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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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Abstract

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.
Application of the Naive Bayes Algorithm to Predict The Purchase Decisions Puspitarini, Erri Wahyu; Masdiyanto, Andreas; Kiyosaki, Robert Baz; Hakiki, Sudrajad; Conteh, Alusine; Wafa, Fachrian Muhammad Ahzami
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 1 No. 2 (2024): June
Publisher : Lumina Infinity Academy Foundation

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Abstract

This study applies the Naive Bayes algorithm to predict the decision to purchase used motorcycles based on attributes such as model, year of manufacture, price, engine capacity, and transaction results. Utilizing the Gaussian Naive Bayes approach for continuous data, this research aims to develop a reliable predictive model and understand the most significant attributes influencing purchasing decisions. The test results show that the predictive model achieves an accuracy rate of 75%, indicating the effectiveness of the Naive Bayes algorithm in handling data classification. This study provides insights that can help industry players enhance their sales strategies based on accurate data analysis.