. 31–37, 2021, [Online]. Available: https://t.co/NfhnfMjtXw

F. Azimah and K. Rizky Nova Wardani, “Sistem Pendeteksi Gejala Awal Covid-19 dengan Penggunaan Metode Al Project Cycle,” Journal Locus Penelitian dan Pengabdian, vol. 1, no. 6, pp. 405–418, Sep. 2022, doi: 10.36418/locus.v1i6.135.

P. Elisa and A. Rahman Isnain, “COMPARISON OF RANDOM FOREST, SUPPORT VECTOR MACHINE AND NAIVE BAYES ALGORITHMS TO ANALYZE SENTIMENT TOWARDS MENTAL HEALTH STIGMA,” Jurnal Teknik Informatika (JUTIF), vol. 5, no. 1, pp. 321–329, 2024, doi: 10.52436/1.jutif.2024.5.1.1817.

P. Kumala Sari and R. Randy Suryono, “KOMPARASI ALGORITMA SUPPORT VECTOR MACHINE DAN RANDOM FOREST UNTUK ANALISIS SENTIMEN METAVERSE,” Jurnal MNEMONIC, vol. 7, no. 1, pp. 31–39, 2024.

B. Ramadhani and R. R. Suryono, “Komparasi Algoritma Naïve Bayes dan Logistic Regression Untuk Analisis Sentimen Metaverse,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 8, no. 2, p. 714, Apr. 2024, doi: 10.30865/mib.v8i2.7458.

S. Algifari Rismawan, Y. Syahidin, P. Piksi Ganesha Bandung, J. Gatot Subroto No, and K. Batununggal, “Implementasi Website Berita Online Menggunakan Metode Crawling Data Dengan Bahasa Pemrograman Python,” vol. 10, no. 3, pp. 167–178, 2023, [Online]. Available: http://jurnal.mdp.ac.id

B. A. Yuniarossy et al., “ANALISIS SENTIMEN TERHADAP ISU FEMINISME DI TWITTER MENGGUNAKAN MODEL CONVOLUTIONAL NEURAL NETWORK (CNN),” vol. 5, no. 1, 2024, doi: 10.46306/lb.v5i1.

J. Lasama, A. Pradika, and A. Prasetiadi, “Prediksi Tsunami Pada Gempa Menggunakan Random Forest Classifier,” Conference on Electrical Engineering, Telematics, Industrial Technology, and Creative Media, pp. 32–39, 2019.

P. M. Nirmala Dharmapatni and N. L. P. Merawati, “Penerapan Algoritma Support Vector Machine Dalam Sentimen Analisis Terkait Kenaikan Tarif BPJS Kesehatan,” Jurnal Bumigora Information Technology (BITe), vol. 2, no. 2, pp. 105–112, Sep. 2020, doi: 10.30812/bite.v2i2.904.

U. Banten Jaya, J. Syeh Nawawi Albantani, and S. -Banten, “PERBANDINGAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE (SVM) DALAM KLASIFIKASI SMS SPAM BERBAHASA INDONESIA,” Jurnal Sains & Teknologi, vol. 3, no. 2, pp. 178–194, 2019.

K. M. Hana, Adiwijaya, S. Al Faraby, and A. Bramantoro, “Multi-label Classification of Indonesian Hate Speech on Twitter Using Support Vector Machines,” in 2020 International Conference on Data Science and Its Applications, ICoDSA 2020, Institute of Electrical and Electronics Engineers Inc., Aug. 2020. doi: 10.1109/ICoDSA50139.2020.9212992.

A. Nofandi, N. Y. Setiawan, and D. W. Brata, “Analisis Sentimen Ulasan Pelanggan dengan Metode Support Vector Machine (SVM) untuk Peningkatan Kualitas Layanan pada Restoran Warung Wareg,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 7, no. 1, pp. 458–466, 2023, [Online]. Available: http://j-ptiik.ub.ac.id

M. I. Fikri, T. S. Sabrila, Y. Azhar, and U. M. Malang, “Perbandingan Metode Naïve Bayes dan Support Vector Machine pada Analisis Sentimen Twitter,” SMATIKA Jurnal, vol. 10, no. 02, pp. 71–76, 2020.

A. Ilham and W. Pramusinto, “3 rd Seminar Nasional Mahasiswa Fakultas Teknologi Informasi (SENAFTI) 30 Agustus 2023-Jakarta,” Seminar Nasional Mahasiswa Fakultas Teknologi Informasi (SENAFTI), vol. 2, no. 2, pp. 539–547, 2023, Accessed: Nov. 18, 2024. [Online]. Available: https://senafti.budiluhur.ac.id/index.php/senafti/index

D. Apriliani, A. Susanto, M. Fikri Hidayattullah, and G. Wiro Sasmito, “Sentimen Analisis Pandangan Masyarakat Terhadap Vaksinasi Covid 19 Menggunakan K-Nearest Neighbors,” Jurnal Informatika: Jurnal pengembangan IT (JPIT, vol. 8, no. 1, pp. 34–37, 2023.

P. Elisa and A. Rahman Isnain, “COMPARISON OF RANDOM FOREST, SUPPORT VECTOR MACHINE AND NAIVE BAYES ALGORITHMS TO ANALYZE SENTIMENT TOWARDS MENTAL HEALTH STIGMA,” Jurnal Teknik Informatika (JUTIF), vol. 5, no. 1, pp. 321–329, 2024, doi: 10.52436/1.jutif.2024.5.1.1817.

Hermanto, A. Y. Kuntoro, T. Asra, E. B. Pratama, L. Effendi, and R. Ocanitra, “Gojek and Grab User Sentiment Analysis on Google Play Using Naive Bayes Algorithm and Support Vector Machine Based Smote Technique,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Nov. 2020. doi: 10.1088/1742-6596/1641/1/012102.

M. Fachriza and H. Artikel, “url : http://studentjournal.umpo.ac.id/index.php/komputek Analisis Sentimen Kalimat Depresi Pada Pengguna Twitter Dengan Naive Bayes, Support Vector Machine, Random Forest,” Komputek, vol. 7, no. 2, pp. 49–58, 2023, [Online]. Available: http://studentjournal.umpo.ac.id/index.php/komputek

R. Tesalonika and E. Mailoa, “IMPLEMENTASI ALGORITMA NAIVE BAYES UNTUK ANALISIS SENTIMEN ISU RESESI EKONOMI 2023 DI INDONESIA PADA PLATFORM TWITTER,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 9, no. 1, pp. 34–40, Feb. 2024, doi: 10.29100/jipi.v9i1.4288.

M. Anwar Sadat, P. Pujiono, A. Pambudi, and S. Ibad, “COMPARISON OF ALGORITHM BETWEEN CLASSIFICATION & REGRESSION TREES AND SUPPORT VECTOR MACHINE IN DETERMINING STUDENT ACCEPTANCE IN STATE UNIVERSITIES,” Jurnal Teknik Informatika (Jutif), vol. 4, no. 6, pp. 1589–1604, Jan. 2024, doi: 10.52436/1.jutif.2023.4.6.1565.

Suci Amaliah, M. Nusrang, and A. Aswi, “Penerapan Metode Random Forest Untuk Klasifikasi Varian Minuman Kopi di Kedai Kopi Konijiwa Bantaeng,” VARIANSI: Journal of Statistics and Its application on Teaching and Research, vol. 4, no. 3, pp. 121–127, Dec. 2022, doi: 10.35580/variansiunm31.