Evaluation of Use of Linear Regression to Predict Profit, Selling Price, and Stock on HSR Wheels Platform

Esa Fauzi; Bagus Alit Prasetyo; Adi Purnama; Rizky Bagus Pangestu

doi:10.59653/ijmars.v3i03.1967

Authors

Esa Fauzi Widyatama University, Indonesia
Bagus Alit Prasetyo Widyatama University, Indonesia
Adi Purnama Widyatama University, Indonesia
Rizky Bagus Pangestu Widyatama University, Indonesia

DOI:

https://doi.org/10.59653/ijmars.v3i03.1967

Keywords:

E-Commerce., linear regression, sales efficiency, sales prediction, data analysis techniques

Abstract

In the ever-evolving digital era, the e-commerce sector faces significant challenges in efficiently managing sales, selling prices, and inventory. This study aims to evaluate the effectiveness of a linear regression model in predicting sales, selling prices, and stock levels on the HSR Wheels e-commerce platform. A quantitative method was used by analyzing daily transaction data to identify the relationship between the time variable and sales, profit, and stock. The results showed that linear regression has limitations in modeling data complexity, with low R² scores and high Mean Absolute Error (MAE) values. These findings indicate the need for more advanced predictive models, such as machine learning algorithms, to improve prediction accuracy. This research is expected to contribute to developing more efficient and relevant sales strategies for e-commerce platforms.

Downloads

Download data is not yet available.

References

Vegetable Price Forecasting in E-Commerce Platform: A Review. Journal of Physics: Conference Series, 1878(1), 012071. https://doi.org/10.1088/1742-6596/1878/1/012071

Ketipov, R., Angelova, V., Doukovska, L., & Schnalle, R. (2023). Predicting User Behavior in e-Commerce Using Machine Learning. Cybernetics and Information Technologies, 23(3), 89–101. https://doi.org/10.2478/CAIT-2023-0026

Listyawati, I., & Kristiana, I. (2021). Pengaruh Return on Equity, Current Ratio, Size Company dan Debt to Equity Ratio Terhadap Nilai Perusahaan. MAKSIMUM: Media Akuntansi Universitas Muhammadiyah Semarang, 10(2), 47–57. https://doi.org/10.26714/MKI.10.2.2020.47-57

Mengenal Box-Plot (Box and Whisker Plots) – Accounting. (n.d.). Retrieved February 24, 2025, from https://accounting.binus.ac.id/2020/12/19/mengenal-box-plot-box-and-whisker-plots/

Narastu, R. S., Wiranata, M., Lubis, T. A., & Parhusip, J. (2025). Analisis Data Eksplorasi Dataset Gempa Bumi Indonesia. JOURNAL SAINS STUDENT RESEARCH, 3(1), 136–142. https://doi.org/10.61722/JSSR.V3I1.3266

Nurahman, N., & Ernawati, N. (2024). Analisis Algoritma C45 dan Regresi Linear untuk Memprediksi Hasil Panen Kelapa Sawit. Journal of Computer System and Informatics (JoSYC), 5(4), 1155–1163. https://doi.org/10.47065/JOSYC.V5I4.5828

Pan, C. L., Bai, X., Li, F., Zhang, D., Chen, H., & Lai, Q. (2021). How Business Intelligence Enables E-commerce: Breaking the Traditional E-commerce Mode and Driving the Transformation of Digital Economy. Proceedings - 2nd International Conference on E-Commerce and Internet Technology, ECIT 2021, 26–30. https://doi.org/10.1109/ECIT52743.2021.00013

Pola Belanja Konsumen serta Prediksi Stok Barang Berbasis Web Made Dwi Cahaya Putra, A. I., Made Arya Sasmita, G., Kadek Dwi Rusjayanthi, N., Raya Kampus Udayana, J., Kuta Sel, K., & Badung, K. (2023). Analisa Pola Belanja Konsumen serta Prediksi Stok Barang Berbasis Web. JEPIN (Jurnal Edukasi Dan Penelitian Informatika), 9(3), 415–427. https://doi.org/10.26418/JP.V9I3.67154

Rachmawati, D., Putriana, L., D4, ), Udara, M. T., Teknologi, S. T., & Abstrak, K. (2023). ANALISIS TREN ARUS KAS SEBELUM DAN SAAT PANDEMI PT ANGKASA PURA (PERSERO). Jurnal Manajemen Dirgantara, 16(1), 194–203. https://doi.org/10.56521/MANAJEMEN-DIRGANTARA.V16I1.810

Rohman, F., Ekonomi, F., Bisnis, D., Roy’an, M. F., Kunci, K., Produksi, B., Distribusi, B., & Produksi, H. (2021). Pengaruh Biaya Produksi dan Biaya Distribusi Terhadap Harga Jual Pada UKM Gendis Meubel. Jurnal Rekognisi Akuntansi , 5(2), 78–89. https://doi.org/10.34001/JRA.V5I2.182

Satya Saputra, P., Putu Gede Abdi Sudiatmika, I., Akuntansi, J., Negeri Bali, P., & Korespondensi, P. (2024). ANALISIS PREDIKSI HARGA SMARTPHONE TAHUN 2023 MENGGUNAKAN MODEL RANDOM FOREST REGRESSION BERDASARKAN FITUR-FITUR SPESIFIKASI TEKNIS ANALYSIS OF SMARTPHONE PRICE PREDICTION IN 2023 USING RANDOM FOREST REGRESSION MODEL BASED ON TECHNICAL SPECIFICATION FEATURES. Jurnal Komputer Dan Teknologi Sains (KOMTEKS), 3(2), 13–17. https://www.kaggle.com/datasets/howisusmanali/mo

Sihombing, P. R., Suryadiningrat, S., Sunarjo, D. A., & Yuda, Y. P. A. C. (2022). Identifikasi Data Outlier (Pencilan) dan Kenormalan Data Pada Data Univariat serta Alternatif Penyelesaiannya. Jurnal Ekonomi Dan Statistik Indonesia, 2(3), 307–316. https://doi.org/10.11594/JESI.02.03.07

Siroj, R. A., Afgani, W., Fatimah, F., Septaria, D., & Salsabila, G. Z. (2024). METODE PENELITIAN KUANTITATIF PENDEKATAN ILMIAH UNTUK ANALISIS DATA. Jurnal Review Pendidikan Dan Pengajaran (JRPP), 7(3), 11279–11289. https://doi.org/10.31004/JRPP.V7I3.32467

Sri Rahayu, Y., Saputra, Y., Irawan, D., Muhammadiyah Karanganyar, U., Teknologi Bisnis Riau Program Studi Teknik Komputer, I., Sains Dan Teknologi Jl Raya Solo-TawangmanguKm, F., Tasikmadu, K., & Karanganyar, K. (2024). IMPLEMENTASI METODE WATERFALL PADA PENGEMBANGAN SISTEM INFORMASI MOBILE E-DISARPUS. ZONAsi: Jurnal Sistem Informasi, 6(2), 523–534. https://doi.org/10.31849/ZN.V6I2.20538

Syahputra, H., Syahril, M., Studi Mahasiswa, P., Triguna Dharma, S., & Studi Dosen Pembimbing, P. (2020). Prediksi Jumlah Murid Baru Dengan Menggunakan Metode Regresi Linear Berganda. Jurnal Cyber Tech, 3(4), 671–679. https://doi.org/10.53513/JCT.V3I4.3215

Zaim Shahrel, M., Mutalib, S., & Abdul-Rahman, S. (2021). PriceCop – Price Monitor and Prediction Using Linear Regression and LSVM-ABC Methods for E-commerce Platform. International Journal of Information Engineering and Electronic Business, 13(1), 1–14. https://doi.org/10.5815/IJIEEB.2021.01.01