Small, Medium Enterprises (SMEs) are one of the supporting parts of the Indonesian economy by absorbing high labor and production value. One of the factors that greatly influences the development of SMEs in Indonesia is the workforce involved in SMEs business activities. Several factors that influence the workforces are gender, education level, marital status and wages level. The data mining used can help processing data into new knowledge that can be used in decision making. The purpose of this study was to analyze the data of SMEs workers in the Palembang city by comparing the Apriori algorithm and the FP-Growth algorithm to see the association between gender, education level, marital status and wages earned by SMEs’s workers in Palembang city. The sample used in this research are 400 SMEs’s workers who were randomly selected from 5 sub-districts in Palembang city. The results show that with a confidence level of 0.8, the Apriori Algorithm produces 25 association patterns while the FP-Growth Algorithm has 11 association patterns. In the Apriori Algorithm, it was found that 72% of associations with 1 level confidence while the rest is less than 1. As for the FP-Growth Algorithm, 9% of the association patterns have 1 confidence level and the rest is less than 1. These results indicate that the Apriori algorithm is able to explain more association between gender, education level, marital status and wages of SMEs’s workers in Palembang City. The results of this study can be used as a consideration for increasing the capacity of SMEs’s workers in Palembang City.
H. Keskin, C. Sentürk, O. Sungur, and H. M. Kiris, “The Importance of SMEs in Developing Economies,” 2nd Int. Symp. Sustain. Dev., pp. 183–192, 2010.
Islamiyah, P. L. Ginting, N. Dengen, and M. Taruk, “Comparison of Priori and FP-Growth Algorithms in Determining Association Rules,” ICEEIE 2019 - Int. Conf. Electr. Electron. Inf. Eng. Emerg. Innov. Technol. Sustain. Futur., pp. 320–323, 2019, doi: 10.1109/ICEEIE47180.2019.8981438.
D. Wicaksono, M. I. Jambak, and D. M. Saputra, “The Comparison of Apriori Algorithm with Preprocessing and FP-Growth Algorithm for Finding Frequent Data Pattern in Association Rule,” vol. 172, no. Siconian 2019, pp. 315–319, 2020, doi: 10.2991/aisr.k.200424.047.
W. P. Nurmayanti, H. M. Sastriana, and A. Rahim, “Market Basket Analysis with Apriori Algorithm and Frequent Pattern Growth ( Fp-Growth ) on Outdoor Product Sales Data,” pp. 132–139.
P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining (New International Editon), no. September. 2013.
M. Liang, Data Mining: Concepts, Models, Methods, and Algorithms, vol. 36, no. 5. 2004.
R. Chauhan and H. Kaur, Predictive Analytics and Data Mining. 2015.
K. Dharmarajan and M. A. Dorairangaswamy, “Analysis of FP-growth and Apriori algorithms on pattern discovery from weblog data,” 2016 IEEE Int. Conf. Adv. Comput. Appl. ICACA 2016, pp. 170–174, 2016, doi: 10.1109/ICACA.2016.7887945.
Republic Act Indonesia No. 20 of 2008. .
T. T. H. Tambunan, UMKM di Indonesia. Bogor: Ghalia Indonesia, 2009.