DOI: https://doi.org/10.21456/vol9iss2pp244-251

Aplikasi Auto Sales Forecasting Berbasis Computational Intelligence Website untuk Mengoptimalisasi Manajemen Strategi Pemasaran Produk

*Rizal Bakri orcid scopus  -  Akuntansi, STIEM Bongaya, Indonesia
Umar Data  -  Manajemen, STIEM Bongaya, Indonesia
Niken Probondani Astuti  -  Manajemen, STIEM Bongaya
Open Access Copyright (c) 2019 JSINBIS (Jurnal Sistem Informasi Bisnis)

Citation Format:
Abstract

Business analytics plays an important role in optimizing the management of product marketing strategies. One of the most popular analytical tools in business analytics is sales forecasting. Businesses need to conduct sales forecasting to optimize marketing management in the form of product availability predictions, predictions of capital adequacy, consumer interest, and product price governance. However, the problem that is often encountered in forecasting is the number of forecasting methods available so that it makes it difficult for business people to choose the best forecasting method. The aims of this research is to develop a forecasting software tha can be accessed online based on computational intelligence, which is a software that can make forececasting with various methods and then intelligently choose the best forecasting method. The software development method used in this study is the SDLC with waterfall model. The result of this research is the Auto sales forecasting software was developed using the R programming language by combining various package and can be accessed online through the page Http://bakrizal.com/AutoSalesForecasting. This software can be used to conduct forecast analysis with various methods such as Simple Moving Average, Robust Exponential Smoothing, Auto ARIMA, Artificial Neural Network, Holt-Winters, and Hybrid Forecast. This software contains intelligence computing to choose the best forecasting method based on the smallest RMSE value. After testing the sales transaction data at the Futry Bakery & Cake Shop in Makassar, the results show that the Robust Exponantial Smoothing method is the best forecasting method with an RMSE value of 0.829

 

 

Note: This article has supplementary file(s).

Fulltext View|Download |  Research Instrument
COPYRIGHT TRANSFER AGREEMENT
Subject
Type Research Instrument
  Download (1MB)    Indexing metadata
Keywords: Busines Analytics; Computational Intelligence; Marketing Management; Sales Forecasting; R Programming

Article Metrics:

Article Info
Section: Research Articles
Language : EN
Recent articles
Perancangan Sistem Informasi Konsultasi Akademik Berbasis Website Analisis Kualitas Layanan E-Government dengan Pendekatan E-GovQual Modifikasi Deteksi Objek Terapung pada Sungai Martapura dengan Metode Haar Like Feature Menggunakan Kamera Smart Phone More recent articles
Most cited articles
Penerapan Green Supply Chain Management Untuk Peningkatan Kinerja Keuangan Perusahaan Penerapan Metode AHP dan Fuzzy Topsis Untuk Sistem Pendukung Keputusan Promosi Jabatan Perencanaan Infrastruktur Teknologi Informasi pada Bank dengan Framework TOGAF Perancangan Model Data Flow Diagram Untuk Mengukur Kualitas Website Menggunakan Webqual 4.0 Sistem Deteksi Retinopati Diabetik Menggunakan Support Vector Machine More cited articles
  1. Assauri, S., 2013. Manajemen Pemasaran. Raja Grafindo : Depok
  2. Cavalcante, R.C., Brasileiro, R.C., Souza, V.L.F., Nobrega, J.P., Oliveira, A.L.I., 2016. Computational Intelligence and Financial Market : A Survey and Future Directions. Expert System with Application 55 (1), 194-211
  3. Cipra, T., 1992. Robust exponential smoothing. Journal of Forecasting, 11(1), 57 - 69
  4. Crevits, R., Bergmeir, C., Hyndman, R., 2018. robets: Forecasting Time Series with Robust Exponential Smoothing. R package version 1.4. https://CRAN.R-project.org/package=robets
  5. Crevits, R., Croux, C., 2016. Forecasting with Robust Exponential Smoothing with Damped Trend and Seasonal Components. SSRN : KBI_1741, 1 - 23
  6. Gelper, S., Fried, R., Croux, C., 2010. Robust forecasting with exponential and Holt–Winters smoothing. Journal of forecasting, 29(3), 285-300
  7. Ghozali, L., Oktavian, K., Natasha, T., Sari, N., Atmadja, C.J., 2019. Analisis Peramalan (Forecasting) Perencanaan Produksi Office Furniture Untuk Meningkatkan Strategi dalam Sistem Penjualan Produk E-Class : Seminar Nasional Teknologi Komputer dan Telekomunikasi (SNTKT IX) 25-26 April, 232-241
  8. Hyndman R, Athanasopoulos G, Bergmeir C, Caceres G, Chhay L, O'Hara-Wild M, Petropoulos F, Razbash S, Wang E, Yasmeen F (2019). forecast: Forecasting functions for time series and linear models. R package version 8.9, URL: http://pkg.robjhyndman.com/forecast
  9. Lammers, B., 2019. ANN2: Artificial Neural Networks for Anomaly Detection. R package version 2.3.2. https://CRAN.R-project.org/package=ANN2
  10. Montgomery, D.C., Jennings, C.L., Kulahci, M., 2008. Introduction to Time Series Analysis and Forecasting. John Wiley & Sons, Inc : Hoboken, New Jersey
  11. R Core Team, 2017. R : A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Austria. URL https://www.r-project.org/
  12. Riatningsih, 2017. Forecasting Penjualan Rumah Dengan Menggunakan Metode Trend Moment Pada PT. Rumakita Prima Karsa. Jurnal Perspektif 15 (1), 40-48
  13. Ribeiro, B.B, Chang, W., 2018. Shinydashboard: Create Dashboard with ‘Shiny’. R version package 0.7.0. URL https://CRAN.R-project.org/package=shinydashboard
  14. Rizkiyani, M., 2014. Penerapan Forecasting Methods untuk Meningkatkan Strategi dalam Sistem Penjualan Ponsel pada Sarang Cell Semarang. Semarang: Seminar Nasional Sistem Informasi Komputer, 1-12
  15. Shaub, D., Ellis., P., 2019. forecastHybrid: Convenient Functions for Ensemble Time Series Forecasts. R package version 4.2.17. https://CRAN.R-project.org/package=forecastHybrid
  16. Shumway, R.H., Stoffer, D.S., 2011. Time Series Analysis and Its Applications with R Examples 3nd. Springer : New York USA
  17. Svetunkov, I., 2018. Smooth : Forecasting using State Space Models. R Package version 2.4.7. URL https://cran.r-project.org/package=smooth
  18. Swastha, Basu, Irawan, 2008. Manajemen Pemasaran Modern. Yogyakarta: Liberty
  19. Wardah, S., Iskandar, 2016. Analisis Peramalan Penjualan Produk Keripik Pisang Kemasan Bungkus : Jurnal Teknik Industri, 11 (3), 135-142
  20. Zhang, G., Patuwo, B.E., Hu, M.Y., 1998. Forecasting with artificial neural networks:: The state of the art. International journal of forecasting, 14(1), 35-62

Last update:

  1. Implementasi aplikasi pemasaran digital olahan beku hampir kedaluarsa pada pedagang Pasar Ciroyom Bandung

    S Supriyati, Ramadhan Syaeful Bahri, Asti Khoerunisa, Tyas Sylva Fitrian Natansya, Rika Sovyatun Nissa, Kevin Fernaldy. KACANEGARA Jurnal Pengabdian pada Masyarakat, 7 (1), 2024. doi: 10.28989/kacanegara.v7i1.1886

Last update: 2024-04-18 11:19:30

  1. SWANSTAT: A user-friendly web application for data analysis using shinydashboard package in R

    Bakri R.. Telkomnika (Telecommunication Computing Electronics and Control), 18 (4), 2020. doi: 10.12928/TELKOMNIKA.v18i4.14182