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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) AMMA : Jurnal Pengabdian Masyarakat
Anjar Ayuning Lestari
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

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Optimalisasi Penggunaan Aplikasi Digital Payment bagi Pedagang Pasar Tradisional Fatihanursari Dikananda; Dodi Solihudin; Anjar Ayuning Lestari; Athhar Hafizha Luthfi
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 04 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

This Community Partnership Program aims to optimize the use of digital payment applications among traditional market traders. Activities include education on the benefits of digital payment, training on application usage, and assistance in non-cash transactions. This program is expected to increase transaction efficiency, security, and financial accessibility for traders, while also promoting financial inclusion in traditional markets.
Improving Sentiment Analysis Performance of Tokopedia Reviews Using Principal Component Analysis and Naïve Bayes Algorithm Anjar Ayuning Lestari; Ahmad Faqih; Gifthera Dwilestari
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.743

Abstract

Tokopedia one of Indonesia's largest e-commerce platforms, offers a wide range of products with diverse customer reviews. These reviews reflect consumer opinions and provide valuable insights for service improvement and marketing strategies. Sentiment analysis is crucial for understanding customer perceptions, but processing large-scale, high-dimensional text data remains a challenge, impacting model efficiency and accuracy. This research uses Principal Component Analysis (PCA) to reduce data dimensionality without losing important information for sentiment classification. The study begins by collecting Tokopedia product reviews and preprocessing the text, including data cleaning, tokenization, stopword removal, and stemming. The reviews are then converted into numerical vectors using the Term Frequency-Inverse Document Frequency (TF-IDF) method. A Gaussian Naïve Bayes model is employed to classify sentiment into three categories: positive, neutral, and negative. The results demonstrate that PCA significantly improves model accuracy from 63.13% to 70.47%, with gains in precision (71.85%), recall (70.47%), and F1-score (71.06%). This research contributes to enhancing sentiment analysis techniques using PCA for Tokopedia reviews and offers a valuable approach that can be applied to other e-commerce platforms.
Co-Authors Ahmad Faqih Athhar Hafizha Luthfi Dodi Solihudin Fatihanursari Dikananda Gifthera Dwilestari