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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) AMMA : Jurnal Pengabdian Masyarakat
Athhar Hafizha Luthfi
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Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

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Accuracy in Sentiment Analysis of the by.U Application Using Naïve Bayes and SMOTE Techniques Athhar Hafizha Luthfi; 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.v4i2.737

Abstract

Imbalanced data is a significant challenge in sentiment analysis, as it often impacts the performance of machine learning models. This study applies the Naïve Bayes algorithm, enhanced with the Synthetic Minority Oversampling Technique (SMOTE), to address class imbalance in user reviews of the by.U application. Using the Knowledge Discovery in Databases (KDD) framework, the research involves data selection, preprocessing (text cleaning, normalization, stemming), transformation using TF-IDF, and train-test data splitting. SMOTE is applied to the training data to improve minority class representation, while Naïve Bayes performs sentiment classification. Model evaluation using cross-validation demonstrates that SMOTE increases accuracy from 84.42% to 85.83%. These results underscore the effectiveness of integrating SMOTE with Naïve Bayes in addressing imbalanced data, offering meaningful insights into user sentiment and aiding the development of improved features for the by.U application.
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.
Co-Authors Ahmad Faqih Anjar Ayuning Lestari Dodi Solihudin Fatihanursari Dikananda Gifthera Dwilestari