Sinaga, Jordiansyah Simto2023-05-042023-05-042023-01PerpustakaanElfitrahttps://repository.unri.ac.id/handle/123456789/10947Increasing population growth and increasingly limited land for housing needs has led to high public interest in housing loans so that they can pay installments every month. But problems that often occur are errors in determining the credit period and the number of installments paid each month causing credit arrears. This causes difficulties for developers in recommending services that are in accordance with the consumer's economy at PT. Semangat Jaya Sentosa enthusiasm for processing data manually. So to overcome this problem, companies need recommendations in the form of strategies obtained from consumer data at PT. Semangat Jaya Sentosa. This study aims to obtain Association Rules from consumer data and information on housing loans. The results of the Association Rules will form strategic recommendations for the developer. The attributes used are gender, status, occupation, district/city, credit period, and income. This study carried out 6 stages in obtaining Association Rules namely literature study, data collection, data selection, data cleaning, implementation of a priori algorithms, and interpretation. This study uses the Python programming language to find Association Rules from consumer housing loan data sets and through join and prune processes. This study uses consumer data for the period from January to December 2021 as many as 1,514 data with a minimum Support value of 30% and a minimum Confidance of 83% so as to produce a pattern of 18 rules so that the highest value for the combination of 3 itemset is Pekanbaru, Lowerclient, 20 has a Support value of 0.4557 = 45.57 % and Confidance value 1 = 100%. The results of the interpretation of the rules concluded, the developer can provide recommendations regarding the appropriate credit period for consumers with income below 3 million rupiah from the Pekanbaru city area, which is 20 years. The results of the Association Rules are then formed to recommend the best strategy for the developer and are expected to be able to overcome the problem of housing loans at PT. Semangat Jaya Sentosa.enAprioriAlgorithm Association Rule MethodConsumer data patternsData miningHome Ownership LoansArticle