Inventory Optimization through FP-Growth-Based Association Rule Mining of Material Stock Usage Patterns

Ubaidillah Ubaidillah; Sumiati Sumiati

doi:10.47065/bits.v7i1.7306

Ubaidillah Ubaidillah * Universitas Pembangunan Nasional "Veteran" Jawa Timur, Surabaya, Indonesia
Sumiati Sumiati Universitas Pembangunan Nasional "Veteran" Jawa Timur, Surabaya, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/bits.v7i1.7306

Keywords: Association Rule Mining; FP-Growth; Material Stock; Inventory Optimization; Data Mining

Abstract

This research focuses on analyzing the usage patterns of material stock at PT Baruna Energi Solusindo Teknik using Association Rule Mining (ARM) with the FP-Growth algorithm. The company often experiences material shortages or excess inventory due to manual and inaccurate stock planning. To address this issue, the study aims to discover frequent itemsets and association rules among materials used in water purification installations, enabling more data-driven procurement decisions. The research employs secondary data on material usage transactions from April 2024 to March 2025, which is processed using RapidMiner software. The FP-Growth algorithm identifies material combinations with high support and confidence values. For instance, Membrane RO has a support value of 75.9%, indicating that it is used in over three-quarters of all projects. Additionally, the combination {Membrane RO, PVC Pipe 1"} → SDL PVC 1" shows a confidence value of 83.3%, signifying a strong association among these items. The results suggest that stock optimization can be achieved by prioritizing frequently used items and associated combinations in procurement planning. This method not only improves inventory efficiency but also helps prevent stockouts and overstocking. The FP-Growth algorithm proves to be suitable and effective in identifying meaningful patterns in stock usage data.

Downloads

Download data is not yet available.

References

Z. N. Dianto and E. Widati, “Analisis Management Inventory Untuk Menghindari Death Stock Product di TB. Sinar Baru,” J. Ilm. Akunt. Keuang. dan Bisnis, vol. 4, no. 1, 2023, doi: 10.36490/value.v4i1.631.

R. Anggraini, S. L. A, and Kiswandono, “Pengendalian Persediaan Material Menggunakan Metode Economic Order Quantity Dan Metode Min-Max Stock Pada UMKM CSH Rengganis,” J. Valtech (Jurnal Mhs. Tek. Ind., vol. 7, no. 1, pp. 54–59, 2024, doi: 10.36040/valtech.v7i1.9254.

A. Supoyo and P. T. Prasetyaningrum, “Analisis Data Mining Untuk Memprediksi Lama Perawatan Pasien Covid-19 Di DIY,” Bianglala Inform., vol. 10, no. 1, pp. 21–29, 2022, doi: 10.31294/bi.v10i1.11890.

M. Sornalakshmi et al., “An Efficient Apriori Algorithm For Frequent Pattern Mining Using Mapreduce in Healthcare Data,” Bull. Electr. Eng. Informatics, vol. 10, no. 1, pp. 390–403, 2021, doi: 10.11591/eei.v10i1.2096.

B. S. Pranata and D. P. Utomo, “Penerapan Data Mining Algoritma FP-Growth Untuk Persediaan Sparepart Pada Bengkel Motor (Study Kasus Bengkel Sinar Service),” Bull. Inf. Technol., vol. 1, no. 2, pp. 83–91, 2020, [Online]. Available: https://journal.fkpt.org/index.php/BIT/article/view/12

Fitriah, I. Riadi, and Herman, “Analisis Data Mining Sistem Inventory Menggunakan Algoritma Apriori,” Decod. J. Pendidik. Teknol. Inf., vol. 3, no. 1, pp. 118–129, 2023, doi: 10.51454/decode.v3i1.132.

Hartono and D. Fitrianah, “A Data Mining Approach For Product Promotion and Inventory Solution using FP-Growth Algorithm,” Int. J. Comput. Appl., vol. 177, no. 38, pp. 37–44, 2020, doi: 10.5120/ijca2020919889.

A. S. Habibah, A. Solehudin, and A. Primajaya, “Analisis Persediaan Stok Barang Warung Menggunakan Data Mining Dengan Algoritma Fp-Growth (Studi Kasus: Warung Bu Nani),” J. Ilm. Wahana Pendidik., vol. 8, no. 17, pp. 46–58, 2022, doi: 10.5281/zenodo.7069512.

A. W. Gultom, “Implementasi Algoritma Fp-Growth Pada Persediaan Barang Elektrikal,” Manag. Inf. Syst. J., vol. 2, no. 2, pp. 20–28, 2024, doi: 10.47065/mis.v2i2.1222.

M. F. Rahman and S. Nursyamsiah, “Pengaruh Praktik Manajemen Persediaan terhadap Kinerja Perusahaan yang Dimediasi oleh Pengetahuan Manajemen Persediaan: Studi Empiris pada Toko Ritel di D.I. Yogyakarta,” Sel. Manaj. J. Mhs. Bisnis Manaj., vol. 02, no. 05, pp. 194–206, 2023, [Online]. Available: https://journal.uii.ac.id/selma/article/view/32947

Y. N. Pudya, I. Hastuti, and E. D. Rahmawati, “Analisis Pengendalian Persediaan Bahan Baku Terhadap Efektivitas Persediaan Produk,” Musytari, vol. 7, no. 5, 2024, doi: 10.8734/mnmae.v1i2.359.

N. Nuraeni and B. Santoso, “Peranan Manajemen Persediaan Bahan Baku terhadap Penjadwalan Produksi PT XYZ,” J. Bisnis dan Manaj., vol. 2, no. 2, pp. 379–394, 2024, doi: 10.61930/jurbisman.v2i2.614.

P. M. S. Tarigan, J. T. Hardinata, H. Qurniawan, M. Safii, and R. Winanjaya, “Implementasi Data Mining Menggunakan Algoritma Apriori Dalam Menentukan Persediaan Barang (Studi Kasus : Toko Sinar Harahap),” Just IT J. Sist. Informasi, Teknol. Inf. dan Komput., vol. 12, no. 2, pp. 51–61, 2022, doi: 10.24853/justit.12.2.%25p.

N. L. W. S. R. Ginantra and I. B. G. Anandita, “Penerapan Metode Single Exponential Smoothing dalam Peramalan Penjualan Benang,” J. Sains Komput. Inform., vol. 3, no. 2, pp. 433–441, 2019, doi: 10.30645/j-sakti.v3i2.162.

Amna et al., Data Mining, vol. 2, no. January 2013. Padang: PT Global Eksekutif Teknologi, 2023. [Online]. Available: https://repository.uinjkt.ac.id/dspace/handle/123456789/71093

M. Zaki and W. Meira, Data Mining and Analysis Fundamental. New York: Cambridge University Press, 2015. doi: 10.1017/CBO9780511810114.

F. Liantoni, Data Mining Dan Penerapan Metode. Purbalingga: Eureka Media Akasara, 2022. [Online]. Available: https://repository.penerbiteureka.com/publications/470258/data-mining-dan-penerapan-metode

E. Munanda and S. Monalisa, “Penerapan Algoritma Fp-Growth Pada Data Transaksi Penjualan Untuk Penentuan Tataletak,” J. Ilm. Rekayasa dan Manaj. Sist. Inf., vol. 7, no. 2, pp. 173–184, 2021, doi: 10.24014/rmsi.v7i2.13253.

B. Mulyawan, Vionelsy, and T. Sutrisno, “Product Recommendation System on Building Materials Shopping Using FP-Growth Algorithm,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1007, no. 1, 2020, doi: 10.1088/1757-899X/1007/1/012144.

N. Isa, S. K. Neddy, and N. Mohamed, “Association Rule Mining Using FP-Growth Algorithm to Prevent Maverick Buying,” ISCAIE 2021 - IEEE 11th Symp. Comput. Appl. Ind. Electron., pp. 77–81, 2021, doi: 10.1109/ISCAIE51753.2021.9431821.

H. J. Jang, Y. Yang, J. S. Park, and B. Kim, “Fp-growth algorithm for discovering region-based association rule in the iot environment,” Electron., vol. 10, no. 24, 2021, doi: 10.3390/electronics10243091.

M. Y. Ardianto, S. Adinugroho, and Indrianti, “Penentuan Tata Letak Produk menggunakan Algoritma FP-Growth pada Toko ATK,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 9, pp. 3826–3832, 2021, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/9741

P. Salsabila, E. Wahyudin, G. Dwilestari, Kaslani, and F. Subhiyanto, “Penerapan Algoritma Fp-Growth Untuk Mengetahui Pola Pembelian Konsumen Di Warung Makan Dede,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 1, pp. 1221–1128, 2024, doi: 10.36040/jati.v8i1.8964.

I. Ismarmiaty and R. Rismayati, “Product Sales Promotion Recommendation Strategy with Purchase Pattern Analysis FP-Growth Algorithm,” Sink. J. dan Penelit. Tek. Inform., vol. 8, no. 1, pp. 202–211, 2023, doi: 10.33395/sinkron.v8i1.11898.

C. Y. Purnamawati and J. A. M. Nugraha, “Application of the Association Method Using FP-Growth Algorithm to Find Pattern Of Medicine Purchasing Transactions at Pharmacy,” J. Appl. Res. Comput. Sci. Inf. Syst., vol. 1, no. 2, pp. 48–53, 2023, doi: 10.61098/jarcis.v1i2.50.

Z. Lin, A. Matta, S. Du, and E. Sahin, “A Partition-Based Random Search Method for Multimodal Optimization,” Mathematics, vol. 11, no. 17, pp. 1–30, 2023, doi: 10.3390/math11010017.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Inventory Optimization through FP-Growth-Based Association Rule Mining of Material Stock Usage Patterns