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Abstract

This study aims to analyze consumer purchasing patterns and compare the performance of Apriori and FP-Growth algorithms using sales transaction data from MNNZR.ID shoe store. A quantitative comparative approach was applied to 520 transaction records collected between June 2023 and January 2025. The data were preprocessed and transformed into a market basket format using one-hot encoding, followed by association rule mining with variations in minimum support and confidence. The results indicate that both algorithms generate identical association rules with similar values of support, confidence, and lift. The strongest rule found is (NB, Adidas, Puma) to Nike, with a confidence of 52.63% and a lift value greater than 1, indicating a positive correlation. However, FP-Growth demonstrates better computational efficiency compared to Apriori. These findings show that association rule mining can effectively support data-driven marketing strategies such as product bundling and cross-selling in retail businesses.

Keywords

Data Mining Association Rule Apriori Algorithm FP-Growth Algorithm Market Basket Analysis

How to Cite

Citation Format
Nizar, M. N., Sarwido, S., & Sucipto, A. (2026). Analisis Data Penjualan Sepatu pada Toko MNNZR.ID Menggunakan Algoritma Apriori dan FP-Growth. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 10(4), 1514-1522. https://doi.org/10.35870/jtik.v10i4.7014

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Author Biographies

Mohammad Nabil Nizar, Universitas Islam Nahdlatul Ulama Jepara

Program Studi Teknik Informatika, Fakultas Sains Dan Teknologi, Universitas Islam Nahdlatul Ulama Jepara, Kabupaten Jepara, Provinsi Jawa Tengah, Indonesia.

Sarwido Sarwido, Universitas Islam Nahdlatul Ulama Jepara

Program Studi Teknik Informatika, Fakultas Sains Dan Teknologi, Universitas Islam Nahdlatul Ulama Jepara, Kabupaten Jepara, Provinsi Jawa Tengah, Indonesia.

Adi Sucipto, Universitas Islam Nahdlatul Ulama Jepara

Program Studi Teknik Informatika, Fakultas Sains Dan Teknologi, Universitas Islam Nahdlatul Ulama Jepara, Kabupaten Jepara, Provinsi Jawa Tengah, Indonesia.

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Copyright (c) 2026 Mohammad Nabil Nizar, Sarwido Sarwido, Adi Sucipto

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This work is licensed under a Creative Commons Attribution 4.0 International License.

This is an open-access journal distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, reproduction, and adaptation in any medium (even commercially), provided appropriate credit is given to the original author(s), a link to the license is provided, and any changes made are indicated. Authors retain copyright without restrictions and grant the journal the right of first publication as well as non-exclusive publishing rights for archiving, indexing, and scholarly dissemination.

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