Implementasi Particle Swarm Optimization (PSO) Pada Analysis Sentiment Review Aplikasi Halodoc Menggunakan Algoritma Naïve Bayes

Nuzuliarini Nuris, Eka Rini Yulia, Kusmayanti Solecha

Abstract


Health is very important for humans, if you experience symptoms or feel pain, it is appropriate for us to have a health check and go to a hospital or clinic, but if it is not possible to leave the house, an online health consultation application is considered to be helpful. But before you can use and take advantage of these applications, it is necessary to know reviews from consumers based on positive opinions and negative opinions. This study applies the Naive Bayes algorithm to perform text classification and selects the particle swarm optimazation selection feature to support the increased accuracy obtained. Classification evaluation and validation are performed using confusion matrix and ROC curves. The results showed an increase in accuracy previously 88.50% and AUC 0.535, increased to 90.50% and AUC 0.525. It can be concluded that the selection of the particle swarm optimazation feature has succeeded in increasing the accuracy.

Keywords: selection features, naïve bayes, particle swarm optimization.




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References


Chandra, D. N., Indrawan, G., & Sukajaya, I. N. (2016). Klasifikasi Berita Lokal Radar Malang Menggunakan Metode Naïve Bayes Dengan Fitur N-Gram. Jurnal Ilmiah Teknologi Informasi Asia, 10(1), 11–19.

Ernawati, S. (2016). Penerapan Particle Swarm Optimization Untuk Seleksi Fitur Pada Analisis Sentimen Review Perusahaan Penjualan Online Menggunakan Naïve Bayes. Evolusi, 4(1), 45–54.

Gorunescu, F. (2011). Data Mining Concepts, Models and Techniques.

Hendra, A. (2021). Analisis Sentimen Review Halodoc Menggunakan Nai ̈ ve Bayes Classifier. 6(2), 78–89.

Indrayuni, E. (2019). Klasifikasi Text Mining Review Produk Kosmetik Untuk Teks Bahasa Indonesia Menggunakan Algoritma Naive Bayes. Jurnal Khatulistiwa Informatika, 7(1), 29–36. https://doi.org/10.31294/jki.v7i1.1

Laksana Utama, P. K. (2018). Identifikasi Hoax pada Media Sosial dengan Pendekatan Machine Learning. Widya Duta: Jurnal Ilmiah Ilmu Agama Dan Ilmu Sosial Budaya, 13(1), 69. https://doi.org/10.25078/wd.v13i1.436

Rodiyansyah, S. F., & Winarko, E. (2012). Klasifikasi Posting Twitter Kemacetan Lalu Lintas Kota Bandung Menggunakan Naive Bayesian Classification. FMIPA UGM, 6(1), 91–100.

Salappa, A., Doumpos, M., &Zopounidis, C. (2007). Feature SelectionAlgorithms in Classification Problems:An Experimental Evaluation. SystemsAnalysis, Optimization and Data Mining in Biomedicine. 199–212.

Wati, R. (2020). Penerapan Algoritma Naive Bayes Dan Particle Swarm Optimization Untuk Klasifikasi Berita Hoax Pada Media Sosial. JITK (Jurnal Ilmu Pengetahuan Dan Teknologi Komputer), 5(2), 159–164. https://doi.org/10.33480/jitk.v5i2.1034




DOI: https://doi.org/10.52643/jti.v7i1.1330

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