PREDIKSI PENJUALAN PAKAIAN SEKOLAH DI SHOPEE MENGGUNAKAN ALGORITMA REGRESI LINEAR DAN REGRESI LOGISTIC

PRINGADI, FARHAN ACHMAD (2025) PREDIKSI PENJUALAN PAKAIAN SEKOLAH DI SHOPEE MENGGUNAKAN ALGORITMA REGRESI LINEAR DAN REGRESI LOGISTIC. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

This research aims to develop a prediction model that can help sellers understand the factors that affect product sales in Shopper. Linear regression algorithms are used to predict sales based on variables such as price and promotion period, while logistic regression is used to understand consumer behavior and factors that influence purchasing decisions. The results of this study have the potential to provide guidance for designing more effective marketing strategies on the Shopee e-commerce platform. The method used in this research is a machine learning method to predict product sales on the Shopee platform. This research aims to help identify sales of school clothes in e-commerce such as Shopee by developing machine learning-based algorithms, such as Linear Regression and Logistic Regression. The machine learning methods are applied to improve the accuracy of sales prediction. The research stages include analyzing sales data, determining objectives, collecting relevant data, analyzing data, and drawing results. The Linear Regression algorithm test showed accuracy with MAE value of 0.05, MSE of 0.05, and RMSE of 0.22. Meanwhile, the Logistic Regression algorithm recorded a prediction accuracy of 93.47%. This study concludes that the Linear Regression and Logistic Regression algorithms have good performance in predictin g sales. It is hoped that the results of this study can help select the most suitable algorithm for sales data processing and provide insights for sellers in determining prediction strategies and understanding the level of sales of school clothes on the Shopee platform. Keywords: E-commerce, Sales, Shopee, Linear Regression, Logistic Regression. Penelitian ini bertujuan untuk mengembangkan model prediksi yang dapat membantu penjual memahami faktor-faktor yang mempengaruhi penjualan produk pada Shopper. Algoritma regresi linear digunakan untuk memprediksi penjualan berdasarkan variabel seperti harga dan periode promosi, sedangkan regresi logistic digunakan untuk memahami perilaku konsumen dan faktor-faktor yang mempengaruhi keputusan pembelian. Hasil penelitian ini berpotensi memberikan panduan untuk merancang strategi pemasaran yang lebih efektif di platform ecommerce Shopee. Metode yang digunakan dalam penelitian ini yaitu metode pembelajaran mesin untuk memprediksi penjualan produk di platform Shopee. Penelitian ini bertujuan untuk membantu mengidentifikasi penjualan pakaian sekolah di e-commerce seperti Shopee dengan mengembangkan algoritma berbasis pembelajaran mesin, seperti Regresi Linear dan Regresi Logistik. Metode pembelajaran mesin tersebut diterapkan untuk meningkatkan akurasi prediksi penjualan. Tahapan penelitian meliputi analisis data penjualan, penentuan tujuan, pengumpulan data yang relevan, analisis data, dan penarikan hasil. Pengujian algoritma Regresi Linear menunjukkan akurasi dengan nilai MAE sebesar 0,05, MSE sebesar 0,05, dan RMSE sebesar 0,22. Sementara itu, algoritma Regresi Logistik mencatat akurasi prediksi sebesar 93,47%. Penelitian ini menyimpulkan bahwa algoritma Regresi Linear dan Regresi Logistik memiliki performa yang baik dalam memprediksi penjualan. Diharapkan hasil penelitian ini dapat membantu memilih algoritma yang paling sesuai untuk pengolahan data penjualan serta menyediakan wawasan bagi penjual dalam menentukan strategi prediksi dan memahami tingkat penjualan pakaian sekolah di platform Shopee. Kata Kunci : E-commerce, Penjualan, Shopee, Regresi Linear, Regresi Logistik

Item Type: Thesis (S1)
Call Number CD: FIK/INFO. 25 061
NIM/NIDN Creators: 41519010151
Uncontrolled Keywords: E-commerce, Penjualan, Shopee, Regresi Linear, Regresi Logistik
Subjects: 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 003 Systems/Sistem-sistem > 003.7 Kinds of Systems/Macam-macam Sistem > 003.74 Linear Systems/Sistem Linear
500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
600 Technology/Teknologi > 640 Home Economic and Family Living Management/Kesejahteraan Rumah Tangga dan Manajemen Kehidupan Keluarga > 646 Sewing Materials and Equipment, Clothing, Management of Personal and Family Life/Mesin Jahit dan Perlengkapan Menjahit, Pakaian, Management Pribadi dan Keluarga
Divisions: Fakultas Ilmu Komputer > Informatika
Depositing User: khalimah
Date Deposited: 14 Mar 2025 05:08
Last Modified: 14 Mar 2025 05:08
URI: http://repository.mercubuana.ac.id/id/eprint/94892

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