Fahirah; Lily Wulandari

The Implementation of Least Square Method on the Palm Shells Sales Forecasting Application

Full Text (PDF, 940KB), PP.1-13

Views: 0 Downloads: 0

Author(s)

1. Information System, Faculty of Computer Science and Information Technology, Gunadarma University, Depok 16424, Indonesia

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2020.05.01

Received: 19 Oct. 2019 / Revised: 23 Jan. 2020 / Accepted: 8 May 2020 / Published: 8 Oct. 2020

Index Terms

Sales targets, Palm Shells, Forecasting, Least Square, Application.

Abstract

Determination of sales targets made by palm shell export companies is often not appropriate and effect the amount of inventory of palm shells sold based on weight which will be reduced if stored too long. Implementation of Least Square method for forecasting the sale of palm shells on web platforms aims to help the company to determine sales targets more accurately. By using this application, companies can forecast for the sale of palm shells for the next month in one year starting from one month after the actual sales period that has been entered. Data testing using Mean Absolute Error (MAPE) shows the error generated is 5.935%, Black Box testing results reach 100%, and User Acceptance Testing shows users agree the application in accordance with the requirements and forecasting results is clearly displayed.

Cite This Paper

Fahirah, Lily Wulandari, "The Implementation of Least Square Method on the Palm Shells Sales Forecasting Application", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.12, No.5, pp. 1-13, 2020. DOI:10.5815/ijieeb.2020.05.01

Reference

[1]M. S. Brown, Data Mining for Dummies, Hoboken: John Wiley & Sons, Inc., 2014.
[2]R. J. Roiger, Data Mining: a Tutorial-Based Primer, 2nd ed., Boca Raton: CRC Press, Taylor & Francis Group, 2017.
[3]M. Ihsan, H. Rosyid, and E. Prasetyo, “Perancangan Aplikasi Peramalan Penjualan Motor Honda Menggunakan Metode Least Square (Studi Kasus: PT. HD Motor 99 Gresik)”, unpublished.
[4]L. Stepvhanie, "Peramalan Penjualan Produk Susu Bayi dengan Metode Grey System Theory dan Neural Network," University of Indonesia, Depok, 2012.
[5]M. F. Alamsyah and R. Arnie, “Model Aplikasi Prediksi Penjualan Sepeda Motor dengan Metode Single Moving Average”, JUSTISI, 4(2), 729-796, 2015.
[6]D. P. Pamungkas, “Implementasi Metode Least Square untuk Prediksi Penjualan Tahu Pong”. NERO Scientific Journal, 2(2), 75-81, 2016.
[7]F. R. Hariri, "Metode Least Square Untuk Prediksi Penjualan Sari Kedelai Rosi," Junal SIMETRIS, vol. 7, no. 2, pp. 731-736, 2016.
[8]N. L. A. K. Yuniastari and I. W. W. Wirawan, "Peramalan Permintaan Produk Perak Menggunakan Metode Simple Moving Average Dan Exponential Smoothing," Jurnal Sistem dan Informatika, vol. 9, no. 1, pp. 97-106, 2014.
[9]A. E. Indriyana, "Aplikasi Peramalan Penjualan Tiket Pesawat Menggunakan Metode Trend Moment (Studi Kasus: Maskapai Penerbangan XYZ)," Gunadarma University, Jakarta, 2017.
[10]U. T. Hantoro, "The Implementation of Machine Learning Method for Tweets Classification in Indonesian Language," Gunadarma University, Jakarta, 2017.
[11]"Tutorial - Building a Use Case Diagram," University of Cyprus, Department of Computer Science, 26 February 2010. [Online]. Available: https://www.cs.ucy.ac.cy/~epl233/labs/EPL233-Lab7.pdf. [Accessed 3 February 2019].
[12]G. D. Everett and R. McLeod, Software Testing − Testing Across the Entire Software Development Life Cycle, Hoboken: John Wiley & Sons, Inc., 2007.

International Journal of Information Engineering and Electronic Business (IJIEEB)