Online Shop Product Sales Prediction Using Multilayer Perceptron Algorithm
Abstract
This study aims to develop a predictive model for forecasting product sales using the Multilayer Perceptron (MLP) algorithm. The model's performance was evaluated using key metrics, including the Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² score. The model achieved an MAE of 0.861, an MSE of 9.521, and an impressive R² score of 0.999, demonstrating its ability to accurately predict product sales with minimal error. Feature correlation analysis identified key variables related to the target prediction, which is the number of products ready for shipment, underscoring the importance of feature selection in enhancing model performance. Prediction results revealed variability among product sales, with products like Foodpak Matte 245 (Code 49) predicted to sell approximately 244.31 units, while others like Stiker Kertas (Code 90) showed lower sales forecasts. The findings suggest that strategic interventions may be necessary to boost sales for underperforming items and capitalize on the demand for popular products. Future improvements, such as optimizing the network architecture, experimenting with activation functions and optimization algorithms, and incorporating external factors such as market trends, could further enhance the model’s accuracy and predictive power. Overall, the MLP model demonstrates strong potential for product sales forecasting, providing valuable insights for business decision-making.
Keywords
Full Text:
PDFReferences
H. Hartono and A. Zein, “PENERAPAN ALGORITMA GENETIKA DAN JARINGAN SYARAF TIRUAN DALAM PENJADWALAN MATA KULIAH Studi Kasus : Prodi Sistem Informasi Universitas Pamulang,” J. Ilmu Komput., vol. VI, no. 03, pp. 6–10, 2023.
M. A. Mazurowski, M. Buda, A. Saha, and M. R. Bashir, “Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI,” J. Magn. Reson. Imaging, vol. 49, no. 4, pp. 939–954, 2019, doi: 10.1002/jmri.26534.
D. H. Lee, Y. T. Kim, and S. R. Lee, “Shallow landslide susceptibility models based on artificial neural networks considering the factor selection method and various non-linear activation functions,” Remote Sens., vol. 12, no. 7, 2020, doi: 10.3390/rs12071194.
N. R. Sari and Y. Mar’atullatifah, “PENERAPAN MULTILAYER PERCEPTRON UNTUK IDENTIFIKASI KANKER PAYUDARA,” J. Cakrawala Ilm., vol. 2, no. 8, pp. 3261–3268, 2023, doi: 10.31862/9785426311961.
F. Rahmawati and N. Merlina, “Metode Data Mining Terhadap Data Penjualan Sparepart Mesin Fotocopy Menggunakan Algoritma Apriori,” PIKSEL Penelit. Ilmu Komput. Sist. Embed. Log., vol. 6, no. 1, pp. 9–20, 2018, doi: 10.33558/piksel.v6i1.1390.
C. O. Sakar, S. O. Polat, M. Katircioglu, and Y. Kastro, “Real-time prediction of online shoppers’ purchasing intention using multilayer perceptron and LSTM recurrent neural networks,” Neural Comput. Appl., vol. 31, no. 10, pp. 6893–6908, Oct. 2019, doi: 10.1007/s00521-018-3523-0.
A. Utku and M. A. Akcayol, “Deep Learning based prediction model for the next purchase,” Adv. Electr. Comput. Eng., vol. 20, no. 2, pp. 35–44, 2020, doi: 10.4316/AECE.2020.02005.
G. Taher, “E-Commerce: Advantages and Limitations,” Int. J. Acad. Res. Accounting, Financ. Manag. Sci., vol. 11, no. 1, pp. 202–221, Feb. 2021, doi: 10.6007/IJARAFMS/v11-i1/8987.
J. Tandean, R. Indrawan, I. Intan, and S. Ramadhani Arifin, “Pengaruh Penerapan Stochastic Gradient Descent Dan Adam Optimizer Pada Hyperparameter Tuning Untuk Klasifikasi Penyakit Tanaman Ubi Kayu,” J. Dipanegara Komput. Tek. Inform., vol. XVI, no. 1, pp. 80–90, 2023, [Online]. Available: https://www.ejurnal.dipanegara.ac.id/index.php/dipakomti/article/view/1377
J. A. Prakash, V. Ravi, V. Sowmya, and K. P. Soman, “Stacked ensemble learning based on deep convolutional neural networks for pediatric pneumonia diagnosis using chest X-ray images,” Neural Comput. Appl., vol. 35, no. 11, pp. 8259–8279, 2023, doi: 10.1007/s00521-022-08099-z.
J. Xu, Y. Zhou, L. Zhang, J. Wang, and D. Lefloch, “Sportswear retailing forecast model based on the combination of multi-layer perceptron and convolutional neural network,” Text. Res. J., vol. 91, no. 23–24, pp. 2980–2994, Dec. 2021, doi: 10.1177/00405175211020518.
D. Sinha and M. El-Sharkawy, “Thin MobileNet: An Enhanced MobileNet Architecture,” 2019 IEEE 10th Annu. Ubiquitous Comput. Electron. Mob. Commun. Conf. UEMCON 2019, pp. 0280–0285, 2019, doi: 10.1109/UEMCON47517.2019.8993089.
M. A. Hanin, R. Patmasari, and R. Y. Nur, “Sistem Klasifikasi Penyakit Kulit Menggunakan Convolutional Neural Network ( Cnn ) Skin Disease Classification System Using Convolutional Neural Network ( Cnn ),” e-Proceeding Eng., vol. 8, no. 1, pp. 273–281, 2021.
R. Poojary, R. Raina, and A. Kumar Mondal, “Effect of data-augmentation on fine-tuned CNN model performance,” IAES Int. J. Artif. Intell., vol. 10, no. 1, p. 84, Mar. 2021, doi: 10.11591/ijai.v10.i1.pp84-92.
D. T. Tran, S. Kiranyaz, M. Gabbouj, and A. Iosifidis, “Heterogeneous Multilayer Generalized Operational Perceptron,” IEEE Trans. Neural Networks Learn. Syst., vol. 31, no. 3, pp. 710–724, Mar. 2020, doi: 10.1109/TNNLS.2019.2914082.
M. Zohdi, M. Rafiee, V. Kayvanfar, and A. Salamiraad, “Demand forecasting based machine learning algorithms on customer information: an applied approach,” Int. J. Inf. Technol., vol. 14, no. 4, pp. 1937–1947, Jun. 2022, doi: 10.1007/s41870-022-00875-3.
N. Chabane et al., “Intelligent personalized shopping recommendation using clustering and supervised machine learning algorithms,” PLoS One, vol. 17, no. 12, p. e0278364, Dec. 2022, doi: 10.1371/journal.pone.0278364.
P. C. Yang, H. M. Wee, and H. J. Yang, “Global optimal policy for vendor–buyer integrated inventory system within just in time environment,” J. Glob. Optim. 2006 374, vol. 37, no. 4, pp. 505–511, Aug. 2006, doi: 10.1007/S10898-006-9058-4.
A. Salamzadeh, P. Ebrahimi, M. Soleimani, and M. Fekete-Farkas, “Grocery Apps and Consumer Purchase Behavior: Application of Gaussian Mixture Model and Multi-Layer Perceptron Algorithm,” J. Risk Financ. Manag., vol. 15, no. 10, 2022, doi: 10.3390/jrfm15100424.
I. Kurniawan, L. S. Silaban, and D. Munandar, “Implementation of Convolutional Neural Network and Multilayer Perceptron in Predicting Air Temperature in Padang,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 6, pp. 2–7, 2020, doi: 10.29207/resti.v4i6.2456.
DOI: https://doi.org/10.15408/jti.v18i1.44286
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Erica Rian Safitri, Lili Tanti, Wanayumini

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
3rd Floor, Dept. of Informatics, Faculty of Science and Technology, UIN Syarif Hidayatullah Jakarta
Jl. Ir. H. Juanda No.95, Cempaka Putih, Ciputat Timur.
Kota Tangerang Selatan, Banten 15412
Tlp/Fax: +62 21 74019 25/ +62 749 3315
Handphone: +62 8128947537
E-mail: jurnal-ti@apps.uinjkt.ac.id
Jurnal Teknik Informatika by Prodi Teknik Informatika Universitas Islam Negeri Syarif Hidayatullah Jakarta is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at http://journal.uinjkt.ac.id/index.php/ti.
JTI Visitor Counter: View JTI Stats