Analysis of Transaction Data for Modeling the Pattern of Goods Purchase Supporting Goods Location

Linda Rosliadewi, Yusmedi Nurfaizal, Retno Waluyo, Mohammad Imron
Pages 65-75
Abstract Views 687
PDF Downloads 463
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DOI https://doi.org/10.47738/jads.v1i2.54

Abstract


Arlinda shop is a shop that sells daily necessities located in Salem, Brebes. Each day, this shop generates more and more data that is not used. The store layout which does not get enough attention will affect the level of sales. This study aimed to process the unused transaction data to obtain purchase patterns, some of the most frequently used algorithms were the apriori algorithm and FP-Growth algorithm to find relationship patterns, however, there was a technical constraint in the recommendation technique used which was frequently ignoring a large collection of items. To overcome this problem, the clustering process was carried out using the K-Medoids algorithm so that the association process became smaller. The test was carried out using RapidMiner with a minimum support of 10% - 30% and a minimum confidence of 70% and the results of recommendations for the layout of the goods with the highest lift ratio, namely if someone buys Nuvo BW then he buys pepsodent act, if someone buys wrapping papers then he buys mamy poko, and if someone buys cereal milo then he buys chitato.

Keywords


Association rule; Clustering; Purchasing Patterns

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References


G. Gunadi and I. S. Dana, “Application of Data Mining Market Basket Analysis Method on Sales Data for Book Products Using Apriori Algorithm and Frequent Pattern Growth (FP-Growth): A Case Study of PT,” Gramedia". J. Telemat. MKOM, vol. 4, no. 1.

H. Aprizal and N. Wahyu, “Implementation of Data Mining for Positioning Goods on Shelves Using the Apriori Method at PT Midi Utama Indonesia",” Techo.com, vol. 15, p. 4.

K. Christidis, D. Apostolou, and G. Mentzas, “Exploring Customer Preferences With Probabilistic Topics Models".”

S. Defiyanti, M. Jajuli, and R. W. Nurul, “Optimizing K-Medoids in Clustering Scholarship Applicants Using Cubic Clustering Criterion”.” .

M. I. Ghozali, Z. E. Ri’fan, and H. S. Wibowo, “Analysis of Shopping Patterns Using the FP-Growth Algorithm, Self Organizing Map (SOM) and K Medoids",” J. SIMETRIS Vol.8 No.1.

N. Maharani et al., “Implementation of Data Mining for Mini Market Layout Settings by Applying Association rules”,” J. Comput.

A. Masnur, Data Mining Analysis Using Market Basket Analysis to Determine Consumer Buying Patterns". SATIN- Science and Information Technology Vol.1 No.2.

D. Nofriansyah, The Concept of Data Mining VS Decision Support Systems. Yogyakarta: Depublish.

L. I. Prahartiwi, “Frequent Itemset Search in Shopping Cart Analysis Using the FP-Growth Algorithm”,” Inf. Syst. Educ. Prof., vol. 2, no. 1.

H. Santoso, I. Putu, and P. H., “Data Mining Analysis of Product Purchase Patterns Using the Apriori Algorithm Method",” in 2016 National Seminar on Information Technology and Multimedia, .

K. Sumangkut, L. Ari, and T. Virginia, “Analysis of the Daily Mart supermarket shopping pattern to determine the layout of goods using the FP-Growth Algorithm",” E-Journal Informatics Eng., vol. 8.

K. N. Wijaya, Shopping Cart Frequency Pattern Analysis Using Apriori Algorithm. Case Study: Minimarket.

N. Omar, Z. ‘Amirah Johari, and M. Smith, “Predicting fraudulent financial reporting using artificial neural network,” J. Financ. Crime, vol. 24, no. 2, pp. 362–387, Jan. 2017, doi: 10.1108/JFC-11-2015-0061.

U. C. Moharana, S. P. Sarmah, and P. K. Rathore, “Application of data mining for spare parts information in maintenance schedule: a case study,” J. Manuf. Technol. Manag., vol. 30, no. 7, pp. 1055–1072, Jan. 2019, doi: 10.1108/JMTM-09-2018-0303.

J. F. Hair, “Knowledge creation in marketing: the role of predictive analytics,” Eur. Bus. Rev., vol. 19, no. 4, pp. 303–315, Jan. 2007, doi: 10.1108/09555340710760134.

G. Boskou, E. Kirkos, and C. Spathis, “Classifying internal audit quality using textual analysis: the case of auditor selection,” Manag. Audit. J., vol. 34, no. 8, pp. 924–950, Jan. 2019, doi: 10.1108/MAJ-01-2018-1785.

M. Yu and C. Guo, “Using news to predict Chinese medicinal material price index movements,” Ind. Manag. Data Syst., vol. 118, no. 5, pp. 998–1017, Jan. 2018, doi: 10.1108/IMDS-06-2017-0287.

J. Manickavasagam and V. S., “An investigational analysis on forecasting intraday values,” Benchmarking An Int. J., vol. 27, no. 2, pp. 592–605, Jan. 2020, doi: 10.1108/BIJ-11-2018-0361.

X. Ji, J. Wang, and Z. Yan, “A stock price prediction method based on deep learning technology,” Int. J. Crowd Sci., vol. 5, no. 1, pp. 55–72, Jan. 2021, doi: 10.1108/IJCS-05-2020-0012.

A. Gepp, M. K. Linnenluecke, T. J. O’Neill, and T. Smith, “Big data techniques in auditing research and practice: Current trends and future opportunities,” J. Account. Lit., vol. 40, no. 1, pp. 102–115, Jan. 2018, doi: 10.1016/j.acclit.2017.05.003.




DOI: https://doi.org/10.47738/jads.v1i2.54

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Journal of Applied Data Sciences

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