Analisis Sentimen Ulasan Aplikasi Shopee Menggunakan Algoritma Random Forest, Naïve Bayes, dan Support Vector Machine di Kota Semarang

Vinsent Brilian Adiguna ; Ryan Arya Pramudya
Digital Business Intelligence Journal 📅 2024 Vol 1, No 1
Abstract
The growth of e-commerce in Indonesia has led to the emergence of various online shopping platforms, with Shopee being one of the most popular in Semarang City. User reviews on the Shopee application serve as a valuable data source for analyzing customer satisfaction levels; however, the large volume of data requires a systematic and accurate analytical approach. This study aims to analyze user review sentiments of the Shopee application using three machine learning algorithms: Random Forest, Naïve Bayes, and Support Vector Machine (SVM), as well as comparing the accuracy of these three algorithms. This research utilized 1000 reviews collected through web scraping from the Play Store, which were categorized into three classifications: positive, neutral, and negative sentiments. The analysis process encompassed pre-processing stages, feature extraction using TF-IDF, and classification using Random Forest, Naïve Bayes, and Support Vector Machine algorithms. The results demonstrated that the Random Forest algorithm achieved the highest accuracy at 96.19%, followed by Support Vector Machine with 95.71% accuracy, and Naïve Bayes with 84.76% accuracy. This research highlights the effectiveness of Random Forest and SVM in classifying user review sentiments towards the Shopee application.
Keywords
TF-IDF; Analisis sentiment; Support Vector Machine; Naive Bayes
How to Cite

Vinsent Brilian Adiguna & Ryan Arya Pramudya (2024). Analisis Sentimen Ulasan Aplikasi Shopee Menggunakan Algoritma Random Forest, Naïve Bayes, dan Support Vector Machine di Kota Semarang. Digital Business Intelligence Journal, 1(1).

Vinsent Brilian Adiguna; Ryan Arya Pramudya, "Analisis Sentimen Ulasan Aplikasi Shopee Menggunakan Algoritma Random Forest, Naïve Bayes, dan Support Vector Machine di Kota Semarang," Digital Business Intelligence Journal, vol. 1, no. 1, 2024.

Vinsent Brilian Adiguna; Ryan Arya Pramudya. "Analisis Sentimen Ulasan Aplikasi Shopee Menggunakan Algoritma Random Forest, Naïve Bayes, dan Support Vector Machine di Kota Semarang." Digital Business Intelligence Journal, vol. 1, no. 1, 2024.

Vinsent Brilian Adiguna; Ryan Arya Pramudya. "Analisis Sentimen Ulasan Aplikasi Shopee Menggunakan Algoritma Random Forest, Naïve Bayes, dan Support Vector Machine di Kota Semarang." Digital Business Intelligence Journal 1, no. 1 (2024).

Vinsent Brilian Adiguna & Ryan Arya Pramudya (2024) 'Analisis Sentimen Ulasan Aplikasi Shopee Menggunakan Algoritma Random Forest, Naïve Bayes, dan Support Vector Machine di Kota Semarang', Digital Business Intelligence Journal, 1(1).

Vinsent Brilian Adiguna; Ryan Arya Pramudya. Analisis Sentimen Ulasan Aplikasi Shopee Menggunakan Algoritma Random Forest, Naïve Bayes, dan Support Vector Machine di Kota Semarang. Digital Business Intelligence Journal. 2024;1(1).

Artikel Terkait

The effect of discounts on consumer purchase decision indices: a case study of e-commerce using secondary data

Zubair, Ahmad; Zubair, Ahmad; Sunarka, Puji Setya; Aulia Indriawati

Comparison of Multiple Linear Regression and Random Forest for Predicting Student Final Grades Using Google Colab

Nuraini, Laili; Nuraini, Laili ; Fatma Ayu Widyoputri, Yohana Maritza; Adiguna, Vinsent Brilian

Comparative Analysis of Multiple Linear Regression and Random Forest in Predicting Student Academic Performance

Inabah, Sekar Farahdila; Inabah, Sekar Farahdila; Putri, Imelda Adelia; Mutiarachim, Atika

Internet of Things Vulnerabilities in E-Commerce Supply Chains: A Literature Review Approach to Identifying and Addressing Security Risks

Putri, Imelda Adelia; Putri, Imelda Adelia; Amsar, Amsar

Building Digital Trust: The Role of Cybersecurity in the Sustainability of E-Business - A Systematic Literature Review

Inabah, Sekar Farahdila; Inabah, Sekar Farahdila; Priyambodo, Syahtri Wardana

Cyber Incident Risk Management: Minimizing Financial and Reputational Losses for E-Businesses

Indriawati, Aulia; Indriawati, Aulia ; Mahesa, Deewar
Tren Sitasi Jurnal