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All Journal Electronic Journal of Education, Social Economics and Technology
Sudi Suryadi
Faculty of Science and Technology, Universitas Labuhanbatu, Sumatera Utara
Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Veterinary Other

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Implementation of Apriori and Fp-Growth Algorithms in Analyzing Sales Patterns on Sekojab's Moving Coffee Rezky Maulana; Sudi Suryadi; Syaiful Zuhri Harahap; Angga Putra Juledi
Electronic Journal of Education, Social Economics and Technology Vol 7, No 1 (2026)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v7i1.1394

Abstract

Advances in information technology have encouraged the use of sales data as a strategic source of information for micro-enterprises. Kopi Keliling Sekojab, a micro-enterprise, generates sales transaction data that has the potential to be analyzed to identify consumer purchasing patterns. However, this data is generally used for administrative purposes without in-depth analysis. This study aims to analyze sales patterns at Kopi Keliling Sekojab by implementing the Apriori and FP-Growth algorithms. The research method used is data mining with a quantitative approach, through the Knowledge Discovery in Database (KDD) stages, which include data collection, pre-processing, data transformation, algorithm application, and analysis of the results. The analyzed data consisted of 30 sales transactions, which were processed to determine support and confidence values to form association rules. The results show that the Apriori and FP-Growth algorithms are capable of identifying customer purchasing patterns, with FP-Growth generating more and more efficient association rules than Apriori. The obtained patterns can be utilized by Kopi Keliling Sekojab businesses in developing sales strategies, stock management, and data-driven service improvements.
Analysis of Generation Z Parenting Styles and Children's Educational Awareness Using Decision Tree and K-Means Methods Novi Syahfitri; Sudi Suryadi; Budianto Bangun; Angga Putra Juledi
Electronic Journal of Education, Social Economics and Technology Vol 7, No 1 (2026)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v7i1.1402

Abstract

This study analyzes how Generation Z parents' parenting styles and technology supervision influence children's educational awareness. The research applied a data mining approach within the Knowledge Discovery in Databases (KDD) framework using Decision Tree and K-Means algorithms. Data were collected through questionnaires from 10 parents in Sumberjo Village. The Decision Tree results show that technology use and supervision provide the highest information gain, indicating that they are the most influential factors in determining children's educational awareness. K-Means clustering with K = 3 also shows that groups characterized by better technology supervision and stronger parenting patterns tend to have higher educational awareness. Validation using RapidMiner produced results that were consistent with the manual calculations, confirming that the analytical model is valid for the dataset used in this study.
Co-Authors Angga Putra Juledi Budianto Bangun Novi Syahfitri Rezky Maulana Syaiful Zuhri Harahap