Prediksi Penjualan Produk Pepsodent Unilever dengan Algoritma K-Nearest Neighbor
-
Dzikra Maulida
*
Universitas Islam Negeri Sumatera Utara, Medan,
Indonesia
- Yusuf Ramadhan Nasution Universitas Islam Negeri Sumatera Utara, Medan, Indonesia (*) Corresponding Author
Abstract
In the era of globalisation and increasingly fierce market competition, companies are striving to improve their operational efficiency and marketing strategies to maintain market share and increase revenue. PT Unilever Tbk, as one of the multinational companies that operates various types of consumer products, including dental care products such as Pepsodent, requires reliable sales prediction to maximise its product performance in the market. The main objectives of this research are to apply the K-Nearest Neighbor method to Unilever pepsodent products in a prediction model that can preprocess pepsodent product data for the last 1 year using Rapid Miner and to measure the accuracy of Pepsodent product sales predictions. The data used is the number of stocks, types of pepsodent, sales, seasonal factors. From the results of analysis and evaluation, it can be concluded that the prediction accuracy in the K-NN algorithm is able to provide fairly accurate sales predictions for Pepsodent Whitening products with a value of 161, 186, 165 equally 114. Pepsodent Economy with a value of 982 predictions 1021, a value of 638 and 774 predictions are both 927. Pepsodent Herbal with a value of 173 predicted 193 and a value of 129 and 118 predicted values are both 207. Accurate sales predictions are helpful in production planning and marketing strategies, which in turn can improve operational efficiency and customer satisfaction. The K-NN algorithm proved to be effective in this case, although proper selection of the K parameter is essential to obtain the best results.
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.
U. Nijunnihayah, S. S. Hilabi, F. Nurapriani, and E. Novalia, “Implementasi Algoritma K-Nearest Neighbor untuk Prediksi Penjualan Alat Kesehatan pada Media Alkes,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 2, pp. 695–701, 2024, doi: 10.57152/malcom.v4i2.1326.
O. Sihombing, E. Sitanggang, E. Luis, and K. W. Winata, “Analisis Spare Part Harbour Tag Pada Divisi Workshop Menggunakan Algoritma Knn Min-Max Scaling,” J. TEKINKOM, vol. 6, no. 1, pp. 72–80, 2023, doi: 10.37600/tekinkom.v6i1.742.
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, 2022, doi: 10.47065/bits.v3i4.1408.
W. Yusuf, R. Witri, and C. Juliane, “Model Prediksi Penjualan Jenis Produk Tekstil Menggunakan Algoritma K-Nearest Neighbor (K-NN),” IJCIT (Indonesian J. Comput. Inf. Technol., vol. 7, no. 1, pp. 1–6, 2022, doi: 10.31294/ijcit.v7i1.11973.
D. Rizkya, H. Roosaputri, and C. Dewi, “Perbandingan Algoritma ARIMA, Prophet, dan LSTM dalam Prediksi Penjualan Tiket Wisata Taman Hiburan (Studi Kasus: Saloka Theme Park),” J. Penerapan Sist. Infomatika (Komputer Manajemen), vol. 4, no. 3, pp. 507–517, 2023.
R. Rismala, I. Ali, and A. Rizki Rinaldi, “Penerapan Metode K-Nearest Neighbor Untuk Prediksi Penjualan Sepeda Motor Terlaris,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 1, pp. 585–590, 2023, doi: 10.36040/jati.v7i1.6419.
J. Dongga, A. ` Sarungallo, N. Koru, and G. Lante, “Implementasi Data Mining Menggunakan Algoritma Apriori Dalam Menentukan Persediaan Barang (Studi Kasus: Toko Swapen Jaya Manokwari),” G-Tech J. Teknol. Terap., vol. 7, no. 1, pp. 119–126, 2023, doi: 10.33379/gtech.v7i1.1938.
Dodi Nofri Yoliad, “Data mining Dalam Analisis Tingkat Penjualan Barang ElektronikMenggunakan Algoritma K-means,” Insearch (Information Syst. Res. J., vol. 3, no. 1, 2023.
I. K. J. Arta, G. Indrawan, and G. Rasben Dantes, “Data Mining Rekomemdasi Calon Mahasiswa Berprestasi di STMIK Denpasar Menggunakan Metode Technique For Other Reference By Similarity to Ideal Solution,” J. Ilmu Komput. Indones., vol. 4, no. 1, pp. 11–21, 2019.
S. G. Ahmad, D. Arifianto, and W. Suharso, “Penerapan Data Mining Untuk Klasifikasi Gaya Belajar Siswa Menggunakan Algoritma C4.5,” Univ. Muhammadiyah Jember, vol. 3, no. 5, pp. 502–510, 2022.
Nikmatun, I. Alvi, Waspada, and Indra, “Implementasi Data Mining Untuk Klasifikasi Masa Studi Mahasiswa Menggunakan Algoritma K-Nearest Neighbor,” J. SIMETRIS, vol. 10, no. 2, pp. 421–432, 2019.
F. V. Sari and A. Wibowo, “Analisis Sentimen Pelanggan Toko Online Jd.Id Menggunakan Metode Naïve Bayes Classifier Berbasis Konversi Ikon Emosi,” J. SIMETRIS, vol. 10, no. 2, pp. 681–686, 2019.
D. M. Y. Sinurat, D. E. Ratnawati, and D. W. Brata, “Analisis Sentimen Terhadap Kenaikan Cukai Rokok pada Media Sosial Twitter menggunakan Algoritma Naïve Bayes Classifier,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 1, pp. 17–25, 2023.
M. Hariyanto, M. Kholiq, A. Yani, and Narti, “Klasifikasi Opportunity Menggunakan Algoritma C4.5, C4.5 Dan Naïve Bayes Berbasis Particle Swarm Optimization,” Inti Nusa Mandiri, vol. 14, no. 2, pp. 133–138, 2020.
J. Homepage, S. R. Cholil, T. Handayani, R. Prathivi, and T. Ardianita, “Implementasi Algoritma Klasifikasi K-Nearest Neighbor (KNN) Untuk Klasifikasi Seleksi Penerima Beasiswa,” IJCIT (Indonesian J. Comput. Inf. Technol., vol. 6, no. 2, pp. 118–127, 2021.
S. Widia Pebrianti, R. Astuti, and F. M Basysyar, “Penerapan Algoritma K-Nearest Neighbor Dalam Klasifikasi Status Stunting Balita Di Desa Bojongemas,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 2, pp. 2479–2488, 2024, doi: 10.36040/jati.v8i2.8448.
F. Putra, H. F. Tahiyat, R. M. Ihsan, R. Rahmaddeni, and L. Efrizoni, “Penerapan Algoritma K-Nearest Neighbor Menggunakan Wrapper Sebagai Preprocessing untuk Penentuan Keterangan Berat Badan Manusia,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 1, pp. 273–281, 2024, doi: 10.57152/malcom.v4i1.1085.
A. J. T, D. Yanosma, and K. Anggriani, “Implementasi Metode K-Nearest Neighbor (Knn) Dan Simple Additive Weighting (Saw) Dalam Pengambilan Keputusan Seleksi Penerimaan Anggota Paskibraka,” Pseudocode, vol. 3, no. 2, pp. 98–112, 2018, doi: 10.33369/pseudocode.3.2.98-112.
H. A. Dwi Fasnuari, H. Yuana, and M. T. Chulkamdi, “Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi Penyakit Diabetes Melitus,” Antivirus J. Ilm. Tek. Inform., vol. 16, no. 2, pp. 133–142, 2022, doi: 10.35457/antivirus.v16i2.2445.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Prediksi Penjualan Produk Pepsodent Unilever dengan Algoritma K-Nearest Neighbor
Pages: 883-892
Copyright (c) 2024 Dzikra Maulida, Yusuf Ramadhan Nasution
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
