Analisis Temporal Long Short-Term Memory untuk Prediksi Pendapatan Penjualan dalam Mendukung Ketahanan Finansial UMKM


Rizqa Zahrotun Nafiah(1*); Bambang Agus Herlambang(2); Noora Qotrun Nada(3);

(1) Program Studi Informatika, Universitas PGRI Semarang, Kota Semarang, Indonesia
(2) Program Studi Informatika, Universitas PGRI Semarang, Kota Semarang, Indonesia
(3) Program Studi Informatika, Universitas PGRI Semarang, Kota Semarang, Indonesia
(*) Corresponding Author

  

Abstract


Usaha Mikro, Kecil, dan Menengah (UMKM) memainkan peran penting dalam pembangunan ekonomi, namun banyak UMKM menghadapi ketidakpastian dalam perencanaan pendapatan penjualan akibat fluktuasi permintaan dan terbatasnya pemanfaatan alat peramalan berbasis data. Penelitian ini mengusulkan sistem prediksi pendapatan penjualan berbasis web untuk UMKM menggunakan metode Long Short-Term Memory (LSTM) guna memodelkan data penjualan harian yang memiliki karakteristik nonlinier dan temporal. Dataset yang digunakan terdiri dari data pendapatan penjualan harian selama lima tahun dari UMKM Rayanzza Fashion, dengan total 1.827 data. Arsitektur stacked LSTM dengan tiga lapisan LSTM digunakan dan dievaluasi menggunakan Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan Mean Absolute Percentage Error (MAPE). Hasil eksperimen menunjukkan bahwa model mampu memprediksi pendapatan penjualan harian secara efektif, dengan nilai MAE sebesar 13.904 rupiah, RMSE sebesar 16.410 rupiah, dan MAPE sebesar 4,38% pada data pengujian. Hasil ini menunjukkan kemampuan generalisasi yang baik serta pembelajaran yang efektif terhadap tren penjualan dan pola musiman. Model yang telah dilatih kemudian diintegrasikan ke dalam sistem berbasis web menggunakan React.js sebagai frontend, Express.js sebagai backend, dan FastAPI untuk penyajian model. Penelitian ini memberikan kontribusi berupa sistem prediksi pendapatan penjualan end-to-end yang dapat diimplementasikan untuk mendukung pengambilan keputusan berbasis data pada UMKM.

Keywords


UMKM; Prediksi Pendapatan Penjualan; LSTM; Sistem Berbasis Web

  
  

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doi  https://doi.org/10.33096/busiti.v7i3.3468 		  

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References


S. Vinatra, “Peran Usaha Mikro, Kecil, dan Menengah (UMKM) dalam Kesejahteraan Perekonomian Negara dan Masyarakat,” Jurnal Akuntan Publik, vol. 1, no. 3, hlm. 01–08, Jul 2023, doi: 10.59581/jap-widyakarya.v1i3.832.

N. Aktaviana, A. Awalia, N. Holilah, V. Silviani, dan A. Muslim, “Profil Bisnis Usaha Mikro, Kecil Dan Menengah (Umkm) Selama Pandemi Covid-19 Di Desa Cikakak Kecamatan Cikakak Kabupaten Sukabumi,” Jurnal Abdi Nusa, vol. 3, no. 2, hlm. 87–91, Jun 2023, doi: 10.52005/abdinusa.v3i2.61.

N. I. Okeke, O. A. Bakare, dan G. O. Achumie, “Forecasting financial stability in SMEs: A comprehensive analysis of strategic budgeting and revenue management,” Open Access Research Journal of Multidisciplinary Studies, vol. 8, no. 1, hlm. 139–149, Sep 2024, doi: 10.53022/oarjms.2024.8.1.0055.

G. Wang, M. Huang, S. C. Perera, S. Jiang, dan S.-C. Fang, “Integrating Supply, Production, and Demand Uncertainties in Manufacturing Inventory Systems,” IEEE Trans. Eng. Manag., vol. 72, no. 9, hlm. 3749–3764, Sep 2025, doi: 10.1109/TEM.2025.3599638.

A. A. Yuniarto dan I. W. Siregar, “From Manual to Digital: Modernizing the Sales Recording System for Growth at MSME Rumah Abon,” Journal of Scientific Insights, vol. 2, no. 5, hlm. 510–525, Sep 2025, doi: 10.69930/jsi.v2i5.537.

A. R. Oktavian dan F. Rumaisa, “Penerapan Business Intelligence Terhadap Data Penjualan UMKM (Foodendez) Menggunakan Metode Algoritma Apriori Dalam Menentukan Segmentasi Pasar,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 6, no. 3, hlm. 1740, Jul 2022, doi: 10.30865/mib.v6i3.4338.

N. Hikmah, A. Fauzi, dan F. U. Nayyiroh, “Measuring the forecast accuracy in retail MSMEs: A comparative analysis between AI and traditional methods in the era of digital selling,” Journal of Management and Informatics, vol. 4, no. 1, hlm. 687–705, Apr 2025, doi: 10.51903/jmi.v4i1.166.

G. Tamami dan M. Arifin, “Penggunaan LSTM dalam Membangun Prediksi Penjualan untuk Aplikasi Laptop Lens,” JURNAL FASILKOM, vol. 14, no. 2, hlm. 301–308, Agu 2024, doi: 10.37859/jf.v14i2.7372.

P. Labastida, M. Simbulan, C. J. Estrella, W. Flores, dan C. Criselle, “Web-based ordering system for Start-UP business with forecasting,” World Journal of Advanced Research and Reviews, vol. 22, no. 3, hlm. 357–368, Jun 2024, doi: 10.30574/wjarr.2024.22.3.1721.

T. de Castro Moraes, X. Yuan, dan E. P. Chew, “Hybrid convolutional long short‐term memory models for sales forecasting in retail,” J. Forecast., vol. 43, no. 5, hlm. 1278–1293, Agu 2024, doi: 10.1002/for.3073.

M. M. Aqil dan F. fauzi, “Enhancing E-commerce Supply Chains with Time-Series Forecasting Using Long Short-Term Memory (LSTM) Networks.,” PatternIQ Mining, vol. 99, hlm. 650–655, Feb 2025, doi: 10.70023/sahd/250204.

R. Feng, Z. Wu, dan J. Gong, “Application of Long Short-Term Memory for prediction of monthly tobacco sales volume,” Highlights in Business, Economics and Management, vol. 53, hlm. 192–200, Mar 2025, doi: 10.54097/d7f26736.

U. M. Sirisha, M. C. Belavagi, dan G. Attigeri, “Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison,” IEEE Access, vol. 10, hlm. 124715–124727, 2022, doi: 10.1109/ACCESS.2022.3224938.

M. Khan, A. Ahmad, F. Sobieczky, M. Pichler, B. A. Moser, dan I. Bukovsky, “A Systematic Mapping Study of Predictive Maintenance in SMEs,” IEEE Access, vol. 10, hlm. 88738–88749, 2022, doi: 10.1109/ACCESS.2022.3200694.

A. Vishwanath, M. Basheeruddin, dan S. D, “Forecasting Sales Data Using Time Series Models and LSTM Model,” dalam 2024 Second International Conference on Advances in Information Technology (ICAIT), IEEE, Jul 2024, hlm. 1–6. doi: 10.1109/ICAIT61638.2024.10690408.

I. Ritharson. P, J. Anitha, dan S. I. Alex Pandian, “A Web-based Real-time Sales Data Analysis using LSTM Model for Better Insight,” dalam 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, Mar 2023, hlm. 1060–1064. doi: 10.1109/ICACCS57279.2023.10112837.

Q.-Q. He, C. Wu, dan Y.-W. Si, “LSTM with particle Swam optimization for sales forecasting,” Electron. Commer. Res. Appl., vol. 51, hlm. 101118, Jan 2022, doi: 10.1016/j.elerap.2022.101118.

H. Santosa dan Kusrini, “Book Sales Forecasting with Bidirectional LSTM: Outlier Handling and Overfitting Reduction Using Clipping and Early Stopping,” Dinasti International Journal of Education Management And Social Science, vol. 6, no. 6, hlm. 5328–5334, Sep 2025, doi: 10.38035/dijemss.v6i6.5449.

T. O. Hodson, “Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not,” Geosci. Model Dev., vol. 15, no. 14, hlm. 5481–5487, Jul 2022, doi: 10.5194/gmd-15-5481-2022.

Md. I. A. Efat dkk., “Deep-learning model using hybrid adaptive trend estimated series for modelling and forecasting sales,” Ann. Oper. Res., vol. 339, no. 1–2, hlm. 297–328, Agu 2024, doi: 10.1007/s10479-022-04838-6.

H. Ahaggach, L. Abrouk, dan E. Lebon, “Systematic Mapping Study of Sales Forecasting: Methods, Trends, and Future Directions,” Forecasting, vol. 6, no. 3, hlm. 502–532, Jul 2024, doi: 10.3390/forecast6030028.

O. S. P. Tamba dan Md. M. Hasan, “Hybrid LSTM-MLP Framework for Enhanced Sales Forecasting,” IJACI : International Journal of Advanced Computing and Informatics, vol. 2, no. 2, hlm. 75–82, Okt 2025, doi: 10.71129/ijaci.v2i2.pp75-82.

Y. Ensafi, S. H. Amin, G. Zhang, dan B. Shah, “Time-series forecasting of seasonal items sales using machine learning – A comparative analysis,” International Journal of Information Management Data Insights, vol. 2, no. 1, hlm. 100058, Apr 2022, doi: 10.1016/j.jjimei.2022.100058.

S. Sinsomboonthong, “Performance Comparison of New Adjusted Min-Max with Decimal Scaling and Statistical Column Normalization Methods for Artificial Neural Network Classification,” Int. J. Math. Math. Sci., vol. 2022, hlm. 1–9, Apr 2022, doi: 10.1155/2022/3584406.

K. Moussa, K. M. Elsayed, M. S. Darweesh, A. Elbaz, dan A. Soltan, “Predicting the availability of power line communication nodes using semi-supervised learning algorithms,” Sci. Rep., vol. 15, no. 1, hlm. 17670, Mei 2025, doi: 10.1038/s41598-025-01064-5.

Z. Zhan dan S.-K. Kim, “Versatile time-window sliding machine learning techniques for stock market forecasting,” Artif. Intell. Rev., vol. 57, no. 8, hlm. 209, Jul 2024, doi: 10.1007/s10462-024-10851-x.

R. S. Ahmed, M. Hasnain, M. H. Mahmood, dan M. A. Mehmood, “Comparison of Deep Learning Algorithms for Retail Sales Forecasting,” ICCK Transactions on Intelligent Systematics, vol. 1, no. 3, hlm. 112–126, Okt 2024, doi: 10.62762/TIS.2024.300700.

F. Messaoudi, M. Loukili, dan M. El Ghazi, “Demand Prediction Using Sequential Deep Learning Model,” dalam 2023 International Conference on Information Technology (ICIT), IEEE, Agu 2023, hlm. 577–582. doi: 10.1109/ICIT58056.2023.10225930.

I. Salehin dan D.-K. Kang, “A Review on Dropout Regularization Approaches for Deep Neural Networks within the Scholarly Domain,” Electronics (Basel)., vol. 12, no. 14, hlm. 3106, Jul 2023, doi: 10.3390/electronics12143106.

S. Ullah, A. Javed, M. Aljasem, dan A. K. J. Saudagar, “Eff-ReLU-Net: a deep learning framework for multiclass wound classification,” BMC Med. Imaging, vol. 25, no. 1, hlm. 257, Jul 2025, doi: 10.1186/s12880-025-01785-z.

Y.-L. Peng dan W.-P. Lee, “Practical guidelines for resolving the loss divergence caused by the root-mean-squared propagation optimizer,” Appl. Soft Comput., vol. 153, hlm. 111335, Mar 2024, doi: 10.1016/j.asoc.2024.111335.

M. Reyad, A. M. Sarhan, dan M. Arafa, “A modified Adam algorithm for deep neural network optimization,” Neural Comput. Appl., vol. 35, no. 23, hlm. 17095–17112, Agu 2023, doi: 10.1007/s00521-023-08568-z.

J. Terven, D.-M. Cordova-Esparza, J.-A. Romero-González, A. Ramírez-Pedraza, dan E. A. Chávez-Urbiola, “A comprehensive survey of loss functions and metrics in deep learning,” Artif. Intell. Rev., vol. 58, no. 7, hlm. 195, Apr 2025, doi: 10.1007/s10462-025-11198-7.

H. Yadav dan A. Thakkar, “NOA-LSTM: An efficient LSTM cell architecture for time series forecasting,” Expert Syst. Appl., vol. 238, hlm. 122333, Mar 2024, doi: 10.1016/j.eswa.2023.122333.

Y. Liu, “Deep Learning for Enterprise Sales Prediction: Haressing CNN-LSTM-Attention Model,” dalam 2024 IEEE 3rd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA), IEEE, Feb 2024, hlm. 746–750. doi: 10.1109/EEBDA60612.2024.10485889.

S. Ghimire, T. Celik, M. Gerdes, dan C. W. Omlin, “Deep learning for blood glucose level prediction: How well do models generalize across different data sets?,” PLoS One, vol. 19, no. 9, hlm. e0310801, Sep 2024, doi: 10.1371/journal.pone.0310801.

H. Chen, X. Li, Y. Wu, L. Zuo, M. Lu, dan Y. Zhou, “Compressive Strength Prediction of High-Strength Concrete Using Long Short-Term Memory and Machine Learning Algorithms,” Buildings, vol. 12, no. 3, hlm. 302, Mar 2022, doi: 10.3390/buildings12030302.

A. Mystakidis, P. Koukaras, N. Tsalikidis, D. Ioannidis, dan C. Tjortjis, “Energy Forecasting: A Comprehensive Review of Techniques and Technologies,” Energies (Basel)., vol. 17, no. 7, hlm. 1662, Mar 2024, doi: 10.3390/en17071662.

Y. Tian, D. Su, S. Lauria, dan X. Liu, “Recent advances on loss functions in deep learning for computer vision,” Neurocomputing, vol. 497, hlm. 129–158, Agu 2022, doi: 10.1016/j.neucom.2022.04.127.

A. Mystakidis, P. Koukaras, N. Tsalikidis, D. Ioannidis, dan C. Tjortjis, “Energy Forecasting: A Comprehensive Review of Techniques and Technologies,” Energies (Basel)., vol. 17, no. 7, hlm. 1662, Mar 2024, doi: 10.3390/en17071662.

Y. Sun dan T. Li, “A transformer-based framework for enterprise sales forecasting,” PeerJ Comput. Sci., vol. 10, hlm. e2503, Nov 2024, doi: 10.7717/peerj-cs.2503.

D. S. K. Karunasingha, “Root mean square error or mean absolute error? Use their ratio as well,” Inf. Sci. (N. Y)., vol. 585, hlm. 609–629, Mar 2022, doi: 10.1016/j.ins.2021.11.036.

K. Warneke, S. D. Siegel, J. Afonso, dan S. Wallot, “What the mean absolute percentage error (MAPE) should adopt from Bland–Altman analyses,” German Journal of Exercise and Sport Research, Des 2025, doi: 10.1007/s12662-025-01084-3.

G. M. C. de Araújo, A. L. da Silva, H. S. Gonçalves, dan A. M. M. Correia, “Management of Micro and Small Enterprises (MPEs): Sales Forecasting in the Retail Sector,” Revista de Gestão Social e Ambiental, vol. 18, no. 9, hlm. e08295, Agu 2024, doi: 10.24857/rgsa.v18n9-170.

S. Lima, A. M. Gonçalves, dan M. Costa, “Predictive accuracy of time series models applied to economic data: the European countries retail trade,” J. Appl. Stat., vol. 51, no. 9, hlm. 1818–1841, Jul 2024, doi: 10.1080/02664763.2023.2238249.


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