Comparison of Multi Layer Perceptron and Holt Winter Accuracy in Forecasting Suzuki Car Brand Production in Indonesia

Muhammad Rizki Rachmadan

Abstract


Car production based on brand holding agents (APM) reports production value to the Indonesian Association of Automotive Industries (Gaikindo) in 2021 of 863,348 units with a percentage range of 11.4% by the Suzuki brand. Based on the public's interest in the need for a car which is the preferred option, the range and selling price are part of the considerations in determining the product of choice as the vehicle owner. This choice is important in activities to meet daily transportation needs. The purpose of this study is to obtain the most effective method and to maintain the number of vehicle unit production, production forecasts are needed according to people's purchasing power patterns using monthly data, especially the Suzuki brand. The research uses the Holt Winters measurement method with two hidden layers on the Multi Layer Perceptron (MLP) measurement method. The findings from this study are comparisons that can be said to be valid and can be an option in predicting data by utilizing methods to predict the amount of production by brand-holding agents. These results can contribute to optimizing the number of production results so that there are no excess or shortage of unit stock. The results showed that forecasting using a Multi Layer Perceptron with two hidden layers produced an accurate value where the lowest value at the Root Mean Square Error (RMSE) was 889.851 and the Mean Absolute Percentage Error (MAPE) was 9.3368.


Keywords


Car; Forecasting; Holt Winters; Multi Layer Perceptron

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References


Anhari, N., Arifin, Z., Maharani, S (2016). Sistem Pendukung Keputusan Pembelian Mobil Baru Dengan Menggunakan Metode Analytical Hierarchy Process. Academia.Edu, 1(1). https://fmipa.unmul.ac.id/files/docs/12.%20Jurnal%20Nanang%20Anhari%20(Ilkom).pdf

Atika, P. D., & Rasim. (2019). Implementasi Jaringan Syaraf Tiruan Metode Backpropagation untuk Prediksi Penjualan Mobil Bekas. Jurnal ICT : Information Communication & Technology, 18(2), 107–112. https://doi.org/10.36054/jict-ikmi.v18i2.70

Crone, S. F., & Kourentzes, N. (2010). Feature selection for time series prediction - A combined filter and wrapper approach for neural networks. Neurocomputing, 73(10–12), 1923–1936. https://doi.org/10.1016/j.neucom.2010.01.017

Dewayana, T. S., Sugiarto, D., & Hetharia, D. (2013). Model Pemilihan Industri Komponen Otomotif Yang Ramah Lingkungan. Jurnal Teknik Industri, 3(3), 208–216. https://doi.org/10.25105/jti.v3i3.1564

Fitryadi, K. & Sutikno (2017). Pengenalan Jenis Golongan Darah Menggunakan Jaringan Syaraf Tiruan Perceptron. Jurnal Masyarakat Informatika, 7(1), 1-10. https://doi.org/10.14710/jmasif.7.1.10794

Fowdur, T. P., Beeharry, Y., Hurbungs, V., Bassoo, V., Ramnarain-Seetohul, V., & Lun, E. C. M. (2018). Performance analysis and implementation of an adaptive real-time weather forecasting system. Internet of Things (Netherlands), 3–4, 12–33. https://doi.org/10.1016/j.iot.2018.09.002

Gaikindo. (2021). Indonesian Automobile Industry Data. Gaikindo.or.Id. https://files.gaikindo.or.id/my_files/?page=2

Karo, G. K., & Munardi, W. E. (2015). Usulan Peramalan Produksi Mobil BMW Dengan Jadwal Induk Dan Perencanaan Material Terhadap Divisi Loistic Produksi Plannning ( Studi Kasus : PT. Tjahja Sakti Motor, Jakarta Utara). Journal of Industrial Engineering & Management Systems, 8(1), 1–23. http://dx.doi.org/10.30813/jiems.v8i1.132

Nielsen, S. F. (2011). Introductory time series with R. Journal of Applied Statistics, 38(10), 2370–2371. https://doi.org/10.1080/02664763.2010.517940

Nurhamidah, N., Nusyirwan, N., & Faisol, A. (2020). Forecasting Seasonal Time Series Data Using the Holt-Winters Exponential Smoothing Method of Additive Models. Jurnal Matematika Integratif, 16(2), 151. https://doi.org/10.24198/jmi.v16.n2.29293.151-157

Nusraningrum, D., MEKAR, T. M., & PRASETYANINGTYAS, S. W. (2021). Persepsi Dan Sikap Terhadap Keputusan Pembelian Produk Pangan Fungsional Pada Generasi Milenial. Jurnal Bisnis dan Akuntansi, 23(1), 37–48. https://doi.org/10.34208/jba.v23i1.767

Oey, E., Wijaya, W. A., & Hansopaheluwakan, S. (2020). Forecasting and aggregate planning application – a case study of a small enterprise in Indonesia. International Journal of Process Management and Benchmarking, 10(1), 1–21. https://doi.org/10.1504/IJPMB.2020.104229

Rajagukguk, R. A., Ramadhan, R. A. A., & Lee, H. J. (2020). A review on deep learning models for forecasting time series data of solar irradiance and photovoltaic power. Energies, 13(24). https://doi.org/10.3390/en13246623

Skansi, S. (2018). Undergraduate Topics in Computer Science Introduction to Deep Learning Series editor. https://doi.org/10.1007/978-3-319-73004-2

Sugiarto, D., Hidayat, W., Ariatmanto, D., & Yaqin, A. (2021). Comparing Holt-Winter and Multi Layer Perceptron in Forecasting The Amount of Rice Supply. ICOIACT 2021 - 4th International Conference on Information and Communications Technology: The Role of AI in Health and Social Revolution in Turbulence Era, 252–255. https://doi.org/10.1109/ICOIACT53268.2021.9563977

Taufik, D. A., Setiawan, I., Wahid, M., Rochim, A., & Tosin, M. (2021). Integrasi Linear Regression dan Aggregate Planning untuk Perencanaan dan Pengendalian Produksi Leaf Spring Hino OW 190/200 di Industri Komponen Otomotif. Operations Excellence: Journal of Applied Industrial Engineering, 13(2), 245. https://doi.org/10.22441/oe.2021.v13.i2.023

Thomas, A. J., Petridis, M., Walters, S. D., Gheytassi, S. M., & Morgan, R. E. (2017). Two hidden layers are usually better than one. Communications in Computer and Information Science, 744, 279–290. https://doi.org/10.1007/978-3-319-65172-9_24

Zhang, G. P., & Qi, M. (2005). Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 160(2), 501–514. https://doi.org/10.1016/j.ejor.2003.08.037




DOI: http://dx.doi.org/10.22441/oe.2023.v15.i1.075

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