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tukino, tukino
Universitas Putera Batam
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Journal : Bulletin of Computer Science Research

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Prediksi Penjualan Barang Menggunakan Metode K-Means dan Regresi Linear Henry Adam; Tukino; Elfina Novalia; Hananto, April Lia
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.541

Abstract

Sales data analysis plays an important role in supporting business decision making, especially to optimise stock management and improve operational efficiency. the main problem faced by Vapestore XYZ in Karawang is the difficulty in accurately predicting the number of product sales, so there is often an imbalance between inventory and market demand. This can cause losses due to overstocks or shortages of goods. Currently, the estimation of stock requirements still relies on intuition and personal experience, without the support of objective data analysis. This research aims to build a sales prediction model by combining the K-Means method for product clustering and Linear Regression for sales quantity prediction. Sales data is taken directly from the store POS application, then goes through the stages of cleaning, labelling, and clustering into three groups, namely ‘Less Sold’, “Sold”, and ‘Very Sold’. Sales prediction is performed using Linear Regression by utilising the clustering results and time variables as inputs. Model performance evaluation is performed using error metrics, namely Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Based on the test results, the developed Linear Regression model obtained MAE of 3.20, MSE of 52.34, and RMSE of 7.23. These error values indicate that the model is able to provide sales estimates that are close enough to the actual data to be reliable in stock planning. Visualisation of the prediction results in the form of tables and heatmaps makes it easy to identify sales trends and compare performance between products. The findings of this study prove that the combination of K-Means and Linear Regression methods is effectively used to support stock decision making and marketing strategies in vape retail stores. Further development is recommended by enriching the dataset and exploring other prediction methods to improve model performance.
Co-Authors Abdul Latif alfannisa annurrallah fajrin Alfannisa Annurrallah Fajrin Alfannisa Annurrullah Fajrin Alfannisa Annurullah Fajrin Alfannisa Fajrin Algifanri Maulana, Algifanri Algifari Maulana Ali Abrar Alvina Leony Amrizal . Amrizal Amrizal Amrizal Amrizal Anggia Arista Anggia Dasa Putri April Lia Hananto Argo Putra Prima Arif Rahman Hakim Arif Rahman Hakim Arnomo, Sasa Ani Arsyad, Fachry Baenil Huda Bahariandi Aji Prasetyo Baru Harahap Bayu Priatna Bayu Yoga Astario Citra Indah Asmarawati Dede Kuswanda Dian Efriyenti Dian Efriyenti Difa Prakoso Fuadi, Muhammad Djumhadi Djumhadi Elisa, Erlin Elsya Tarigan Paskaria Loyda Tarigan Elva Susanti Erlin Elisa Fitria Nurapriani Hananto, Agustia Handoko, Koko Harman, Rika Hasanah, Haprilianh Hayati, Cucu Henry Adam Hibatullah, Muhammad Hafizh Hilabi, Shofa Shofiah Huban Kabir Huda, Baenil Ihsan, Mohammad Maftuh Ilham Fariz Asya Mubarok Imam Zaenuddin Jabar Sanjaya Juniardi Lestari, Renita M Irhash Erlangga Meiti Subardhini Melisa Mildawati, Milly Muhammad Khaerudin Muhammad Taufik Syastra NANDA HARRY MARDIKA Nofriani Fajrah, Nofriani Novalia, Elfina Nurafriani, Fitria Nurapriani, Fitria Olvia Nursaadah P, Shafira Putri Kusuma Priyatna, Bayu Purnomo, Andromedo Cahyo Ratna Juwita, Ayu Realize, Realize Rizki Prakasa Hasibuan Rohana, Tatang Rohman Nurafan Putra Pratama Ronald Wangdra Roza Yenita Ruliansyah Ruliansyah, Ruliansyah Sabrina Amanda Salsabila Saepul Aripiyanto Sama, Hendi Sandi Ahmad Shidiq, Faisal Shofa Shofia Hilabi Shofia Hilabi, Shofa Shofiah Hilabi, Shofa Silvana Nazuah Soli Vernika Manalu Sri Watini Steven Famy Subarkah, Ade Surala, Lyvia Suvianto Wangdra Syaeful Akbar Syahril Effendi Tony Wibowo Versanudin Hekmatyar Wibisono, Eko Gunawan Widyanti, Tyas Winarni Winda Yohanna Siahaan Young, Filbert Yovika Aprianti Yvonne Wangdra Zulkifli, Mohamad