Klasifikasi Naïve Bayes dan Confusion Matrix pada Pengguna Aplikasi E-Commerce di Play Store

Mohamad Rizki Humaidi; Alif Maulani

doi:10.35316/jimi.v8i2.132-139

Mohamad Rizki Humaidi Universitas Pamulang
Alif Maulani Universitas Pamulang

10.35316/jimi.v8i2.132-139

Keywords: Confusion Matrix, E-commerce, Naïve Bayes Classification, Play store, Shopee

Abstract

Shopee is an e-commerce application that is very popular among Indonesians. Shopee is an online shopping service center that is in great demand by Indonesians and offers many types of products. In addition, the shopee application has features, one of which is shopee food and shopee pay, which distinguishes the shopee application from other e-commerce applications. Although there are many Shopee application users, of course not all reviews and ratings given by users are good as a reference for improving the Shopee application features. In conducting research, a method is needed that can classify review data into positive and negative reviews. One that provides a review and rating feature is the Play Store application. In this study, using 1528 review data, Positive and negative class labels are the categories used.. The machine learning classification method used is Naïve Bayes classification. The outcomes of the method's classification accuracy testing are measured using a confusion matrix. So that the accuracy result using Naïve Bayes classification is 0.87 or 87%. Based on these results, using the Naïve Bayes classification gets high accuracy results in the process of classifying review data on Shopee application user research in the Play Store.

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References

K. Laudon C and J. Laudon P, Management Information Systems Thirteenth Edition Global Edition. 2014.

W. Social, Are, “Penggunaan E-Commerce Indonesia Tertinggi di Dunia,” KataData, no. April, p. 2021, 2021, [Online]. Available: https://databoks.katadata.co.id/datapublish/2021/06/04/penggunaan-e-commerce-indonesia-tertinggi-di-dunia.

Romindo et al., E-Commerce: Implementasi, Strategi dan Inovasinya. 2019.

IPrice, “10 E-Commerce dengan Pengunjung Terbanyak Kuartal I 2022,” Katadata, p. 2022, 2022, [Online]. Available: https://databoks.katadata.co.id/datapublish/2022/07/19/10-e-commerce-dengan-pengunjung-terbanyak-kuartal-i-2022.

D. Yani, Deviacita, S. Pratiwi, Helen, and H. Muhardi, “Implementasi Web Scraping untuk Pengambilan Data pada Situs Marketplace,” Jurnal Sistem dan Teknologi Informasi (JUSTIN), vol. 7, no. 4, p. 257, 2019, doi: 10.26418/justin.v7i4.30930.

K. Suryadewiansyah, Muhammad and T. Tju, Eng, Endra, “Naïve Bayes dan Confusion Matrix untuk Efisiensi Analisa Intrusion Detection System Alert,” vol. 08, no. 02, pp. 81–88, 2022.

D. Normawati and S. Prayogi, Aliit, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” vol. 5, no. November 2019, pp. 697–711, 2021.

Riskania and F. Thalib, “Jurnal Teknologi,” Jurnal Teknologi, vol. 8, no. 1, pp. 31–39, 2020, [Online]. Available: www.jurnalteknologi.utm.my.

A. Kusuma and H. N. Irmanda, “Analisis Sentimen Pada Ulasan Aplikasi Indodax di Google Play Store Menggunakan Metode Support Vector Machine,” vol. 3, no. 2, pp. 563–574, 2022.

R. Prasetya, “Data Mining Application on Weather Prediction Using Classification Tree, Naïve Bayes and K-Nearest Neighbor Algorithm With Model Testing of Supervised Learning Probabilistic Brier Score, Confusion Matrix and ROC,” Jaict, vol. 4, no. 2, p. 25, 2020, doi: 10.32497/jaict.v4i2.1690.

I. Cholissodin and A. A. Soebroto, “AI , Machine Learning & Deep Learning ( Teori & Implementasi ),” 2021.