Use of Data Mining for The Analysis of Consumer Purchase Patterns with The Fpgrowth Algorithm on Motor Spare Part Sales Transactions Data

Tri Ahmad Djabalul Lael; Deskha Akmal Pramudito

doi:10.34306/itsdi.v4i2.582

Authors

Tri Ahmad Djabalul Lael Bursa Uludag Universitesi
Deskha Akmal Pramudito Padjajaran University

DOI:

https://doi.org/10.34306/itsdi.v4i2.582

Keywords:

FP-Growth Algorithm, Data Mining, Consumer Buying

Abstract

This study aims to analyze consumer purchasing patterns for motorcycle parts using data mining methods and FP-Growth algorithms on motorcycle parts sales transaction data. This research aims to obtain helpful information for companies in planning marketing strategies and increasing sales. The data used in this study are motorcycle parts sales transaction data from motorcycle parts stores for one year. The data is then processed using the FP-Growth algorithm to find significant purchasing patterns. The results of this study show that the FP-Growth algorithm can be used to identify substantial consumer purchasing patterns. Some purchase patterns found include a combination of often purchased products, the most active purchase time, and the most purchased product category. Using data mining and the FP-Growth algorithm can assist companies in understanding significant consumer purchasing patterns to improve the effectiveness of marketing strategies and increase sales of motorcycle parts. The novelty of this research lies in using data mining methods and FP-Growth algorithms on motorcycle parts sales transaction data to analyze consumer purchasing patterns. This research also provides valuable information for companies in planning marketing strategies and increasing sales by identifying significant consumer purchasing patterns, such as product combinations often purchased together and the most purchased product categories.

Downloads

Download data is not yet available.

References

G. N. B. Safrizal and G. N. Budiadyana, “Analysis Application Design Career Development Center In The STMIK Insan Pembangunan and (Case Study: Information Study Program),” IAIC Trans. Sustain. Digit. Innov., vol. 1, no. 1, pp. 66–77, 2019.

A. Dogan and D. Birant, “Machine learning and data mining in manufacturing,” Expert Syst. Appl., vol. 166, p. 114060, 2021.

S. Wang, J. Cao, and P. Yu, “Deep learning for spatio-temporal data mining: A survey,” IEEE Trans. Knowl. Data Eng., 2020.

L. I. Khalaf, S. A. Aswad, S. R. Ahmed, B. Makki, and M. R. Ahmed, “Survey On Recognition Hand Gesture By Using Data Mining Algorithms,” in 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), 2022, pp. 1–4.

M. S. Amin, Y. K. Chiam, and K. D. Varathan, “Identification of significant features and data mining techniques in predicting heart disease,” Telemat. Informatics, vol. 36, pp. 82–93, 2019.

D. Ahmad, N. Lutfiani, A. D. A. R. Ahmad, U. Rahardja, and Q. Aini, “Blockchain technology immutability framework design in e-government,” J. Adm. Publik (Public Adm. Journal), vol. 11, no. 1, pp. 32–41, 2021.

M. Yusup, P. A. Sunarya, N. Lutfiani, and E. A. Nabila, “A Blockchain-Based Framework Gamification for Securing Learners Activity in Merdeka Belajar-Kampus Merdeka,” in 2022 4th International Conference on Cybernetics and Intelligent System (ICORIS), 2022, pp. 1–6.

E.H. A. Rady and A. S. Anwar, “Prediction of kidney disease stages using data mining algorithms,” Informatics Med. Unlocked, vol. 15, p. 100178, 2019.

M. A. Ledhem, “Data mining techniques for predicting the financial performance of Islamic banking in Indonesia,” J. Model. Manag., vol. 17, no. 3, pp. 896–915, 2022.

L. Rutkowski, M. Jaworski, and P. Duda, Stream data mining: algorithms and their probabilistic properties. Springer, 2020.

G. Ramaswami, T. Susnjak, A. Mathrani, J. Lim, and P. Garcia, “Using educational data mining techniques to increase the prediction accuracy of student academic performance,” Inf. Learn. Sci., vol. 120, no. 7/8, pp. 451–467, 2019.

Sudaryono, U. Rahardja, and N. Lutfiani, “The Strategy of Improving Project Management Using Indicator Measurement Factor Analysis (IMF) Method,” in Journal of Physics: Conference Series, 2020, vol. 1477, no. 3, doi: 10.1088/1742-6596/1477/3/032023.

M. E. Lokanan, “Data mining for statistical analysis of money laundering transactions,” J. Money Laund. Control, vol. 22, no. 4, pp. 753–763, 2019.

I. Amsyar, E. Christopher, A. Dithi, A. N. Khan, and S. Maulana, “The Challenge of Cryptocurrency in the Era of the Digital Revolution: A Review of Systematic Literature,” Aptisi Trans. Technopreneursh., vol. 2, no. 2, pp. 153–159, 2020.

S. Dewan and L. Singh, “Use of blockchain in designing smart city,” Smart Sustain. built Environ., 2020.

H. Mansour, “How successful countries are in promoting digital transactions during COVID-19,” J. Econ. Stud., vol. 49, no. 3, pp. 435–452, 2022.

D. Gohil and S. V. Thakker, “Blockchain-integrated technologies for solving supply chain challenges,” Mod. Supply Chain Res. Appl., vol. 3, no. 2, pp. 78–97, 2021.