https://ejurnal.seminar-id.com/index.php/bulletinds <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> Forum Kerjasama Pendidikan Tinggi (FKPT) en-US Bulletin of Data Science 2807-9493 <p>Authors who publish with this journal agree to the following terms:</p> <ol> <li class="show">Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under&nbsp;<a href="http://creativecommons.org/licenses/by/4.0/" rel="license">Creative Commons Attribution 4.0 International License</a>&nbsp;that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.</li> <li class="show">Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.</li> <li class="show">Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to&nbsp;<a href="http://opcit.eprints.org/oacitation-biblio.html" rel="license">The Effect of Open Access</a>).</li> </ol> https://ejurnal.seminar-id.com/index.php/bulletinds/article/view/9903 <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> Amali Amali Edy Widodo ##submission.copyrightStatement## http://creativecommons.org/licenses/by/4.0 2026-02-28 2026-02-28 5 2 46 56 10.47065/bulletinds.v5i2.9903