MSMEs Recommendation System using Item-Based Collaborative Filtering and LightGBM Machine Learning

Mar’atuttahirah Mar’atuttahirah; Khaera Tunnisa; Danang Fatkhur Razak Ra; Hafizah Najwa; Januar Fahrisal

doi:10.52436/1.jutif.2025.6.5.5292

Authors

Mar’atuttahirah Information System, Institut Teknologi Bacharuddin Jusuf Habibie, Indonesia
Khaera Tunnisa Information System, Institut Teknologi Bacharuddin Jusuf Habibie, Indonesia
Danang Fatkhur Razak Ra Information System, Institut Teknologi Bacharuddin Jusuf Habibie, Indonesia
Hafizah Najwa Information System, Institut Teknologi Bacharuddin Jusuf Habibie, Indonesia
Januar Fahrisal Information System, Institut Teknologi Bacharuddin Jusuf Habibie, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.5.5292

Keywords:

Collaborative Filtering, Item, Feature, Machine Learning, Revenue, Recomendation

Abstract

Micro, Small, and Medium Enterprises (MSMEs) face challenges in recommendation systems for digital economy growth, particularly in participatory development and sustainable revenue optimization. This study aims to develop a recommendation system using Item-Based Collaborative Filtering and LightGBM for stock prediction and item recommendation at Kedai Pesisir MSME. Based on 1,229 transaction records from January to July 2025, we performed preprocessing, feature engineering, and LightGBM training to generate daily stock predictions and monthly priorities for August 2025 to January 2026. Evaluation yielded RMSE 0.069, MAE 0.034, and MAPE 1.14%, indicating high accuracy. This advances informatics by providing a scalable AI tool for MSME inventory management and revenue enhancement, supporting strategic decisions in dynamic markets.

Downloads

Download data is not yet available.

References

M. Purwaningsih, B. Purwandari, M. Mishbah, and P. O. H. Putra, “Collaborative Commerce Model for SMEs A Knowledge and Resources Sharing Mechanism for Co Creation,” DESIDOC J. Libr. Inf. Technol., vol. 42, no. 2, pp. 80–87, Feb. 2022, doi: 10.14429/djlit.42.2.17294.

C. Xiaochen, “Research on the Measurement of China’s High-quality Economic Development Level,” in 2020 2nd International Conference on Economic Management and Model Engineering (ICEMME), Chongqing, China: IEEE, Nov. 2020, pp. 779–782. doi: 10.1109/ICEMME51517.2020.00159.

X. Xiao, “Research on the integration of digital economy and real economy to promote high-quality economic development,” in 2020 Management Science Informatization and Economic Innovation Development Conference (MSIEID), Guangzhou, China: IEEE, Dec. 2020, pp. 8–11. doi: 10.1109/MSIEID52046.2020.00009.

D. Rifai, S. Fitri, and I. N. Ramadhan, “Perkembangan Ekonomi Digital Mengenai Perilaku Pengguna Media Sosial Dalam Melakukan Transaksi,” ADI Bisnis Digit. Interdisiplin J., vol. 3, no. 1, pp. 49–52, Jun. 2022, doi: 10.34306/abdi.v3i1.752.

I. Indrabayu, A. A. Ilham, I. S. Areni, A. Bustamin, Muh. A. F. I. Oemar, and S. W. Sidehabi, “Sosialisasi Rekondisi UMKM di Kelurahan Borongloe Kabupaten Gowa dengan Teknologi Informasi dan BMC di Era Pandemi Covid 19,” SIPISSANGNGI J. Pengabdi. Kpd. Masy., vol. 1, no. 3, p. 10, Sep. 2021, doi: 10.35329/sipissangngi.v1i3.2252.

E. Haryo, “Dorong UMKM Naik Kelas dan Go Export, Pemerintah Siapkan Ekosistem Pembiayaan yang Terintegrasi,” p. 2, Agustus 2023.

R. A. Rifani, “Implementasi Layanan Food Delivery bagi Penjualan UMKM Makanan dan Minuman di Kota Parepare,” J. Manag., vol. 5, no. 2, Sep. 2022.

S. Maratuttahirah and A. K. Jaya, “Klasifikasi Omset ATK menggunakan Algoritma Naïve Bayes,” vol. 5, no. 1, 2024.

L. Chao, “Hypergraph Contrastive Collaborative Filtering,” SIGIR 22 Proc. 45th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., vol. 64, pp. 70–79, Jul. 2022, doi: https://doi.org/10.1145/3477495.3532058.

Y. Afoudi, M. Lazaar, and M. Al Achhab, “Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network,” Simul. Model. Pract. Theory, vol. 113, p. 102375, Dec. 2021, doi: 10.1016/j.simpat.2021.102375.

G. Varoquaux and V. Cheplygina, “Machine learning for medical imaging: methodological failures and recommendations for the future,” Npj Digit. Med., vol. 5, no. 1, p. 48, Apr. 2022, doi: 10.1038/s41746-022-00592-y.

D. Roy and M. Dutta, “A systematic review and research perspective on recommender systems,” J. Big Data, vol. 9, no. 1, p. 59, Dec. 2022, doi: 10.1186/s40537-022-00592-5.

J. Yu et al., “Collaborative Filtering Recommendation with Fluctuations of User’ Preference,” in 2021 IEEE International Conference on Information Communication and Software Engineering (ICICSE), Chengdu, China: IEEE, Mar. 2021, pp. 222–226. doi: 10.1109/ICICSE52190.2021.9404120.

E. Zangerle and C. Bauer, “Evaluating Recommender Systems: Survey and Framework,” ACM Comput. Surv., vol. 55, no. 8, pp. 1–38, Aug. 2023, doi: 10.1145/3556536.

X. Yuan, H. Chen, Y. Song, X. Zhao, and Z. Ding, “Improving Sequential Recommendation Consistency with Self-Supervised Imitation,” in Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, Montreal, Canada: International Joint Conferences on Artificial Intelligence Organization, Aug. 2021, pp. 3321–3327. doi: 10.24963/ijcai.2021/457.

X. Xin, A. Karatzoglou, I. Arapakis, and J. M. Jose, “Self-Supervised Reinforcement Learning for Recommender Systems,” Jun. 11, 2020, arXiv: arXiv:2006.05779. doi: 10.48550/arXiv.2006.05779.

C. Lokker et al., “Boosting efficiency in a clinical literature surveillance system with LightGBM,” PLOS Digit. Health, vol. 3, no. 9, p. e0000299, Sep. 2024, doi: 10.1371/journal.pdig.0000299.

Y. Wu and Q. Wang, “LightGBM Based Optiver Realized Volatility Prediction,” in 2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE), SC, USA: IEEE, Aug. 2021, pp. 227–230. doi: 10.1109/CSAIEE54046.2021.9543438.

G. V. D. Kumar, V. Deepa, N. Vineela, and G. Emmanuel, “Detection of Parkinson’s disease using LightGBM Classifier,” in 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India: IEEE, Mar. 2022, pp. 1292–1297. doi: 10.1109/ICCMC53470.2022.9753909.

X. Li, “Design of a Web Content Personalized Recommendation System Based on Collaborative Filtering Improved by Combining k-means and LightGBM,” J. Web Eng., Apr. 2025, doi: 10.13052/jwe1540-9589.2425.

L. Chen, L. Wu, R. Hong, K. Zhang, and M. Wang, “Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach,” Proc. AAAI Conf. Artif. Intell., vol. 34, no. 01, pp. 27–34, Apr. 2020, doi: 10.1609/aaai.v34i01.5330.

A. Latif and S. Khotimatul Wildah, “ANALISIS KINERJA ALGORITMA ENSEMBLE DALAM PREDIKSI PERILAKU PEMBELIAN PELANGGAN,” JATI J. Mhs. Tek. Inform., vol. 9, no. 1, pp. 557–563, Dec. 2024, doi: 10.36040/jati.v9i1.12413.

S. A. S. Purwandhani, A. A. N. Sajiatmoko, and C. S. K. Aditya, “Optimizing Indonesian Banking Stock Predictions with DBSCAN and LSTM,” J. Tek. Inform. Jutif, vol. 6, no. 3, pp. 1173–1188, Jun. 2025, doi: 10.52436/1.jutif.2025.6.3.4439.

X. Zhang, “Ion channel prediction Using Lightgbm Model,” in 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), Fuzhou, China: IEEE, Jun. 2020, pp. 349–352. doi: 10.1109/ICBAIE49996.2020.00079.

X. Li, “Design of a Web Content Personalized Recommendation System Based on Collaborative Filtering Improved by Combining k-means and LightGBM,” J. Web Eng., Apr. 2025, doi: 10.13052/jwe1540-9589.2425.

R. Wahyusari, S. Sunardi, and A. Fadlil, “Comparative Analysis Of Ant Lion Optimization And Jaya Algorithm For Feature Selection In K-Nearest Neighbor (Knn) Based Electricity Consumption Prediction,” J. Tek. Inform. Jutif, vol. 6, no. 3, pp. 1373–1388, Jun. 2025, doi: 10.52436/1.jutif.2025.6.3.4692.

Y. D. Rosita and L. S. Moonlight, “Perbandingan Metode Prediksi untuk Nilai Jual USD: Holt-Winters, Holt’s, dan Single Exponential Smoothing,” JTIM J. Teknol. Inf. Dan Multimed., vol. 5, no. 4, pp. 322–333, Jan. 2024, doi: 10.35746/jtim.v5i4.473.

B. Zhao, B. Li, J. Zhang, W. Cao, and Y. Gao, “DCLGM: Fusion Recommendation Model Based on LightGBM and Deep Learning,” Neural Process. Lett., vol. 56, no. 1, p. 17, Feb. 2024, doi: 10.1007/s11063-024-11504-4.

T. Sugiura, Y. Yamagishi, and Y. Kishimoto, “Leveraging LightGBM Ranker for Efficient Large-Scale News Recommendation Systems,” in Proceedings of the Recommender Systems Challenge 2024, Bari Italy: ACM, Oct. 2024, pp. 27–31. doi: 10.1145/3687151.3687156.