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All Journal International Journal of Science and Environment
Handala Simetris Harahap
Program Studi Teknik Informatika,Universitas Pelita Bangsa, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Mathematics Physics

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Application of Apriori Algorithm To Determine Product Purchase Patterns In Online Stores Using The Kdd Method Shanti Cahyaningtyas; Asep Arwan Sulaeman; Handala Simetris Harahap
International Journal of Science and Environment (IJSE) Vol. 6 No. 2 (2026): May 2026
Publisher : CV. Inara in Colaboration with www.stie-sampit.ac.id

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijse.v6i2.643

Abstract

The growth of e-commerce businesses has led to an increasing volume of sales transaction data stored in Online shops. However, this transaction data is often underutilized, even though it contains valuable product purchasing patterns that can support business decision-making. This study aims to apply the Apriori algorithm to identify product purchasing patterns in an Online shop using the Knowledge Discovery in Databases (KDD) method. The KDD framework is employed to ensure a systematic data analysis process, including data Selection, Preprocessing, data Transformation, application of the Apriori algorithm, and result interpretation. The data used in this study consist of Online retail transaction data obtained from the Kaggle platform, namely the Online Retail dataset, which represents real ecommerce transactions. Data processing is carried out using WEKA software. The analysis focuses on discovering frequent item sets and generating association rules based on minimum support, confidence, and lift ratio values. The results show that the Apriori algorithm can identify products that are frequently purchased together within a single transaction. These purchasing patterns can be utilized as a basis for marketing strategy recommendations, product bundling promotions, more efficient inventory management, and as support for developing product recommendation systems in Online shops.
Implementation of a Province Clustering Dashboard Based on IPM and Smoking Prevalence 2024 Using K-Means with KNN Validation as a Support for Data-Driven Decision Making Indry Widiyani; Asep Arwan Sulaeman; Handala Simetris Harahap
International Journal of Science and Environment (IJSE) Vol. 6 No. 2 (2026): May 2026
Publisher : CV. Inara in Colaboration with www.stie-sampit.ac.id

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51601/ijse.v6i2.649

Abstract

Differences in the Human Development Index (HDI) and smoking rates across provinces in Indonesia indicate variations in social conditions that need to be systematically analyzed. This study aims to implement an interactive dashboard to cluster provinces based on their HDI and smoking rates for the year 2024 using the K-Means algorithm, and to validate the clustering results using K-Nearest Neighbor (KNN). The research data were obtained from the Central Statistics Agency (BPS) and the Regional Management Information System (SIMREG-Bappenas) and processed using RapidMiner Studio. The research stages included data cleaning, normalization using Z-transformation, clustering with K-Means into three clusters, validation using KNN via cross-validation, and the implementation of a Streamlit-based dashboard. The results show that provinces in Indonesia can be grouped into three clusters with distinct characteristics based on HDI values and smoking rates. The developed dashboard presents the analysis results in the form of tables, graphs, and interactive maps, thereby facilitating data interpretation and supporting data-driven decision-making. Validation results indicate that the clustering model exhibits a high level of consistency, making it suitable as a basis for formulating policy recommendations regarding regional development and public health.
Co-Authors Asep Arwan Sulaeman Indry Widiyani Shanti Cahyaningtyas