Implementasi Algoritma K-Nearest Neighbour dalam Memprediksi Stok Sepeda Motor
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Desyanti Desyanti
*
Sekolah Tinggi Teknologi Dumai, Dumai,
Indonesia
- Denok Wulandari Amik Tridharma Pekan Baru, Pekan Baru, Indonesia (*) Corresponding Author
Abstract
PT. Dasatama Cemerlang Motor is a company engaged in the automotive sector. With the increasingly fierce competition among the automotive industry, companies are required to be able to handle inter-industry competition. Sales system PT. Dasatama Cemerlang Motor uses a cash or credit system. For every motorcycle sale, the admin inputs sales data using Ms.Excel. Even though Ms.Excel has many features and functions that are used to process numbers, it cannot predict annual motorcycle sales for the future as a reference in marketing strategy. Because of that, forecasting is needed which will help the company to find out the trend in the number of motorcycle sales for the coming year. The KNN algorithm is one of the methods used for classification analysis, but in the last few decades the KNN method has also been used for prediction. KNN looks for the shortest distance between the data to be evaluated and its K closest neighbors. The results achieved in this study resulted in the number of motorcycles for each brand that will be sold in 2022 obtained from the addition of 5 motorcycles for each sale of each motorcycle brand. Based on the research results, the prediction accuracy rate using the KNN method is 97%.
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References
A. Pratama, P. S. Akuntansi, and U. Pamulang, “Perkembangan teknologi informasi dan komunikasi dalam upaya menanggulangi plagiarisme dikalangan mahasiswa,” vol. 3, no. 2, 2018.
R. Febrina, “Persaingan Usaha pada Era Digital Menurut Persepektif Hukum Persaingan Usaha,” J. karya Ilm. multidisiplin, vol. 2, no. 1, pp. 121–127, 2022.
F. S. W. Abdul Gani Muttaqin, Kalina aulia Sari, “Penerapan metode K-Nearest Neighbor untuk Prediksi Penjualan Berbasis Web Pada PT.Wika Industry Energy.” pp. 1–6, 2020.
M. O. Odja, F. J. Likadja, W. T. Ina, and S. I. Pella, “Penggunaan Microsoft Excel untuk Kemudahan Pengolahan Data Nilai Hasil Belajar Siswa,” vol. XV, no. 2, pp. 22–29, 2021.
D. R. Indah, “Sistem Forecasting Perencanaan Produksi dengan Metode Single Eksponensial Smoothing pada Keripik Singkong Srikandi Di Kota Langsa,” J. Penelit. Ekon. Akunt., vol. 2, no. 1, pp. 10–18, 2018.
F. S. Desyanti, “SATIN – Sains dan Teknologi Informasi Penerapan Algoritma Apriori Terhadap Perawatan Tubuh di Kakiku,” vol. 5, no. 1, 2019.
S. Dewi, “PADA PREDIKSI KEBERHASILAN PEMASARAN PRODUK LAYANAN PERBANKAN,” J. Techno Nusa Mandiri, vol. XIII, no. 1, pp. 60–66, 2016.
R. L. Hasanah, M. Hasan, and W. E. Pangesti, “KLASIFIKASI PENERIMA DANA BANTUAN DESA MENGGUNAKAN METODE KNN ( K-NEAREST NEIGHBOR ),” vol. 16, no. 1, pp. 1–6, 2019.
M. R. Alghifari and A. P. Wibowo, “Penerapan Metode K-Nearest Neighbor Untuk Klasifikasi Kinerja Satpam Berbasis Web,” vol. 5, no. 1.
S. S. AgustinaDheaTiaratuti, “PERAMALAN PENJUALAN MOBIL PADA PT BENGAWAN,” vol. Vol 2, no. No 1, pp. 101–109, 2019.
A. W. Faris Asrory, Joseph Dedy Irawan, “APLIKASI PREDIKSI KELULUSAN MAHASISWA TEPAT WAKTU MENGGUNAKAN METODE KNN,” JATI (Jurnal Mhs. Tek. Inform., vol. 4, no. 2, 2020.
M. R. Setiawan, Y. A. Sari, and P. P. Adikara, “Klasifikasi Citra Makanan Menggunakan K-Nearest Neighbor dengan Fitur Bentuk Simple Morphological Shape Descriptors dan Fitur Warna Grayscale Histogram,” vol. 3, no. 3, pp. 2726–2731, 2019.
Y. Saputra, Arie Yandi, Primadasa, “Penerapan Teknik Klasifikasi Untuk Prediksi Kelulusan Mahasiswa Menggunakan Algoritma K-Nearest Neighbour,” Techno.COM, vol. 17, no. 4, pp. 395–403, 2018.
H. Saleh and R. I. Faisal, Muh.Musa, “KLASIFIKASI STATUS GIZI BALITA MENGGUNAKAN METODE K-NEAREST NEIGHBOR,” vol. 4, no. 2, pp. 120–126, 2019.
N. Luh and G. Pivin, Penerapan Metode K-Nearest Neighbor Untuk Sistem Rekomendasi Pemilihan Mobil, vol. 16, no. 2. 2017.
F. T. Admojo, “Klasifikasi Aroma Alkohol Menggunakan Metode KNN,” vol. 1, no. 2, pp. 34–38, 2020.
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