The Use of Single Moving Average and Linear Regression in Spare Part Sales Forecasting at PT. CNC
Main
Eko Prasetiamaolana*
Mohammad Syafrullah
Given the highly competitive nature of Indonesia's automotive sector, accurate sales forecasting has become a crucial business strategy. This research investigates the application of Single Moving Average and Linear Regression methods for forecasting spare part sales at PT. CNC, an automotive spare parts manufacturer in Indonesia. The study analyzes monthly sales data from January 2019 to December 2022, employing both Single Moving Average and Linear Regression forecasting methods. Model performance was evaluated using multiple accuracy metrics including Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with data normalized using min-max normalization. The analysis yielded error metrics of MSE = 0.043, RMSE = 0.208, MAE = 0.005, and MAPE = 4.36%, demonstrating the effectiveness of these forecasting methods for spare part sales prediction.
Ayuni, G. N., & Fitrianah, D. (2019). Penerapan metode Regresi Linear untuk prediksi penjualan properti pada PT XYZ. Jurnal Telematika, 14(2), 79–86.
Azis, A. K., & Kustanto, K. (2023). Penerapan Moving Average Pada Prediksi Penjualan Accu. Jurnal Teknologi Informasi Dan Komunikasi (TIKomSiN), 11(1), 25–34.
Dewi, K. R., & Mauladi, K. F. (2020). Analisa Algoritma C4. 5 untuk Prediksi Penjualan Obat Pertanian di Toko Dewi Sri. Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 4(3), 109–114.
Dewi, S. P., Nurwati, N., & Rahayu, E. (2022). Penerapan Data Mining Untuk Prediksi Penjualan Produk Terlaris Menggunakan Metode K-Nearest Neighbor. Building of Informatics, Technology and Science (BITS), 3(4), 639–648.
Hasmawati, J. N., & Muchtar, M. (2017). Aplikasi prediksi penjualan barang menggunakan metode k-nearest neighbor (knn)(studi kasus tumaka mart). Semantik.
Indarwati, T., Irawati, T., & Rimawati, E. (2019). Penggunaan Metode Linear Regression Untuk Prediksi Penjualan Smartphone. Jurnal Teknologi Informasi Dan Komunikasi (TIKomSiN), 6(2).
Kamal, I. M., & Ilyas, R. (2017). Prediksi penjualan buku menggunakan data mining di pt. niaga swadaya. Semnasteknomedia Online, 5(1), 1–2.
Maricar, M. A. (2019). Analisa perbandingan nilai akurasi moving average dan exponential smoothing untuk sistem peramalan pendapatan pada perusahaan xyz. Jurnal Sistem Dan Informatika (JSI), 13(2), 36–45.
Rakasyiwi, G. R., Witjaksana, B., & Tjendani, H. T. (2022). Project Scheduling Analysis Using The Critical Path Method–Case Study: Subsidized House Construction Project In Hill Mulya Housing, Samarinda City. International Journal On Advanced Technology, Engineering, And Information System, 1(4), 73–88.
Setiawan, R. (2016). Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Untuk Menentukan Strategi Promosi Mahasiswa Baru (Studi Kasus: Politeknik Lp3i Jakarta). Jurnal Lentera Ict, 3(1), 76–92.
Yuliyanti, R., & Arliani, E. (2022). Peramalan jumlah penduduk menggunakan model arima. Jurnal Kajian Dan Terapan Matematika, 8(2), 114–128.
