Analisis Machine Learning untuk Prediksi Permintaan Produk pada Percetakan PT. Next Sihar Dagulon Menggunakan Algoritma Random Forest
DOI:
https://doi.org/10.62712/juktisi.v5i2.1399Keywords:
Machine Learning, Random Forest, Prediksi Permintaan, Percetakan, Data MiningAbstract
Permintaan produk percetakan pada PT. Next Sihar Dagulon mengalami fluktuasi sehingga perusahaan sering menghadapi kesulitan dalam menentukan kebutuhan bahan baku dan kapasitas produksi. Pendekatan prediksi yang masih bersifat manual berpotensi menimbulkan kelebihan stok, kekurangan stok, keterlambatan produksi, dan peningkatan biaya operasional. Penelitian ini bertujuan menganalisis penerapan machine learning menggunakan algoritma Random Forest untuk memprediksi permintaan produk percetakan. Data yang digunakan berupa data historis transaksi penjualan periode Januari 2022 sampai Desember 2024 sebanyak 2.500 data, yang setelah preprocessing menjadi 2.400 data valid. Variabel penelitian meliputi jenis produk, bulan transaksi, harga, jumlah pelanggan, promosi, permintaan periode sebelumnya, dan jumlah permintaan sebagai target. Tahapan penelitian terdiri atas pengumpulan data, preprocessing, transformasi data kategorikal, pembagian data latih dan data uji, pemodelan Random Forest Regressor, serta evaluasi menggunakan MAE, RMSE, MAPE, dan R². Hasil pengujian menunjukkan nilai MAE sebesar 8,74, RMSE sebesar 12,61, MAPE sebesar 7,86%, dan R² sebesar 0,91. Hasil tersebut menunjukkan bahwa Random Forest mampu menghasilkan prediksi permintaan dengan tingkat kesalahan rendah dan dapat digunakan sebagai dasar perencanaan produksi serta pengelolaan stok perusahaan.
Downloads
References
[1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Hoboken, NJ, USA: Pearson, 2021
[2] L. Breiman, "Random forests," Machine Learning, vol. 45, no. 1, pp. 5-32, 2001, doi: 10.1023/A:1010933404324.
[3] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. New York, NY, USA: Springer, 2009, doi: 10.1007/978-0-387-84858-7.
[4] G. Biau and E. Scornet, "A random forest guided tour," TEST, vol. 25, no. 2, pp. 197-227, 2016, doi: 10.1007/s11749-016-0481-7.
[5] P. Probst, M. N. Wright, and A.-L. Boulesteix, "Hyperparameters and tuning strategies for random forest," WIREs Data Mining and Knowledge Discovery, vol. 9, no. 3, 2019, doi: 10.1002/widm.1301.
[6] F. Pedregosa et al., "Scikit-learn: Machine learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011. DOI: -.
[7] S. Makridakis, E. Spiliotis, and V. Assimakopoulos, "Statistical and machine learning forecasting methods: Concerns and ways forward," PLOS ONE, vol. 13, no. 3, 2018, doi: 10.1371/journal.pone.0194889.
[8] R. J. Hyndman and A. B. Koehler, "Another look at measures of forecast accuracy," International Journal of Forecasting, vol. 22, no. 4, pp. 679-688, 2006, doi: 10.1016/j.ijforecast.2006.03.001.
[9] H. Chen, R. H. L. Chiang, and V. C. Storey, "Business intelligence and analytics: From big data to big impact," MIS Quarterly, vol. 36, no. 4, pp. 1165-1188, 2012, doi: 10.2307/41703503.
[10] T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2016, pp. 785-794, doi: 10.1145/2939672.2939785.
[11] G. James, D. Witten, T. Hastie, R. Tibshirani, and J. Taylor, An Introduction to Statistical Learning: With Applications in Python. Cham, Switzerland: Springer, 2023, doi: 10.1007/978-3-031-38747-0.
[12] I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, 4th ed. Burlington, MA, USA: Morgan Kaufmann, 2017, doi: 10.1016/C2015-0-02071-8.
[13] M. Kuhn and K. Johnson, Applied Predictive Modeling. New York, NY, USA: Springer, 2013, doi: 10.1007/978-1-4614-6849-3.
[14] J. H. Friedman, "Greedy function approximation: A gradient boosting machine," Annals of Statistics, vol. 29, no. 5, pp. 1189-1232, 2001, doi: 10.1214/aos/1013203451.
[15] C. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006. DOI: -.
[16] D. Chicco, M. J. Warrens, and G. Jurman, "The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation," PeerJ Computer Science, vol. 7, 2021, doi: 10.7717/peerj-cs.623.
[17] J. Bergstra and Y. Bengio, "Random search for hyper-parameter optimization," Journal of Machine Learning Research, vol. 13, pp. 281-305, 2012. DOI: -.
[18] L. Rokach, "Ensemble-based classifiers," Artificial Intelligence Review, vol. 33, pp. 1-39, 2010, doi: 10.1007/s10462-009-9124-7.
[19] A. Natekin and A. Knoll, "Gradient boosting machines, a tutorial," Frontiers in Neurorobotics, vol. 7, 2013, doi: 10.3389/fnbot.2013.00021.
[20] P. N. Tan, M. Steinbach, A. Karpatne, and V. Kumar, Introduction to Data Mining, 2nd ed. Boston, MA, USA: Pearson, 2019. DOI: -.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Lamsihar Banjarnahor, Muhammad Irfan Sarif, Muhammad Putra Novelan
This work is licensed under a Creative Commons Attribution 4.0 International License.
