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JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Published by Lumina Infinity Academy
ISSN : -     EISSN : 30903939     DOI : -
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering
JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture The Journal of Information Technology Applications in Education, Economy, Health and Agriculture (JITEEHA), published by the Lumina Infinity Academy Foundation, was established in January 2024. JITEEHA is a rigorously reviewed, double-blind peer-reviewed journal committed to publishing high-quality articles. The focus of the journal encompasses the innovative application of information technology across various sectors including educational technology and management, economic systems, business, finance, healthcare, and agriculture. JITEEHA is published triannually, with issues released in February, June, and October each year. The journal aims to provide a platform for academics, researchers, and practitioners to disseminate their findings and contribute to the advancement of knowledge in these critical fields. This journal is published three issues per year, in February, June, and October.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 5 Documents
 1 
Search results for , issue "Vol. 2 No. 3 (2025): October" : 5 Documents clear
Application of Apriori Algorithm to Find Flower Purchase Patterns Tusianto, Daffa Yauzan; Fairuzabadi, Ahmad; Sujito, Sujito
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 3 (2025): October
Publisher : Lumina Infinity Academy Foundation

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Abstract

This research aims to apply the Apriori algorithm in analyzing flower purchase patterns at a flower shop. Apriori algorithm is used to identify product combinations that are often purchased together, in the hope of finding purchasing patterns that can be utilized to improve marketing strategies and store operational efficiency. Transaction data from the shop is processed to extract frequent itemsets and generate association rules by setting the right threshold of support and confidence values. The results of this study show that flower combinations such as Tulip and Bougenville frequently co-occur in purchases, with significant support-confidence products. These findings provide insights into consumer purchasing behavior that can be used to recommend product bundling or product rearrangement in stores. This research contributes to the application of data mining in the retail sector, particularly in increasing sales and customer satisfaction in flower shops.
Purchase Pattern Analysis on Komol Kopi Transaction Data Using Apriori Algorithm Pratama, Dafa Septian Putra; Praseptiawan, Mugi; Paramita, Niken
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 3 (2025): October
Publisher : Lumina Infinity Academy Foundation

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Abstract

This research aims to analyze purchasing patterns in Komol Kopi transaction data using the Apriori algorithm. This algorithm enables the discovery of relationships between items in large datasets that can be used to support business decisions, such as bundling promotions and inventory management. The dataset includes 12 transactions with various combinations of items, such as Kopi Hitam, Kopi Tubruk, and Nasi Telur. The analysis results show some significant purchase patterns with high support, confidence, and lift values. An example of an association found is between Kopi Hitam and Es Teh, which provides insights for more effective marketing strategies. This study confirms that the Apriori algorithm is an efficient tool in unearthing purchasing patterns, providing a solid foundation for the development of data-driven business strategies. Further research can integrate this analysis with recommendation systems to improve customer experience.
Analysis of Puchase Patterns on Office Stationery Sales Data using Apriori Algorithm Wahyudi, M. Ilham Setyo; Nurdiyansyah, Firman; Kristianti, Dini
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 3 (2025): October
Publisher : Lumina Infinity Academy Foundation

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Abstract

This study analyzes purchasing patterns in office stationery sales using the Apriori algorithm, a data mining method for generating association rules and frequent itemsets. The research examines transaction data to identify combinations of frequently purchased items, aiming to improve inventory management and marketing strategies. The Apriori algorithm calculates metrics such as support, confidence, and lift to determine strong associations between items. Results indicate key purchasing patterns, such as frequent copurchases of notebooks and pencils, which inform targeted promotions and stock planning. The findings highlight the potential of data-driven decisionmaking to enhance business efficiency and customer satisfaction in the retail sector.
Application of Data Mining with Apriori Algorithm on Furniture Sales to Support Business Intelligence Syamsudin, Mochammad; Nathasia, Novi Dian; Kadir, Shaifany Fatriana
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 3 (2025): October
Publisher : Lumina Infinity Academy Foundation

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Abstract

This study explores the application of Data Mining using the Apriori algorithm in furniture sales to support Business Intelligence. The research process includes collecting weekly transaction data, forming frequent itemsets, analyzing association rules using metrics such as support, confidence, and lift, and integrating the results into business strategies. The findings indicate that tables, wardrobes, and bookshelves have the highest purchase rates at 100%, followed by cabinets at 83.33%, chairs at 91.67%, and sofas at 66.67%. Strongly associated itemsets, such as {Table, Bookshelf} and {Wardrobe, Cabinet}, provide valuable insights for business owners in designing marketing strategies, maintaining stock availability, and enhancing customer satisfaction. Utilizing the Apriori algorithm, this study successfully identifies significant purchasing patterns that can be used to drive sustainable business growth in the furniture industry.
Apriori Algorithm and Business Intelligence Methods for Bookstore’s Customer Preferences Analysis Ramadhan, Silmy Kafi; Septiani, Devi; Rahman, Afida; Handayani, Endah Tri Esti
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 3 (2025): October
Publisher : Lumina Infinity Academy Foundation

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Abstract

This study explores the use of a priori algorithm in analyzing sales transaction data at Rony Jaya Bookstore. By combining data mining and business intelligence, the study successfully uncovered significant customer buying patterns, which were then used to support strategic decision-making. The results of the analysis showed that there was a close relationship between certain book categories, such as Fiction Books and Educational Books with a confidence level of 87.5%, as well as Non-Fiction Books and Educational Books with a confidence level of 88.89%. These findings provide valuable insights into developing marketing strategies, such as creating custom promotional packages and arranging product layouts in stores to make them more appealing to customers. This research also highlights the importance of ensuring data quality so that the resulting analysis is more accurate and relevant. Overall, the study offers a practical guide for Rony Jaya Bookstore and other businesses looking to leverage data mining and business intelligence technologies to improve efficiency and customer satisfaction.

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