Sales Prediction Analysis and Startegy for Increasing the Value of Basic Needs Products at PT. Bella Santika Group
Analisis Prediksi Penjualan dan Strategi Peningkatan Nilai Produk Kebutuhan Pokok pada PT. Bella Santika Group
DOI:
https://doi.org/10.21070/ups.6301Keywords:
Sales Prediction, K-Nearest Neighbor, Product Value, Value ChainAbstract
The wholesale industry for daily necessities is currently experiencing rapid growth. Maintaining excess inventory can lead to high storage costs and the risk of product quality decreasing, both physically and out of date. By aligning inventory levels with demand, businesses can reduce risk and increase profits. This research focuses on developing a Unilever product sales prediction system using the K-Nearest Neighbor (KNN) method and Value Chain Analysis to assess results and sales. The dataset consists of sales data for twelve months from January 2023 to December 2023, which will be used for one year of Training data and three months of tasking data. RapidMiner will be used for data processing to categorize sales levels as not selling, medium, selling, and very selling.
Downloads
References
A. Alfani W.P.R., F. Rozi, and F. Sukmana, “Prediksi Penjualan Produk Unilever Menggunakan Metode K-Nearest Neighbor,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 6, no. 1, pp. 155–160, 2021, doi: 10.29100/jipi.v6i1.1910.
E. Purnomo et al., “Penerapan Metode Trend Moment Untuk Forecast Penjualan Barang di Indomaret,” Pros. Semin. Ilmu Komput. dan Teknol. Inf., vol. 3, no. 1, 2018.
S. P. Dewi, N. Nurwati, and E. Rahayu, “Penerapan Data Mining Untuk Prediksi Penjualan Produk Terlaris Menggunakan Metode K-Nearest Neighbor,” Build. Informatics, Technol. Sci., vol. 3, no. 4, pp. 639–648, Mar. 2022, doi: 10.47065/bits.v3i4.1408.
A. A. Leasiwal, A. M. Andrian, and I. V Masala, “Implementasi Algoritma C4. 5 Untuk Klasifikasi Harga Emas,” J. Tek. Inform. Unika St. Thomas, vol. 06, pp. 1–12, 2021.
S. Zhang, “Challenges in KNN Classification,” IEEE Trans. Knowl. Data Eng., pp. 1–13, 2021, doi: 10.1109/TKDE.2021.3049250.
H. Leidiyana, “Penerapan Algoritma K-Nearest Neighbor Untuk Penentuan Resiko Kredit Kepemilikan Kendaraan Bermotor,” J. Penelit. Ilmu Komputer, Syst. Embed. Log., vol. 1, no. 1, pp. 65–76, 2013.
M. Ghita, “牛 犇 1 王 储 2 宋明皓 3,” Hub. Pengetah. Ibu Hamil dan Tingkat Ekon. tentang Kejadian Stunting, vol. 3, no. 2,
Downloads
Additional Files
Posted
License
Copyright (c) 2024 UMSIDA Preprints Server
This work is licensed under a Creative Commons Attribution 4.0 International License.
