IMPLEMENTASI ALGORITMA FP-GROWTH DALAM MENGELOMPOKKAN DATA TRANSAKSI PENDAPATAN PADA LAUNDRY ZONE

Mariska Mariska; Agsu Bahtiar; Bani Nurhakim

Authors

Mariska Mariska STMIK IKMI Cirebon, Indonesia
Agsu Bahtiar STMIK IKMI Cirebon, Indonesia
Bani Nurhakim STMIK IKMI Cirebon, Indonesia

Keywords:

Data mining, FP-Growth, association rules, income transactions, Laundry Zone.

Abstract

The rapid development of information technology has created opportunities for small and medium-sized enterprises (SMEs) to utilize transaction data as the foundation for business decision-making. One of the promising approaches is data mining, particularly association rule mining using the FP-Growth algorithm, which is recognized for its efficiency in discovering frequent patterns. This study aims to implement the FP-Growth algorithm in grouping income transaction data at Laundry Zone, a laundry service business located in Cirebon, Indonesia. Transaction data were collected on September 16, 2025, covering various service types including kilo laundry, ironing, dry cleaning, and express service. The research stages consisted of data collection, data preprocessing (aggregate, rename, set role), FP-Growth algorithm implementation, and result evaluation using support, confidence, and lift parameters. The results indicate that kilo laundry is the most dominant service, with a frequency of 65%, followed by ironing services at 55%. The combination {kilo laundry, ironing} appears as the most frequent transaction pattern with 45% support. The association rule {ironing} → {kilo laundry} achieved 82% confidence, while {dry clean} → {kilo laundry} recorded 80% confidence. These findings highlight that customers tend to order ironing or dry cleaning services alongside kilo laundry. Moreover, the rule {express service} → {ironing} with 72% confidence suggests a potential strategy to develop express packages combined with ironing for customers seeking both speed and neatness. In practical terms, these results can be utilized by Laundry Zone to design targeted promotional strategies, such as bundled packages of kilo laundry plus ironing or value-added promotions based on customer transaction patterns. Academically, this study contributes by expanding the application of the FP-Growth algorithm to the service sector, particularly laundry services, which remain underexplored. Therefore, the research confirms that FP-Growth-based data analysis can support SMEs in transforming into data-driven businesses, making them more adaptive and competitive in the digital era.

References

“Pendeteksian Kanker Paru-Paru Menggunakan Perpaduan Moment Invariants dan Jaringan Syaraf Propagasi Balik.”

Latifah Listyalina, “PENENTUAN PENYAKIT PARU DENGAN MENGGUNAKAN JARINGAN SARAF TIRUAN,” Jurnal SIMETRIS, Vol. 11 No. 1 April 2020, 2020.

K. Ioannou, D. Karampatzakis, P. Amanatidis, V. Aggelopoulos, and I. Karmiris, “Low-cost automatic weather stations in the internet of things,” Apr. 01, 2021, MDPI AG. doi: 10.3390/info12040146.

R. Teguh and H. Usup, “Realtime monitoring for groundwater level and local climate based on universal communication system,” Computer Science and Information Technologies, vol. 2, no. 2, pp. 67–76, Jul. 2021, doi: 10.11591/csit.v2i2.p67-76.

D. A. Kurnia, A. Setiawan, D. R. Amalia, R. W. Arifin, and D. Setiyadi, “Image Processing Identifacation for Indonesian Cake Cuisine using CNN Classification Technique,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Feb. 2021. doi: 10.1088/1742-6596/1783/1/012047.

D. Sudrajat, R. D. Dana, N. Rahaningsih, A. R. Dikananda, and D. A. Kurnia, “Clustering student’s satisfaction in complex adaptive blended learning with the six value system using the K-means algorithm,” Universal Journal of Educational Research, vol. 7, no. 9, pp. 1990–1995, 2019, doi: 10.13189/ujer.2019.070920.

R. Widadi, B. Arif Widodo, and D. Zulherman, “Klasifikasi Sinyal EEG pada Sistem BCI Pergerakan Jari Manusia Menggunakan Convolutional Neural Network,” Jurnal Techno.com, vol. 19, no. 4, pp. 459–467, 2020.

A. Purnomo and H. Tjandrasa, “IMPROVED DEEP LEARNING ARCHITECTURE WITH BATCH NORMALIZATION FOR EEG SIGNAL PROCESSING,” JUTI: Jurnal Ilmiah Teknologi Informasi, vol. 19, no. 1, p. 19, Jan. 2021, doi: 10.12962/j24068535.v19i1.a1023.

H. B. Kim, Y. S. Park, J. E. Lee, K. Do Han, and Y. H. Park, “Study on relationship between self-recognition of voice disorder and mental health status: Korea National Health and Nutrition Examination Survey,” J Affect Disord, vol. 338, pp. 482–486, Oct. 2023, doi: 10.1016/j.jad.2023.05.082.

U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan, and H. Adeli, “Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals,” Comput Biol Med, vol. 100, pp. 270–278, Sep. 2018, doi: 10.1016/j.compbiomed.2017.09.017.

A. Eviyanti, H. Hindarto, Sumarno, and H. A. A. Duddin, “Epilepsi detection system based on EEG record using neural network backpropagation method,” in Journal of Physics: Conference Series, Institute of Physics Publishing, Nov. 2019. doi: 10.1088/1742-6596/1381/1/012037.

A. Novianto and M. D. Anasanti, “Autism Spectrum Disorder (ASD) Identification Using Feature-Based Machine Learning Classification Model,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 17, no. 3, p. 259, Jul. 2023, doi: 10.22146/ijccs.83585.