Analisis Sentimen Kepuasan Pengguna Aplikasi Whatsapp Menggunakan Algoritma NaÃ¯ve Bayes Dan Support Vector Machine

Acep Saepulrohman; Sudin Saepudin; Dudih Gustian

doi:10.34010/aisthebest.v6i2.4919

[1] A. Habibie et al., “Analisis Preferensi Konsumen Dalam Memilih Smartphone Di Indonesia Consumer Analysis of Preferences in Choosing Smartphone in,” vol. 7, no. 1, pp. 114–124, 2020.
[2] Newzoo, “Top Countries by Smartphone Users.” .
[3] Supriyati, R. S. Bahri, and E. Komarudin, “Computerized of International Financial Report Standard for Good Governance in Small Medium Enterpreses,” in IOP Conference Series: Materials Science and Engineering, 2019, vol. 662, no. 5, doi: 10.1088/1757-899X/662/5/052009.
[4] V. Cipher, A. E. S. Dan, R. C. Untuk, and A. Pesan, “Implementasi Kriptografi Dengan Algoritma,” vol. 1, no. 3, pp. 931–937, 2018.
[5] Y. Pusparisa, “Whatsapp Kuasai Pasar Aplikasi Pesan Instan Global 2020,” Databoks, 2020. .
[6] F. Marleny and Mambang, “Analisis Pengguna Whatsapp Terhadap Kesalahan Mengirim Pesan Teks Menggunakan Metode Klasifikasi,” J. Teknol. Inf. Univ. Lambung Mangkurat, vol. 5, no. 1, pp. 19–24, 2020, doi: 10.20527/jtiulm.v5i1.43.
[7] T. S. Gunardi, Dedi Sulistiyo S, “Usulan Peta Strategis Teknologi Informasi Menggunakan Pendekatan Analisis Critical Success Factor (CSF) dan IT Balanced Scorecard,” Http://Jurnal.Unikom.Ac.Id/Jurnal/Usulan-Peta-Strategi.2V, vol. 10, no. 1, pp. 143–153, 2012.
[8] A. P. Giovani, A. Ardiansyah, T. Haryanti, L. Kurniawati, and W. Gata, “Analisis Sentimen Aplikasi Ruang Guru Di Twitter Menggunakan Algoritma Klasifikasi,” J. Teknoinfo, vol. 14, no. 2, p. 115, 2020, doi: 10.33365/jti.v14i2.679.
[9] A. Erfina and Y. H. Putra, “Irony Sentence Detection Techniques Using Fuzzy Historical Classifier,” IOP Conf. Ser. Mater. Sci. Eng., vol. 662, no. 6, 2019, doi: 10.1088/1757-899X/662/6/062004.
[10] D. C. Rini, Y. Farida, and D. Puspitasari, “Klasifikasi Menggunakan Metode Hybrid Bayessian-Neural Network (Studi Kasus: Identifikasi Virus Komputer),” J. Mat. “MANTIK,” vol. 1, no. 2, p. 38, 2016, doi: 10.15642/mantik.2016.1.2.38-43.
[11] A. P. Fadillah and B. Hardiyana, “Penerapan Naïve Bayes Classifier Untuk Pemilihan Konsentrasi Mata Kuliah,” J. Teknol. dan Inf. UNIKOM, vol. 8, no. 2, 2018, doi: 10.34010/jati.v8i2.1039.
[12] A. R. T. Lestari, R. S. Perdana, and M. A. Fauzi, “Analisis Sentimen Tentang Opini Pilkada DKI 2017 Pada Dokumen Twitter Berbahasa Indonesia Menggunakan Näive Bayes dan Pembobotan Emoji,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 1, no. 12, pp. 1718–1724, 2017.
[13] D. J. Haryanto, L. Muflikhah, and M. A. Fauzi, “Analisis Sentimen Review Barang Berbahasa Indonesia Dengan Metode Support Vector Machine Dan Query Expansion,” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 2, no. 9, pp. 2909–2916, 2018.
[14] I. Santoso, W. Gata, and A. B. Paryanti, “Penggunaan Feature Selection di Algoritma Support Vector Machine untuk,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 10, pp. 5–11, 2019.
[15] K. A. Rokhman, B. Berlilana, and P. Arsi, “Perbandingan Metode Support Vector Machine dan Decision Tree untuk Analisis Sentimen Review Komentar Pada Aplikasi Transportasi Online,” J. Inf. Syst. Manag., vol. 2, no. 2, pp. 1–7, 2021, doi: https://doi.org/10.24076/joism.2021v3i1.341.
[16] S. A. Aaputra, “Sentiment Analysis Analisis Sentimen E-Wallet Pada Google Play Menggunakan Algoritma Naive Bayes Berbasis Particle Swarm Optimization,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 3, pp. 377–382, 2019.
[17] R. Tineges, A. Triayudi, and I. D. Sholihati, “Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM),” J. Media Inform. Budidarma, vol. 4, no. 3, p. 650, 2020, doi: 10.30865/mib.v4i3.2181.
[18] U. Rofiqoh, R. S. Perdana, and M. A. Fauzi, “Analisis Sentimen Tingkat Kepuasan Pengguna Penyedia Layanan Telekomunikasi Seluler Indonesia Pada Twitter Dengan Metode Support Vector Machine dan Lexion Based Feature,” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 1, no. 12, pp. 1725–1732, 2017.
[19] D. Gustian, I. Suciati, and S. Saepudin, “Sistem Pakar Dengan Algoritma Naive Bayes Untuk Prediksi Hasil Produksi Ayam Broiler Plasma ( Studi Kasus : Pt . Sekawan Sinar Surya ),” no. 12. .
[20] D. A. Kristiyanti, A. H. Umam, M. Wahyudi, R. Amin, and L. Marlinda, “Comparison of SVM Naïve Bayes Algorithm for Sentiment Analysis Toward West Java Governor Candidate Period 2018-2023 Based on Public Opinion on Twitter,” 2018 6th Int. Conf. Cyber IT Serv. Manag. CITSM 2018, no. Citsm, pp. 1–6, 2019, doi: 10.1109/CITSM.2018.8674352.
[21] S. Sarafrazi and H. Nezamabadi-Pour, “Facing the classification of binary problems with a GSA-SVM hybrid system,” Math. Comput. Model., vol. 57, no. 1–2, pp. 270–278, 2013, doi: 10.1016/j.mcm.2011.06.048.

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Affiliations

Acep Saepulrohman
Universitas Nusa Putra

Sudin Saepudin
Universitas Nusa Putra

Dudih Gustian
Universitas Nusa Putra

How to Cite

[1]

A. Saepulrohman, S. Saepudin, and D. Gustian, “Sentiment Analysis of Whatsapp Application User Satisfaction Using the Naive Bayes Algorithm and Support Vector Machine”, aisthebest, vol. 6, no. 2, pp. 91-105, Dec. 2021.

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Sentiment Analysis of Whatsapp Application User Satisfaction Using the Naive Bayes Algorithm and Support Vector Machine

Acep Saepulrohman ,
Sudin Saepudin ,
Dudih Gustian

Vol 6 No 2 (2021): @is The Best : Accounting Information Systems and Information Technology Business Enterprise

DOI 10.34010/aisthebest.v6i2.4919

Abstract

Information and communication technology was currently growing rapidly, one of which was chat or instant messaging applications such as whatsapp, line and telegram. In october 2020, the majority of instant messaging app users was whatsapp app users, with a total of 2 billion users. Even though the whatsapp application was in the top ranking and got the highest score, but this could not been used as a measured of satisfaction because there were still negative views on the whatsapp application, some users assumed that whatsapp often had errors when used, then other problems that arise such as the network that the user used was unstable. To conduct an analysis of this, a sentiment analysis approached was needed to categorize user comments into positive or negative. This studied used the naÃ¯ve bayes algorithm with support vector machine in analyzing positive and negative comments on the satisfaction of users of the whatsapp application on the google played store. From the results of tests carried out on 1500 user commented data, the evaluation of the model used 10 folded crossed validation shows that the leveled of accuracy for whatsapp application user satisfaction based on the naÃ¯ve bayes algorithm was 70. 40% and support vector machine was 77. 00%, while the auc valued naÃ¯ve bayes was 0. 585 and support vector machine was 0. 876. From these results, the svm algorithm could been used for researched with the same data characteristics.