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All Journal Journal of Student Research Exploration
Rahman, Raihan Muhammad Rizki
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Online payment fraud prediction with machine learning approach using naive bayes algorithm Rahman, Raihan Muhammad Rizki; Muslim, Much Aziz
Journal of Student Research Exploration Vol. 2 No. 2: July 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v2i2.343

Abstract

The increase in e-commerce has provided easy access for the public, but it also opens up opportunities for fraud in online transactions. Payment fraud is also a problem that often arises in transactions through electronic media. This research aims to analyze payment fraud in e-commerce transactions. This research uses a machine learning approach using the Naive Bayes algorithm. This research uses online transaction datasets involving various attributes such as payment and shipping methods. The developed Naive Bayes model achieved an accuracy of 61.03% with K = 7. The evaluation shows a balance between precision (59.46%) and recall (62.05%), although this study is limited by data quality and basic assumptions of Naive Bayes. In future research, it is worth considering the use of additional features or more complex data processing to improve the performance of fraud detection in online transactions. This research provides important insights in the fight against financial crime in the context of electronic commerce.
Implementation of Lexicon-Based and SVM Methods in Sentiment Analysis of Sayurbox App Users Rahman, Raihan Muhammad Rizki; Prasetiyo, Budi
Journal of Student Research Exploration Vol. 4 No. 1 (2026): January 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v4i1.391

Abstract

The ever-growing technology certainly produces a large amount of data, which can provide useful information if analyzed and used properly. The purpose of this research is to analyze user sentiment towards the Sayurbox application on the Google Play Store with a Lexicon-Based approach and the Support Vector Machine (SVM) algorithm. User review data is obtained through web scraping with a total of 16,468 reviews. After preprocessing and sentiment labeling, training and test data were divided. The results showed that SVM achieved accuracy, recall, and precision of 94%, 96%, and 96% respectively, with 9 prediction errors. The model tends to predict reviews as positive sentiment, indicating user satisfaction with Sayurbox's product service, delivery, quality, and price. The findings make a contribution to the understanding of user sentiment in e-commerce services and can assist Sayurbox in improving their user experience.
Co-Authors Budi Prasetiyo, Budi Much Aziz Muslim