https://ejurnal.seminar-id.com/index.php/bulletinds/issue/feed 2026-05-10T16:17:29+07:00 Support Journal seminar.id2020@gmail.com Open Journal Systems <p data-start="0" data-end="433">The <strong><em data-start="4" data-end="30">Bulletin of Data Science</em></strong> is a journal managed and published by the <a href="https://fkpt.org/"><strong><span class="hover:entity-accent entity-underline inline cursor-pointer align-baseline"><span class="whitespace-normal">Forum Kerjasama Pendidikan Tinggi</span></span> (FKPT)</strong></a>, located in <span class="hover:entity-accent entity-underline inline cursor-pointer align-baseline"><span class="whitespace-normal">Medan</span></span>, <span class="hover:entity-accent entity-underline inline cursor-pointer align-baseline"><span class="whitespace-normal">North Sumatra</span></span>. The journal publishes research in the field of <strong>Informatics</strong>, particularly those related to <strong>Data Science</strong>. It holds the online ISSN <a href="https://issn.perpusnas.go.id/terbit/detail/20210917311475138">2807-9493 (online)</a>, based on Decree No. 0005.28079493/K.4/SK.ISSN/2021.09 issued on September 20, 2021.<br>The <strong><em data-start="439" data-end="465">Bulletin of Data Science</em></strong> is published three times a year, namely in October (<strong>Issue 1</strong>), February (<strong>Issue 2</strong>), and June (<strong>Issue 3</strong>). It is indexed in <strong>&nbsp;&nbsp;<a href="https://scholar.google.com/citations?hl=id&amp;user=c9HhReoAAAAJ">Google Scholar</a> |</strong><strong>&nbsp;<a href="https://garuda.kemdikbud.go.id/journal/view/24551">Portal Garuda&nbsp;</a>|&nbsp;<a href="https://portal.issn.org/resource/ISSN/2807-9493#">ROAD</a>&nbsp;| <a href="https://app.dimensions.ai/discover/publication?and_facet_source_title=jour.1492637">Dimensions</a> | &nbsp;Science and Technology Index (SINTA 4)&nbsp;</strong></p> <p>&nbsp;</p> https://ejurnal.seminar-id.com/index.php/bulletinds/article/view/9903 2026-05-10T16:17:29+07:00 Amali Amali amali@pelitabangsa.ac.id Edy Widodo ewidodo@pelitabangsa.ac.id

<p>The development of information technology encourages companies to utilize sales transaction data as a strategic source of information in business decision-making. However, the increasing amount of transaction data is often not optimally utilized to identify consumer purchasing patterns. This study aims to analyze consumer purchasing patterns in spare parts sales transactions using association rules based on the Apriori algorithm to support the optimization of sales strategies and inventory management. The research method used is a quantitative approach consisting of data collection, data preprocessing, transaction data transformation, frequent itemset generation, and association rule formation. The data used in this study consisted of 350 spare parts sales transactions processed using the Apriori algorithm with a minimum support value of 20% and a minimum confidence value of 70%. The results showed that the products Front Bumper and Brake Pads had the strongest association relationship with a confidence value of 76% and support value of 23%. In addition, the relationship between Radiator and Side Mirror products showed a confidence value of 71%. The study proves that the Apriori algorithm is effective in identifying relationships between products and can assist companies in determining promotional strategies, inventory management, and data-driven business decision-making to improve spare parts sales</p>

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