Analysis of Customer Transaction Data Associations Based on The Apriori Algorithm

Tri Astuti, Bella Puspita


UD Dian Pertiwi is one of the small and medium enterprises engaged in materials with the main product is building materials. This business experiences large amounts of transactions every day, the data obtained becomes increasingly large, and it will only be limited to a pile of useless data or commonly called junk. By utilizing a data mining approach apriori algorithm technique, the data can be utilized to support the sales process and achieve a target of UD Dian Pertiwi. Based on research and data mining that has been done using association analysis and apriori algorithms by applying a minimum of support = 1% and a minimum of confidence = 70% resulted in the ten strongest association rules can be used by UD Dian Pertiwi in the process of applying a sales strategy including determining interrelationships, in short, the product has the potential to be purchased at the same time, increasing the amount of product stock and conducting promotions.

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Data mining; Association rules; Apriori algorithm; Minimal support; Confidence.

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P. N. Tan, M. Stenbach, and V. Kumar, Introduction to Data Mining. Boston: Pearson Education, 2006.

F. Goronescu, Data Mining: Concepts, Models, and Techniques. Verlag Berlin Heidelberg: Springer. 2011.

I. H. Witten, E. Frank, and M. A. Hall. Data Mining: Practical Mchine Learning Tools and Techniques, 3rd ed. Burlington, MA: Morgan Kaufmann, 2011.

Lahouar A, Ben Hadj Slama J. Day-ahead load forecast using random forest and expert input selection. Energy Convers Manage 2015;103:104051.

Mahmoud Thair S, Habibi Daryoush, Hassan Mohammed Y, et al. Modelling selfoptimised short term load forecasting for medium voltage loads using tunning fuzzy systems and Artificial Neural Networks. Energy Convers Manage 2015;106:1396408.

Saboori H, Hemmati R, Abbasi V. Multistage distribution network expansion planning considering the emerging energy storage systems. Energy Convers Manage 2015;105:93845.

Wolisz H, Punkenburg C, Streblow R, Mler D. Feasibility and potential of thermal demand side management in residential buildings considering different developments in the German energy market. Energy Convers Manag 2016;107:8695.

Wang F, Xu H, Xu T, et al. The values of market-based demand response on improving power system reliability under extreme circumstances. Appl Energy 2017;193:22031.

Wang F, Zhou L, Ren H, et al. Multi-objective optimization model of source-loadstorage synergetic dispatch for building energy system based on TOU price demand response. IEEE Trans Ind Appl 2017;54:101728.

Chicco G. Overview and performance assessment of the clustering methods for electrical load pattern grouping. Energy 2012;42:6880.

Chicco G, Ilie I-S. Support vector clustering of electrical load pattern data. IEEE Trans Power Syst 2009;24:161928.

Chicco G, Ionel O, Porumb R. Electrical load pattern grouping based on centroid model with ant colony clustering. IEEE Trans Power Syst 2013;28:170615.

Chen Q, Wang F, Hodge BM, et al. Dynamic price vector formation model based automatic demand response strategy for PV-assisted EV charging station. IEEE Trans Smart Grid 2017;8:290315.

Granell R, Axon CJ, Wallom DCH. Clustering disaggregated load profiles using a Dirichlet process mixture model. Energy Convers Manag 2015;92:50716.

Rhodes JD, Cole WJ, Upshaw CR, Edgar TF, Webber ME. Clustering analysis of residential electricity demand profiles. Appl Energy 2014;135:46171.


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barcodeInternational Journal of Informatics and Information Systems (IJIIS)
ISSN: 2579-7069 (online)
Organized by Information System Department - Universitas Amikom Purwokerto - Indonesia, Laboratoire Signaux Et Systèmes (L2s) - Université Paris 13 - France, and Bright Publisher
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