Keywords
Artificial Neural Network
Raw material control
Inventory management
Production optimization
How to Cite
Abstract
This research addresses the challenges faced by food manufacturing companies, focusing on UD. XYZ as a case study. With fluctuating sales levels causing raw material buildup and shortages, the study proposes an improved sales forecasting method to enhance raw material control. By comparing Artificial Neural Network (ANN) and Double Exponential Smoothing Holts, the research aims to optimize inventory management and production processes. Results indicate ANN's superiority over Holts, with an accuracy rate of 0.118 compared to 11.639. The ANN model accurately forecasts sales for the upcoming twelve-month period, highlighting a decline from July 2023 to May 2024. Implementing advanced forecasting methods can mitigate raw material-related risks and enhance operational efficiency for companies like UD. XYZ.
Highlight:
- Enhanced sales prediction methods crucial for inventory planning.
- Artificial Neural Network outperforms traditional forecasting techniques.
- Improved forecasting mitigates raw material shortages and excesses.
Keywoard: Sales forecasting, Artificial Neural Network, Raw material control, Inventory management, Production optimization.
References
A. Lusiana, P. Yuliarty, “Penerapan Metode Peramalan (Forecasting) Pada Permintaan Atap di PT X”, Jurnal Teknik Industri, vol. 10, no. 5, pp. 11-20, 2020.
B. W. N. Tantyo, D. Swanjaya, “Perbandingan Antara Metode Holt Winter Dan Backpropagation Pada Model Peramalan Penjualan” Jurnal Seminar Nasional Inovasi Teknologi, vol. 3, no, 1, pp. 174-181, 2021.
A. Dzulfikar, Iswanto, N. Ramsari, S. Sutjiningtyas, Hernawati, “Implementasi Peramalan Penjualan Produk di PT. Prima Per Tradea Utama Menggunakan Metode Artificial Neural Network”, Jurnal Teknologi Informasi dan Komunikasi, vol. 11, no. 2, pp.10-11, 2021.
I. Solikin, S. Hardini, “Aplikasi Forecasting Stok Barang Menggunakan Metode Weighted Moving Average (WMA) Pada Metrojaya Komputer”, Jurnal Pengembangan, vol. 4, no. 2, pp. 100-105, 2019, doi: 10.3059/jpt.v4i2.1373.
J. R. Saragih, M. B. S. Saragih, A. Wanto, “Analisis Algoritma Backpropagation Dalam Prediksi Nilai Ekspor (Juta USD)”, Jurnal Pendidikan Teknologi dan Kejuruan, vol. 15, no. 2, pp. 254-264, 2018, doi: https://ejournal.undiksha.ac.id/index.php/JPTK/issue/view/851.
I. Yulian, D. S. Anggraeni, Q. Aini. “Penerapan Metode Trend Moment Dalam Forecasting Penjualan Produk CV. Rabbani Asyisa”, Jurnal Teknologi dan Sistem Informasi, vol. 6, no. 2, pp. 193-200, 2020, doi: https://doi.org/10.33330/jurteksi.v6i2.443.
E. E. Pratama, H. Sastypratiwi, Yulianti, “ Analisis Kecenderungan Informasi Terkait Covid-19 Berdasarkan Big Data Sosial Media Dengan Menggunakan Metode Data Mining”, Jurnal Informasi Polinema, vol. 7, no. 2, pp. 1-6, 2021.
J. Han, M. Kamber, J. Pei. “ Data Mining Concepts And Techniques” Edisi ke-3, USA: Morgan Kaufmann, 2006.
Z. Nabila, A. R. Isnain, Z. Abidin, “Analisis Data Mining Untuk Clustering Kasus Covid-19 Di Provinsi Lampung Dengan Algoritma K-Means”, Jurnal Teknologi dan Sistem Informasi, vol. 2, no. 2, pp. 100-108, 2021.
B. Santosa, A. Umam, “Data Mining dan Big Data Analytics”, Edisi ke-2, Yogyakarta: Penebar Media Pustaka, 2018.
H. D. Wijaya, S. Dwiasnati, “Implementasi Data Mining Dengan Algoritma Naïve Bayes Pada Penjualan Obat”, Jurnal Informatika, vol. 7, no. 1, pp. 1-7, 2020.
M. N. Zain, “Algoritma Artificial Neural Network Dalam Klasifikasi Chest X-Rays Pasien Covid-19”, Jurnal Riset Statistika, vol. 2, no. 2, pp. 137-144, 2022. Doi: https://doi.org/10.29313/jrs.v2i2.1426.
M. F. Mahfuzh, R. V. Yuliantari, “Analisis Penerapan Artificial Neural Network Algoritma Propagasi Balik Untuk Meramalkan Harga Saham Pada Bursa Efek Indonesia”, vol. 6, no. 1, pp. 1-3, 2022.
N. F. Hasan, Kusrini, H. A. Fatta, “Peramalan Jumlah Penjualan Menggunakan Jaringan Syaraf Tiruan Backpropagation Pada Perusahaan Air Minum Dalam Kemasan”, Jurnal Teknik Informatika dan Sistem Informasi, vol. 5, no. 2, pp. 179-188, 2019.
A. Ambarwati, Q. J. Adrian, Y. Herdiyeni, ”Analisis Pengaruh Data Scaling Terhadap Performa Algoritme Machine Learning Untuk Identifikasi Tanaman”, Jurnal Rekayasa Sistem dan Teknologi Informasi, vol. 4, no. 1, pp. 117-122, 2019, http://jurnal.iaii.or.id.
M. Y. Habibi, E. Riksakomara, “Peramalan Harga Garam Konsumsi Menggunakan Artificial Neural Network Feedforward Backpropagation (Studi Kasus: PT. Garam, Rembang, Jawa Tengah), Jurnal Teknik, vol. 6, no. 2, pp. 306-310, 2018
V. N. Oktavianty, T. Sukmono, “Optimalisasi Penentuan Biaya Minimum Pada Pengendalian Persediaan Bahan Baku Dengan Menggunakan Metode Dynamic Programming (Studi Kasus di PT. XYZ)”, Jurnal Spektrum Industri, vol. 18, no. 1, pp. 18-22, 2020, doi: http://dx.doi.org/10.12928/si.v18i1.10972
C.V. Hudiyanti, F. A. Bachtiar, B. D. Setiawan, “Perbandingan Double Moving Average dan Double Exponential Smoothing Untuk Peramalan Jumlah Kedatangan Wisata Mancanegara di Bandara Ngurah Rai”, Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 3, no. 3, pp. 2667-2672, 2019.
M. A. Putri, T. Sukomo, “Analisa Peramalan Penjualan Kerupuk Udang Dengan Menggunakan Metode Artificial Neural Network (ANN)”, pp. 1-9, 2022.
Humairo, D. P. Habsari, I. Purnamasari, D. Desi Yuniarti, “Peramalan Menggunakan Metode Double Exponential Smoothing Dan Verifikasi Hasil Peramalan Menggunakan Grafik Pengendalian Tracking Signal”, Jurnal Ilmu Matematika dan Terapan, vol. 14, no.1, pp. 013-022, 2020, doi: https://doi.org/10.30598barekengvol14iss1pp013-022.
K. R. P. Irawan, T. Sukmono, “Planning Total Veener Production PT XYZ”, Jurnal Procedia Of Engineering And Life Science, vol. 1, no.2, 2021.
D. Kusbiamto, Y. Ariyanto, M. R. T. Billah, ”Implementasi Metode Triple Exponential Smoothing Pada Sistem Peramalan Permintaan Produk Furniture”, Jurnal Informatika Aplikatif Polinema, 2020.
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