Forecasting Needs Of Mountain Types Of DDD Bike Using The Seasonal Autoregressive Integrated Moving Average Model Approach

Peramalan Kebutuhan Sepeda DDD Jenis Gunung Dengan Pendekatan Model Seasonal Autoregressive Integrated Moving Average

  • Didin Muhjidin Program Studi Teknik Industri, Fakultas Sains dan teknologi, Universitas Muhammadiyah Sidoarjo
  • Tedjo Sukmono Program Studi Teknik Industri, Fakultas Sains dan teknologi, Universitas Muhammadiyah Sidoarjo
Keywords: forecasting, SARIMA, Box Jenkins

Abstract

One of the bicycle manufacturers in Indonesia, namely PT. DDD is a manufacture engaged in the production of various types of bicycles with a make to stock production system. Market demand that fluctuates every year results in a lack of readiness to meet market needs. So a re-planning is needed in order to meet all market demands. The Box Jenkins statistical method, the Seasonal Autoregressive Integrated Moving Average model, is one of the appropriate approaches to solve problems at PT. DDD. The advantages of the SARIMA model can be used to forecast seasonal or non-seasonal time series simultaneously. The best SARIMA model approach to forecasting demand for mountain bikes at PT. DDD is SARIMA (0,0,0)(0,1,1)12 with the equation Zt=Zt-12+ΘQat-12+at with the smallest MAPE value of 32.35%. So that the model is said to be feasible to predict mountain bikes and the model can predict up to 12 periods in 2021.

References

[1] Kamiel, Berli Paripurna, Ghozi Adib Nugrahab, and Sunardi, “Perancangan dan Analisis Kekuatan Frame Sepeda Lipat Menggunakan Autodesk Inventor,” JMPM, vol. 2, no. 2, pp. 126–135, 2018.
[2] Durrah, Fara Inka, Yulia, Tessa Prihartina Parhusip, and Asep Rusyana, “Peramalan Jumlah Penumpang Pesawat Di Bandara Sultan Iskandar Muda Dengan Metode SARIMA (Seasonal Autoregressive Integrated Moving Average),” Journal of Data Analysis, vol. 1, no. 1, pp. 01–11, 2018.
[3] Lestari, Nofinda, and Nuri Wahyuningsih, “Peramalan Kunjungan Wisata dengan Pendekatan Model SARIMA (Studi kasus : Kusuma Agrowisata),” Jurnal Sains dan Seni ITS, vol. 1, no. 1, 2019.
[4] Wahyuni, Ni Putu Mirah Sri, I Wayan Sumarjaya, and I Gusti Ayu Made Srinadi, “Peramalan Curah Hujan Menggunakan Metode Analisis Spektral,” E-Jurnal Matematika, vol. 5, no. 4, pp. 183–193, 2016.
[5] Prabhadika, I Putu Yudi, Ni Ketut Tari Tastrawati, and Luh Putu Ida Harini, “Peramalan Persediaan Infus Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA) Pada Rumah Sakit Umum Pusat Sanglah,” E-Jurnal Matematika, vol. 7, no. 2, pp. 129–133, 2018.
[6] Kafara, Zaenab, F. Y. Rumlawang and L. J. Sinay, “Peramalan Curah Hujan Dengan Pendekatan Seasonal Autoregressive Integrated Moving Average (SARIMA),” Barekeng, vol. 1, no. 1, pp. 63–74, 2017.
[7] Sitorus, Verawaty Bettyani, Sri Wahyuningsih, and Memi Nor Hayati, “Peramalan dengan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) di Bidang Ekonomi,” Jurnal EKSPONENSIAL, vol. 8, no. 1, pp. 17–26, 2017.
[8] Rahmadayanti, Riza, Boko Susilo, and Diyah Puspitaningrum, “Perbandingan Keakuratan Metode Autoregressive Integrated Moving Average (ARIMA) dan Exponential Smoothing Pada Peramalan Penjualan Semen Di PT. Sinar Abadi,” Jurnal Rekursif, vol. 3, no. 1, pp. 23-36, 2015.
[9] Fahrudin, Rifqi, and Irfan Dwiguna Sumitra, “Sistem Peramalan Kebutuhan Hidup Layak Minimum (Kapita/Bulan) Kota Bandung,” Jurnal Sistem Informasi Bisnis, vol. 9, no. 2, pp. 192-203, 2019.
[10] Maricar, M. Azman, “Analisa Perbandingan Nilai Akurasi Moving Average dan Exponential Smoothing untuk Sistem Peramalan Pendapatan pada Perusahaan XYZ,” Jurnal Sistem dan Informatika, vol. 13, no. 2, pp. 36-45, 2019.
Published
2021-07-16