Penerapan Algoritma Random Forest Untuk Prediksi Penjualan Dan Sistem Persediaan Produk
DOI:
https://doi.org/10.30865/resolusi.v5i1.2149Keywords:
Random Forest Algorithm; Sales Prediction; Inventory ManagementAbstract
This study aims to optimize sales prediction and inventory management of Bolen Crispy products in Pekalongan Village using the Random Forest algorithm. Bolen Crispy entrepreneurs face challenges in the form of large sales fluctuations and difficulties in managing inventory efficiently. Mistakes in estimating the amount of sales can lead to shortages or excess stock, which has an impact on increasing operational costs and reducing customer satisfaction. This problem of overstocking and understocking has the potential to cause financial losses. The Random Forest algorithm was chosen because of its ability to handle complex data and produce more accurate predictions. By utilizing historical sales data, this algorithm is applied to predict product demand. Testing was carried out using sales data for one year, with a division of 80% for training and 20% for testing. Initial results show that the use of the Random Forest algorithm can increase the accuracy of sales predictions by up to 85%, compared to conventional methods. With more accurate predictions, inventory management becomes more efficient, reducing the risk of shortages and excess stock.
Downloads
References
A. Nurdin, R. Amelia Zunaidi, M. Arkan Fauzan Wicaksono, and A. Lobita Japtara Martadinata, “Analisis Kredit Pembayaran Biaya Kuliah Dengan Pendekatan Pembelajaran Mesin,” J. Teknol. Inf. dan Ilmu Komput., vol. 10, no. 2, pp. 271–280, 2023, doi: 10.25126/jtiik.20231026301.
F. Wang and J. Aviles, “Enhancing Operational Efficiency: Integrating Machine Learning Predictive Capabilities in Business Intellgence for Informed Decision-Making,” Front. Business, Econ. Manag., vol. 9, no. 1, pp. 282–286, 2023, doi: 10.54097/fbem.v9i1.8694.
K. B. Praveen, P. Kumar, J. Prateek, G. Pragathi, and P. M. J, “Inventory Management System Using Machine Learning,” Int. J. Innov. Eng. Manag. Res., vol. 9, no. 06, pp. 769–785, 2022, doi: 10.48047/ijiemr/v11/i06/51.
I. Khan, A. I. Malik, and B. Sarkar, “A distribution-free newsvendor model considering environmental impact and shortages with price-dependent stochastic demand,” Math. Biosci. Eng., vol. 20, no. 2, pp. 2459–2481, 2023, doi: 10.3934/mbe.2023115.
E. Martins and N. V. Galegale, “Sales forecasting using machine learning algorithms,” Rev. Gestão e Secr. (Management Adm. Prof. Rev., vol. 14, no. 7, pp. 11294–11308, 2023, doi: 10.7769/gesec.v14i7.1670.
Prof. Kiran Likhar, Anish Jha, Sudarshan Tiwari, Alice Sunar, Sanjana Shahu, and Shruti Thate, “Machine Learning-Based Sales Prediction and Inventory Management for Grocery Stores,” Int. J. Adv. Res. Sci. Commun. Technol., pp. 24–32, 2023, doi: 10.48175/ijarsct-13605.
D. R. Putri, D. Swanjaya, and I. N. Farida, “Model Integrasi Algoritma Spectral Clustering Dan Backpropagation Pada Prediksi Penjualan Barang,” J. Nusant. Eng., vol. 7, no. 1, pp. 59–66, 2024, [Online]. Available: https://ojs.unpkediri.ac.id/index.php/noe
R. M. van Steenbergen and M. R. K. Mes, “Forecasting demand profiles of new products,” Decis. Support Syst., vol. 139, no. September, p. 113401, 2020, doi: 10.1016/j.dss.2020.113401.
Putri Choirunisa, “Implementasi Artificial Inteligence Untuk Memprediksi Harga Penjualan Rumah Menggunakan Metode Random Forest Dan Flask,” UII.ac.id, pp. 1–101, 2020.
W. Candra, A. Dharma, C. Christnatalis, and J. P. Turnip, “Implementatiton of Random Forest Algortihm on Sales Data To Predict Churn Potential in Suzuya Supermarket Products,” SinkrOn, vol. 8, no. 2, pp. 866–872, 2023, doi: 10.33395/sinkron.v8i2.12243.
S. Anisya, J. Prayudha, and S. Murniyanti, “Implementasi Metode Random Forest Pada Sistem Persediaan Bahan Kimia Di Laboraturium Forensik Cabang Medan,”, 2020.
A. M. M. Fattah, A. Voutama, N. Heryana, and N. Sulistiyowati, “Pengembangan Model Machine Learning Regresi sebagai Web Service untuk Prediksi Harga Pembelian Mobil dengan Metode CRISP-DM,” JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 5, p. 1669, 2022, doi: 10.30865/jurikom.v9i5.5021.
Prithi M and Tamizharasi K, “Revolutionizing Retail: Machine Learning Applications for Enhanced Customer Experience and Operational Efficiency,” Int. J. Eng. Technol. Manag. Sci., vol. 7, no. 5, pp. 408–411, 2023, doi: 10.46647/ijetms.2023.v07i05.049.
M. E. Sulistyo et al., “Web-Based Health Service Management Information System Development With The Linear Sequential Model Method,” E3S Web Conf., vol. 465, pp. 1–6, 2023, doi: 10.1051/e3sconf/202346502066.
N. Rachma and I. Muhlas, “Comparison Of Waterfall And Prototyping Models In Research And Development (R&D) Methods For Android-Based Learning Application Design,” J. Inov. Inov. Teknol. Inf. dan Inform., vol. 5, no. 1, p. 36, 2022, doi: 10.32832/inova-tif.v5i1.7927.
P. M. J. Samonte, E. Britanico, K. E. M. Antonio, J. E. J. Dela Vega, T. J. P. Espejo, and D. C. Samonte, “Applying Deep Learning for the Prediction of Retail Store Sales,” IEOM Soc. Int., vol. 3, no. 20, pp. 1–11, 2023, doi: 10.46254/af03.20220028.
Mahmud Mustapa, Erwin Gatot Amiruddin, Ezra Maharani Chaniago, and Ummiati Rahmah, “Web-Based Student Payment Administration Information System Using The Waterfall Method,” Ceddi J. Educ., vol. 2, no. 2, pp. 11–23, 2023, doi: 10.56134/cje.v2i2.52.
B. V. Thummadi and K. Lyytinen, “How much method-in-use matters? A case study of agile and waterfall software projects and their design routine variation,” J. Assoc. Inf. Syst., vol. 21, no. 4, pp. 864–900, 2020, doi: 10.17705/1jais.00623.
A. Saravanos and M. X. Curinga, “Simulating the Software Development Lifecycle: The Waterfall Model,” Appl. Syst. Innov., vol. 6, no. 6, 2023, doi: 10.3390/asi6060108.
R. Kang, “Sales Prediction of Big Mart Based on Linear Regression , Random Forest , and Gradient Boosting,” EWA Publ., vol. 17, pp. 200–207, 2023, doi: 10.54254/2754-1169/17/20231094.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Penerapan Algoritma Random Forest Untuk Prediksi Penjualan Dan Sistem Persediaan Produk
ARTICLE HISTORY
Issue
Section
Copyright (c) 2024 Muhammad Syahrul Efendi, Sarwido, Akhmad Khanif Zyen
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
