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All Journal JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Rahman, Afida
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Computer Science & IT Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering

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E-commerce Transaction Fraud Detection Using the Naive Bayes Algorithm Dautd, Zahri Aksa; Aqmal S, M Fauzan; Sugiarta, Achmad; Rahman, Afida
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 1 (2025): February
Publisher : Lumina Infinity Academy Foundation

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Abstract

This study utilizes the Naive Bayes algorithm to detect fraudulent transactions occurring on e-commerce platforms by analyzing several key attributes, including the transaction time, transaction amount, the user's geographic location, and the payment method used. This algorithm was chosen due to its advantage of simplicity in handling probabilistic-based classification, which facilitates the analysis of complex data. Based on the study's findings, the Naive Bayes model demonstrates a commendable ability with an accuracy rate of 80% in identifying transactions categorized as fraudulent activities. This research contributes valuable insights that can be applied to enhance the security and trust in online transaction systems.
Analysis of Cigarette Sales Transactions Using Apriori Algorithm at Madura Store Mahendra, Mochammad Augustiar; Sa'adah, Mamba'us; Puspitarini, Erri Wahyu; Rahman, Afida
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 2 (2025): June
Publisher : Lumina Infinity Academy Foundation

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Developments in the cigarette industry continue to increase and there are also challenges in classifying cigarette sales. In this case, the method of classifying cigarette sales using the Apriori algorithm can be one way that can be used. The purpose of this study is to identify significant cigarette sales and classify sales transactions based on sales patterns. The method to be used in this study has several stages. First, we collect cigarette sales data from several different cigarette shops. The data includes information such as transaction ID, items purchased, and sales amounts. Then, we pre-process the data to prepare the raw data for further analysis. The results of this study indicate that classifying cigarette sales using the Apriori algorithm is able to identify significant sales patterns and classify transactions with a more adequate level of accuracy. This research provides new insights in analyzing cigarette sales data and can help decision-making in the cigarette industry.
Co-Authors Aqmal S, M Fauzan Dautd, Zahri Aksa Mahendra, Mochammad Augustiar Puspitarini, Erri Wahyu Sa'adah, Mamba'us Sugiarta, Achmad