Analisis Sentimen Review Produk Skincare Dengan Naïve Bayes Classifier Berbasis Particle Swarm Optimization (PSO)

Authors

  • Tri Astuti Universitas Amikom Yogyakarta, Yogyakarta
  • Yuli Astuti Universitas Amikom Yogyakarta, Yogyakarta

DOI:

https://doi.org/10.30865/mib.v6i4.4119

Keywords:

Naive Bayes, Particle Swarm Optimization, Sentiment Analysis, Review, Cross Validation

Abstract

Skin care products have become the main needs of all people who are the targets of various brands of skin care products. However, not all skin care products have good quality according to consumer needs. They look for products that have the best quality by looking at reviews from other people, so they have an idea that influences their interest from other people's reviews submitted through various marketplace platforms or social media regarding the results after using these skin care products. Sentiment analysis is one way to analyze and classify reviews into positive opinions and negative opinions regarding the product in question to look for product quality based on public views. The algorithm used in this research is the Naive Bayes Classifier. The Naive Bayes Classifier method was chosen for reasons of ease of implementation, fast and high accuracy. The Naïve Bayes method also has a disadvantage, namely it is sensitive to feature selection, which results in low classification accuracy. Therefore, in this study, the feature selection method, namely Particle Swarm Optimization, was used in order to increase the accuracy of the Naïve Bayes classifier. The dataset used is 800 data reviews and tested using 10-Fold Cross Validation. The results showed an increase in accuracy from 77.96% to 79.85%.

References

F. Latief and N. Ayustira, “Pengaruh Online Costumer Review Dan Customer Rating Terhadap Keputusan Pembelian Produk Kosmetik Di Sociolla,†J. Mirai Manag., vol. 4, no. 2, pp. 122–136, 2019, [Online]. Available: https://journal.stieamkop.ac.id/index.php/mirai.

D. Cahya Purnomo, M. Yanti, and A. P. Widyassari, “Bab 1 Latar Belakang Kulit, Skincare,†J. Ilm. Intech Inf. Technol. J. UMUS, vol. 3, no. 01, pp. 32–41, 2021.

D. N. Sari, D. N. Sari, F. Adelia, F. Rosdiana, B. B. Butar, and M. Hariyanto, “Analisa Sentimen Terhadap Review Produk Kecantikan Menggunakan Metode Naive Bayes Classifier,†JIKA (Jurnal Inform., vol. 4, no. 3, p. 109, 2020, doi: 10.31000/jika.v4i3.3086.

N. B. N-gram, I. Pujadayanti, M. A. Fauzi, and Y. A. Sari, “Prediksi Rating Otomatis pada Ulasan Produk Kecantikan dengan Metode Prediksi Rating Otomatis pada Ulasan Produk Kecantikan dengan Metode Naïve Bayes dan N-gram,†no. April, 2018.

C. H. Yutika, A. Adiwijaya, and S. Al Faraby, “Analisis Sentimen Berbasis Aspek pada Review Female Daily Menggunakan TF-IDF dan Naïve Bayes,†J. Media Inform. Budidarma, vol. 5, no. 2, p. 422, 2021, doi: 10.30865/mib.v5i2.2845.

D. C. Oktaviana et al., “Rate Prediction of Cosmetic Product Based on Test Review from W ebsite F emale D ailyUsing N aive B ayes C lassifier Sari Widya Sihwi,†vol. 8, no. 1, 2019.

B. Gunawan, H. S. Pratiwi, and E. E. Pratama, “Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes,†J. Edukasi dan Penelit. Inform., vol. 4, no. 2, p. 113, 2018, doi: 10.26418/jp.v4i2.27526.

E. Indrayuni, “Klasifikasi Text Mining Review Produk Kosmetik Untuk Teks Bahasa Indonesia Menggunakan Algoritma Naive Bayes,†J. Khatulistiwa Inform., vol. 7, no. 1, pp. 29–36, 2019, doi: 10.31294/jki.v7i1.1.

V. Kevin, S. Que, A. Iriani, and H. D. Purnomo, “Analisis Sentimen Transportasi Online Menggunakan Support Vector Machine Berbasis Particle Swarm Optimization ( Online Transportation Sentiment Analysis Using Support Vector Machine Based on Particle Swarm Optimization ),†vol. 9, no. 2, pp. 162–170, 2020.

F. Ratnawati, “Implementasi Algoritma Naive Bayes Terhadap Analisis Sentimen Opini Film Pada Twitter,†INOVTEK Polbeng - Seri Inform., vol. 3, no. 1, p. 50, 2018, doi: 10.35314/isi.v3i1.335.

A. D. Cahyani and T. Mardiana, “Sentiment Analysis of Digital Wallet Service Users Using Naïve Bayes Classifier and Particle Swarm Optimization,†J. Ris. Inform., vol. 2, no. 4, pp. 241–250, 2020, doi: 10.34288/jri.v2i4.160.

A. Taufik, “Optimasi Particle Swarm Optimization Sebagai Seleksi Fitur Pada Analisis Sentimen Review Hotel Berbahasa Indonesia Menggunakan Algoritma Naïve Bayes,†J. Tek. Komput., vol. III, no. 2, pp. 40–47, 2017.

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.

N. Hayatin, G. I. Marthasari, and L. Nuraini, “Optimization of Sentiment Analysis for Indonesian Presidential Election using Naïve Bayes and Particle Swarm Optimization,†J. Online Inform., vol. 5, no. 1, pp. 81–88, 2020, doi: 10.15575/join.v5i1.558.

R. N. Handayani, “Optimasi Algoritma Support Vector Machine untuk Analisis Sentimen pada Ulasan Produk Tokopedia Menggunakan PSO,†Media Inform., vol. 20, no. 2, pp. 97–108, 2021, doi: 10.37595/mediainfo.v20i2.59.

Ratino, N. Hafidz, S. Anggraeni, and W. Gata, “Sentimen Analisis Informasi Covid-19 menggunakan Support Vector Machine dan Naïve Bayes,†J. JUPITER, vol. 12, no. 2, pp. 1–11, 2020.

A. Novrisal, G. I. Marthasari, and C. Aditya, “Sentimen Analisis Tweet Berbahasa Indonesia Pada Pilkada Serentak 2020 Menggunakan Metode Naive Bayes Berbasis Particle Swarm Optimization,†J. Repos., vol. 3, no. 2, pp. 191–198, 2021, doi: 10.22219/repositor.v3i2.1169.

A. W. Attabi, L. Muflikhah, and M. A. Fauzi, “Penerapan Analisis Sentimen untuk Menilai Suatu Produk pada Twitter Berbahasa Indonesia dengan Metode Naïve Bayes Classifier dan Information Gain,†J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 11, pp. 4548–4554, 2018.

R. Rinawati, “Penentuan Penilaian Kredit Menggunakan Metode Naive Bayes Berbasis Particle Swarm Optimization,†J-SAKTI (Jurnal Sains Komput. dan Inform., vol. 1, no. 1, p. 48, 2017, doi: 10.30645/j-sakti.v1i1.28.

Downloads

Published

2022-10-25

Issue

Vol. 6 No. 4 (2022): Oktober 2022

Section

Articles

License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).