Utilizing Linear Regression to Forecast Sales in RD.bag's Online Outlet

##plugins.themes.academic_pro.article.sidebar##

Published Apr 3, 2024
Download
PDF
Statistic

Downloads

Download data is not yet available.
Vol. 16 No. 01 (2024): Vol. 16 No. 01 January 2024

##plugins.themes.academic_pro.article.main##

Rudi Purnomo
Universitas Muria Kudus
Tutik Khotimah
Universitas Muria Kudus
Ahmad Jazuli
Universitas Muria Kudus

Abstract

This research aims to analyze the relationship between various variables and the sales performance of the RD.bag online store using sales data from the period of February 1, 2021, to January 31, 2023. The data collection phase involves retrieving data from Shopee and storing it in an Excel file format. The data is then cleansed of duplicates and irrelevant information in the data cleaning stage, followed by data transformation to tidy up datetime formats and create a dataframe. The analysis is carried out using the linear regression method to observe the relationship between independent and dependent variables. These analysis findings provide insights into the sales performance of the RD.bag online store and can aid in developing more effective marketing strategies. The study also presents suggestions for future research development, including adding variables, exploring advanced analysis methods, and conducting comparative analyses with other online stores in the same industry.

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to Cite
Rudi Purnomo, Tutik Khotimah, & Ahmad Jazuli. (2024). Utilizing Linear Regression to Forecast Sales in RD.bag’s Online Outlet. SAINTEKBU, 16(01), 62–71. Retrieved from https://ejournal.unwaha.ac.id/index.php/saintek/article/view/3908

References

  1. Ayuningsih, A.P., Setiawan, N.Y. dan Wijoyo, S.H. (2022) “Analisis Prediksi Penjualan Obat Hewan menggunakan Metode Regresi Linier melalui Visualisasi Dashboard (Studi Kasus PT. Satwa Jawa Jaya),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 6(4), hal. 1568–1575. Tersedia pada: http://j-ptiik.ub.ac.id.
  2. Islam, S.F.N., Sholahuddin, A. dan Abdullah, A.S. (2021) “Extreme gradient boosting (XGBoost) method in making forecasting application and analysis of USD exchange rates against rupiah,” Journal of Physics: Conference Series, 1722(1). Tersedia pada: https://doi.org/10.1088/1742-6596/1722/1/012016.
  3. Kennard Taruna, G. dan Budi, S. (2022) “Penerapan Data Science pada Dataset Olympics,” Strategi, 4(November), hal. 2443–2229.
  4. Kothandaraman, D. et al. (2022) “Intelligent Forecasting of Air Quality and Pollution Prediction Using Machine Learning,” Adsorption Science and Technology. Diedit oleh L. R, 2022, hal. 1–15. Tersedia pada: https://doi.org/10.1155/2022/5086622.
  5. Kurniawan, A.F., Pane, S.F. dan Awangga, R.M. (2021) “Prediksi Jumlah Penjualan Rumah di Bojongsoang ditengah Pandemi Covid-19 dengan Metode ARIMA,” Jurnal Media Informatika Budidarma, 5(4), hal. 1479. Tersedia pada: https://doi.org/10.30865/mib.v5i4.3121.
  6. Lumunon, R.R., Sendow, G.M. dan Uhing, Y. (2019) “Pengaruh Work Life Balance, Kesehatan Kerja Dan Beban Kerja Terhadap Kepuasan Kerja Karyawan Pt. Tirta Investama (Danone) Aqua Airmadidi the Influence of Work Life Balance, Occupational Health and Workload on Employee Job Satisfaction Pt. Tirta Investama,” Jurnal EMBA, 7(4), hal. 4671–4680. Tersedia pada: https://ejournal.unsrat.ac.id/index.php/emba/article/view/25410.
  7. Maaloul, K. dan Brahim, L. (2022) “Comparative Analysis of Machine Learning for Predicting Air Quality in Smart Cities,” Wseas Transactions on Computers, 21, hal. 248–256. Tersedia pada: https://doi.org/10.37394/23205.2022.21.30.
  8. Ochita Ratna Sari dan Trisni Handayani (2022) “Hubungan Pola Asuh Orang Tua Terhadap Pembentukan Karakter Religius Siswa Sekolah Dasar Islam Terpadu,” Jurnal Cakrawala Pendas, 8(4), hal. 1011–1019. Tersedia pada: https://doi.org/10.31949/jcp.v8i4.2768.
  9. Retnowati, P. dan Khotimah, T. (2020) “Aplikasi Forecasting Kehadiran Siswa Di Smp 2 Jekulo,” Jurnal SIMETRIS, 11(2). Tersedia pada: https://jurnal.umk.ac.id.
  10. Ruamiana, W.B., Nangi, J. dan Tajidun, L.M. (2018) “Aplikasi Forecasting Jumlah Frekuensi Penumpang Pesawat Terbang Lion Air Pada Bandar Udara Halu Oleo Dengan Menggunakan Metode Least Square,” semanTIK, 4(1), hal. 151–160. Tersedia pada: http://ojs.uho.ac.id/index.php/semantik/article/view/4468.
  11. Vinceti, A. et al. (2023) “An interactive web application for processing, correcting, and visualizing genome-wide pooled CRISPR-Cas9 screens,” Cell Reports Methods, 3(1), hal. 100373. Tersedia pada: https://doi.org/10.1016/j.crmeth.2022.100373.
  12. Vinet, L. dan Zhedanov, A. (2011) “A ‘missing’ family of classical orthogonal polynomials,” Journal of Physics A: Mathematical and Theoretical, 44(8), hal. 1–13. Tersedia pada: https://doi.org/10.1088/1751-8113/44/8/085201.