MODEL JARINGAN SARAF TIRUAN UNTUK PREDIKSI PERMINTAAN PRODUK UMKM DI PEMATANG SIANTAR
Abstract
This study aims to develop an Artificial Neural Network (ANN) model in predicting demand for MSME products in Pematangsiantar to optimize production and inventory management. The main problem faced by MSME actors is demand uncertainty which causes excess or shortage of stock, thus affecting business efficiency. The ANN model is applied with a guided learning approach using the backpropagation algorithm to analyze demand patterns based on historical sales data. Data were obtained from the Cooperatives and MSMEs Office of Pematangsiantar City and interviews with business actors. The research process includes data collection and pre-processing, variable selection, data sharing, model development, training, optimization, and evaluation using the Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percent Error (MAPE) metrics. The results of the study show that the ANN model with the backpropagation algorithm is able to provide accurate demand predictions, with a MAPE value below 10%, which indicates very good forecasting. The implementation of this model helps make it easier for MSMEs to make strategic decisions related to production and inventory, thereby increasing competitiveness in the market.
References
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[22] W. Satria, “Jaringan Syaraf Tiruan Backpropagation Untuk Peramalan Penjualan Produk (Studi Kasus Di Metro Electronic Dan Furniture),” Djtechno J. Teknol. Inf., vol. 1, no. 1, pp. 14–19, 2021, doi: 10.46576/djtechno.v1i1.966.
[23] R. T. Siregar, H. P. Silitonga, and J. A. Putri, “Strategi Pengembangan Usaha Mikro, Kecil, Dan Menengah (Umkm) Di Kota Pematangsiantar,” J. Konsep Bisnis Dan Manaj., vol. 6, no. 2, pp. 133–142, 2020.
[2] Badan Pusat Statistik Kota Pematangsiantar, “Kota Pematangsiantar Dalam Angka 2023,” Kota Pematangsiantar Dalam Angka2023, 2023.
[3] O. M. Adisa et al., “Application of artificial neural network for predicting maize production in South Africa,” Sustain., vol. 11, no. 4, pp. 1–17, 2019, doi: 10.3390/su11041145.
[4] V. Amaratunga, L. Wickramasinghe, A. Perera, J. Jayasinghe, U. Rathnayake, and J. G. Zhou, “Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data,” Math. Probl. Eng., vol. 2020, 2020, doi: 10.1155/2020/8627824.
[5] S. Amiludin and N. A. Mahbubah, “Evaluasi Peramalan Permintaan Produk Kopi Bubuk Menggunakan Pendekatan Time Series Di Ukm Eyang Kakung-Gresik,” Sigma Tek., vol. 6, no. 1, pp. 33–043, 2023.
[6] I. Ayu Rahayu Nirahim et al., “Implementasi Sistem Peramalan Persediaan Bahan Baku Laundry Dengan Metode Weighted Moving Average,” Digit. Intell., vol. 3, no. 1, pp. 32–44, 2022.
[7] M. David, I. Cholissodin, and N. Yudistira, “Prediksi Harga Cabai menggunakan Metode Long-Short Term Memory (Case Study : Kota Malang),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 3, pp. 1214–1219, 2023.
[8] T. Fakhta Tri Nasution and A. Ridho Lubis, “Analisis Metode Trend Moment Sebagai Peramalan (Forecast) Penjualan UMKM Dimsum Analysis of the Trend Moment Method for Forecasting Dimsum MSME Sales,” Januari, vol. 2023, no. 2, pp. 117–126, 2022.
[9] A. Firmansyah and M. Akbar, “Implementasi Neural Network Untuk Prediksi Penjualan Produk (Studi Kasus Penjualan Siomay),” Progresif J. Ilm. Komput., vol. 18, no. 1, p. 115, 2022, doi: 10.35889/progresif.v18i1.808.
[10] K. M. Hindrayani, A. Anjani, and A. L. Nurlaili, “Penerapan Machine Learning pada Penjualan Produk UMKM : Studi Literatur,” Pros. Semin. Nas. Sains Data, vol. 1, no. 01, pp. 19–23, 2021.
[11] S. Sonang and E. Sirait, “Expert system rekomendasi perjalanan wisata danau toba monaco of asia,” vol. 6, pp. 446–453, 2023, doi: 10.37600/tekinkom.v6i2.1117.
[12] D. Prasetyo, V. D. Nurmalia, and L. E. Wijayanti, “Pemanfaatan Sistem Informasi Cerdas untuk Prediksi Kebutuhan Sumber Daya Manusia oleh UMKM Utilization of Intelligent Information Systems for Predicting …,” Download.Garuda.Kemdikbud.Go.Id, vol. 7, no. Februari, pp. 52–64, 2020.
[13] A. Revi, S. Solikhun, and I. Parlina, “Jaringan Syaraf Tiruan Dalam Memprediksi Tingkat Pertumbuhan Industri Mikro Dan Kecil Berdasarkan Provinsi,” Teknika, vol. 7, no. 2, pp. 129–137, 2018, doi: 10.34148/teknika.v7i2.123.
[14] S. S. Sitanggang, Y. Yuhandri, and Adil Setiawan, “Image Transformation With Lung Image Thresholding and Segmentation Method,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 7, no. 2, pp. 278–285, Mar. 2023, doi: 10.29207/resti.v7i2.4321.
[15] A. R. D. Mandala, F. R. Hidayat, R. Primadian, W. Sutopo, Y. Yuniaristanto, and D. Prianjani, “Perbandingan Metode Trend Line Analysis dan Metode Jaringan Syaraf Tiruan Backpropagation untuk Peramalan Permintaan Koran,” Performa Media Ilm. Tek. Ind., vol. 21, no. 2, p. 190, 2022, doi: 10.20961/performa.21.2.58135.
[16] D. I. Mulyana, YkhSanur, FikriYadi, Sahroni, and A. S. Sumarsono, “Penerapan Metode Neural Network Dengan Struktur Backpropagation Untuk Memprediksi Kebutuhan Stok Pada Toko Umkm Perlengkapan Bayi Babyqu,” J. Indones. Manaj. Inform. dan Komun., vol. 4, no. 1, pp. 121–128, 2023, doi: 10.35870/jimik.v4i1.131.
[17] N. A. Santoso, N. Fadilah, R. D. Kurniawan, and A. Supratman, “Penerapan Neural Network Method dengan Struktur Backpropagation dalam Menentukan Prediksi StockBarang,” Ris. dan E-Jurnal Manaj. Inform. Komput., vol. 7, no. 3, pp. 1585–1593, 2023.
[18] S. Sonang, A. T. Purba, and S. Sirait, “PREDIKSI PRESTASI MAHASISWA DENGAN MENGGUNAKAN ALGORITMA BACKPROPAGATION,” J. Tek. Inf. dan Komput., vol. 5, no. 1, p. 67, Jun. 2022, doi: 10.37600/tekinkom.v5i1.512.
[19] S. Sonang, S. Defit, and M. Ramadhan, “Analisis Optimasi Fungsi Pelatihan Machine Learning Neural Network dalam Peramalan Kemiskinan,” J. Edukasi dan Penelit. Inform., vol. 7, no. 3, pp. 359–369, 2021, [Online]. Available: https://www.bps.go.id/.
[20] P. Ipa, F. Keguruan, and U. Tidar, “Ininnawa+201206,” vol. 01, no. 02, pp. 201–206, 2023.
[21] A. Kopka and D. Fornahl, “Artificial intelligence and firm growth — catch-up processes of SMEs through integrating AI into their knowledge bases,” Small Bus. Econ., 2023, doi: 10.1007/s11187-023-00754-6.
[22] W. Satria, “Jaringan Syaraf Tiruan Backpropagation Untuk Peramalan Penjualan Produk (Studi Kasus Di Metro Electronic Dan Furniture),” Djtechno J. Teknol. Inf., vol. 1, no. 1, pp. 14–19, 2021, doi: 10.46576/djtechno.v1i1.966.
[23] R. T. Siregar, H. P. Silitonga, and J. A. Putri, “Strategi Pengembangan Usaha Mikro, Kecil, Dan Menengah (Umkm) Di Kota Pematangsiantar,” J. Konsep Bisnis Dan Manaj., vol. 6, no. 2, pp. 133–142, 2020.
Published
2024-12-31
How to Cite
SONANG, Sahat; SINAGA, Kalvin.
MODEL JARINGAN SARAF TIRUAN UNTUK PREDIKSI PERMINTAAN PRODUK UMKM DI PEMATANG SIANTAR.
Jurnal Teknik Informasi dan Komputer (Tekinkom), [S.l.], v. 7, n. 2, p. 1054-1060, dec. 2024.
ISSN 2621-3079.
Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/1849>. Date accessed: 23 mar. 2025.
doi: https://doi.org/10.37600/tekinkom.v7i2.1849.
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