Association Pattern Analysis of Production Results Using the Apriori Algorithm

Authors

  • Zacky Achmad Sholeh Universitas Bhyangkara Jakarta Raya, Bekasi
  • Wowon Priatna Universitas Bhyangkara Jakarta Raya, Bekasi
  • Muhammad Yasir Universitas Bhyangkara Jakarta Raya, Bekasi

DOI:

https://doi.org/10.58776/ijitcsa.v4i2.208

Keywords:

Apriori Algorithm, Association Rules, KDD Process, Production Data, Pattern Analysis

Abstract

This study aims to analyze association patterns in production data at CV. Sinar Agung Teknik using the Apriori algorithm. The company faces challenges in identifying co-produced product relationships, which complicates production pattern recognition. The research adopts the Knowledge Discovery in Databases (KDD) approach, comprising data selection from three months of daily production, data cleaning, transformation into transactional format, application of the Apriori algorithm, and result visualization. Key parameters applied in the mining process include support, confidence, and lift. The analysis was conducted from 1-itemset to 5-itemset combinations to determine product co-occurrence frequencies. The results revealed several significant association rules. One notable rule shows that the production of Karet Membran TT, Panel Pressure Destec, and Plat C Starcam is followed by Join Tuas Starcam and Karet Membran COM, with a confidence of 90% and a lift value of 2.25. A lift greater than 1 indicates a strong correlation among the products. These findings are expected to provide data-driven insights that can support decision-making in warehouse management, inventory control, and the strategic arrangement and retrieval of products

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Published

02-07-2026

How to Cite

Zacky Achmad Sholeh, Priatna, W., & Yasir, M. (2026). Association Pattern Analysis of Production Results Using the Apriori Algorithm. International Journal of Information Technology and Computer Science Applications, 4(2), 141–153. https://doi.org/10.58776/ijitcsa.v4i2.208

Issue

Vol. 4 No. 2 (2026): May - August 2026

Section

New Submission

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Copyright (c) 2026 Zacky Achmad Sholeh, Wowon Priatna, Muhammad Yasir

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