Family Economic Correlation To Students Learning Achievment Using Apriori Method
Keywords:Apriori Algorithm, Association Rule, Correlation, Minsupport, Min Confidence
The education system in Indonesia as mandated in the GBHN aims to educate the nation while at the same time responding to new challenges to create a decent and prosperous life. Understanding, apprecation, and experience of cultural and religious values in the right and true form will be increasingly needed. The economic status of the family is one of the factors that is sufficient to support the level of continuing education, especially for teenagers who are still student in school. Apriori method is used to obtain association rules that describe the relationship between item in the transactional database. There are two databases used, each of which has a different number of transactions. This study aims to aplly the apriori algorithm, as an analytical technique. The data taken as a case example is familiy economic data. This association search uses WEKA which will later find the rules and MySQL as the placeholder for the Database. From the results of the analysis using apriori, the highest confidence value was obtained at 0.9 with support 0.1 resulting in a students rule whose economics supported the learning achievement was very supportive, and the lowest confidence value of 0.2 with support 0.1 resulted in a students rule who had sufficient economics, so their learning achievement was also quite increased..
R. Ruswati, A. I. Gufroni, and R. Rianto, “Associative Analysis Data Mining Pattern Against Traffic Accidents Using Apriori Algorithm,” Sci. J. Informatics, vol. 5, no. 2, pp. 91–104, 2018, doi: 10.15294/sji.v5i2.16199.
T. Marnoto, “Drying of Rosella (Hibiscus sabdariffa) Flower Petals using Solar Dryer with Double Glass Cover Collector,” Int. J. Sci. Eng., vol. 7, no. 2, pp. 155–160, 2014, doi: 10.12777/ijse.7.2.150-154.
R. Rusdiansyah, N. Suharyanti, T. Triningsih, and M. Darussalam, “Application Of Pizza Sales Data Mining Using Apriori Method,” SinkrOn, vol. 4, no. 2, p. 1, 2020, doi: 10.33395/sinkron.v4i2.10500.
A. P. Windarto, “Implementation of Data Mining on Rice Imports by Major Country of Origin Using Algorithm Using K-Means Clustering Method,” Int. J. Artif. Intell. Res., vol. 1, no. 2, p. 26, 2017, doi: 10.29099/ijair.v1i2.17.
A. Azwar, “Algorithm Apriori Use for a Consumer Behavior in,” Sains dan Inform., vol. 1, pp. 45–59, 2015.
R. Mustofa and Irhamah, “Topic Discovery pada Jurnal-jurnal di IEEE Explore menggunakan Association Rule Mining dengan Pendekatan Closed Frequent Itemset,” Ejurnal.Its.Ac.Id, vol. 8, no. 2, 2019, [Online]. Available: http://www.ejurnal.its.ac.id/index.php/sains_seni/article/view/43653.
W. W. Ariestya, W. Supriyatin, and I. Astuti, “Marketing Strategy for the Determination of Staple Consumer Products Using Fp-Growth and Apriori Algorithm,” J. Ilm. Ekon. Bisnis, vol. 24, no. 3, pp. 225–235, 2019, doi: 10.35760/eb.2019.v24i3.2229.
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