Planning Total Veener Production PT. XYZ


Perencanaan Jumlah Produksi (Veneer) PT. XYZ


  • (1) * Krisna Risky Putra Irawan            Universitas Muhammadiyah Sidoarjo  
            Indonesia

  • (2)  Tedjo Sukmono            Universitas Muhammadiyah Sidoarjo  
            Indonesia

    (*) Corresponding Author

Abstract

PT. XYZ is engaged in the manufacture and sale of wood veneers. Starting from the constant occurrence of over stock, now the company must make improvements to the production forecasting process so that over stock can be avoided. It can be seen that accurate production forecasting can create conditions for an effective and efficient production system. This study aims to obtain a more accurate forecast of material requirements using the Support Vector Regression (SVR) method, which is the result of the development of a Support Vector Machine (SVM) which has good performance in predicting time series data. Application of the Support Vector Regression (SVR) method with the RBF kernel in predicting the need for veneer production using the MATLAB application, it produces the smallest error rate with a MAPE of 5%, RMSE of 4364.63 and of 0.748274147. on  67 training data and 20 testing data.

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References

S. Wardah and I. Iskandar, “ANALISIS PERAMALAN PENJUALAN PRODUK KERIPIK PISANG KEMASAN BUNGKUS (Studi Kasus : Home Industry Arwana Food Tembilahan),” J@ti Undip J. Tek. Ind., vol. 11, no. 3, p. 135, 2017. DOI: https://doi.org/10.14710/jati.11.3.135-142

D. R. Indah and E. Rahmadani, “Sistem Forecasting Perencanaan Produksi dengan Metode Single Eksponensial Smoothing pada Keripik Singkong Srikandi Di Kota Langsa,” J. Penelit. Ekon. Akutansi, vol. 2, no. 1, pp. 10–18, 2018.

A. Adetayo et al., “Jurnal Penelitian Terapan Industri Metode Peramalan untuk Permintaan Penumpang Udara Domestik di Nigeria,” vol. 5, no. 2, pp. 146–155, 2018.

A. M. Al’afi, W. Widiart, D. Kurniasari, and M. Usman, “Peramalan Data Time Series Seasonal Menggunakan Metode Analisis Spektral,” J. Siger Mat., vol. 1, no. 1, pp. 10–15, 2020. DOI: https://doi.org/10.23960/jsm.v1i1.2484

E. M. Priliani, A. T. Putra, and M. A. Muslim, “Forecasting Inflation Rate Using Support Vector Regression (SVR) Based Weight Attribute Particle Swarm Optimization (WAPSO),” Sci. J. Informatics, vol. 5, no. 2, pp. 118–127, 2018. DOI: https://doi.org/10.15294/sji.v5i2.14613

B. H. Prakoso, “Pengaruh Preprocessing Data pada Metode SVR dalam Memprediksi Permintaan Obat,” J. Sist. Teknol. Inf. Indones., vol. 2, no. 2, pp. 92–99, 2017.

Mustakim, A. Buono, and I. Hermadi, “Support Vector Regression Untuk Prediksi Produktivitas Kelapa Sawit Di Provinsi Riau,” J. Sains, Teknol. dan Ind., vol. 12, no. 2, pp. 179–188, 2015.

L. Kunhui, L. Qiang, Z. Changle, and Y. Junfeng, “Time series prediction based on linear regression and SVR,” Proc. - Third Int. Conf. Nat. Comput. ICNC 2007, vol. 1, no. 2006, pp. 688–691, 2007.

N. D. Maulana, B. D. Setiawan, and C. Dewi, “Implementasi Metode Support Vector Regression ( SVR ) Dalam Peramalan Penjualan Roti ( Studi Kasus : Harum Bakery ),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 3, pp. 2986–2995, 2019.

J. Chen, H. Chen, Y. Huo, and W. Gao, “Application of SVR Models in Stock Index Forecast Based on Different Parameter Search Methods,” Open J. Stat., vol. 07, no. 02, pp. 194–202, 2017. DOI: https://doi.org/10.4236/ojs.2017.72015

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Published
2021-07-14
 
How to Cite
[1]
K. R. P. Irawan and T. Sukmono, “Planning Total Veener Production PT. XYZ”, PELS, vol. 1, no. 2, Jul. 2021.

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