PENERAPAN BIG DATA ANALYTICS DALAM PREDIKSI TREN E-COMMERCE DI INDONESIA
DOI:
https://doi.org/10.54314/jssr.v8i4.4587Abstrak
Abstract: The growth of e-commerce in Indonesia has been accelerating, driven by increasing internet penetration and the widespread use of mobile devices. The large, complex, and diverse volume of transaction data requires appropriate analytical methods to produce accurate trend predictions. This study aims to apply Big Data Analytics in analyzing consumer shopping patterns, popular product trends, and factors influencing purchasing decisions. Data were collected from various e-commerce platforms, processed using Hadoop and Spark, and further analyzed through predictive modeling with Machine Learning algorithms. The results indicate that integrating Big Data Analytics can improve trend prediction accuracy by up to 85% compared to conventional methods. These findings are expected to support strategic decision-making in Indonesia’s e-commerce sector.
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Keywords: Big Data Analytics, E-commerce, Machine Learning, Trend Prediction, Indonesia
Abstrak: Pertumbuhan e-commerce di Indonesia semakin pesat, didorong oleh penetrasi internet dan meningkatnya penggunaan perangkat mobile. Data transaksi yang besar, kompleks, dan beragam membutuhkan metode analisis yang tepat untuk menghasilkan prediksi tren yang akurat. Penelitian ini bertujuan untuk menerapkan Big Data Analytics dalam menganalisis pola belanja konsumen, tren produk populer, serta faktor yang memengaruhi keputusan pembelian. Metode yang digunakan mencakup pengumpulan data dari berbagai platform e-commerce, pemrosesan menggunakan Hadoop dan Spark, serta analisis prediktif dengan algoritma Machine Learning. Hasil penelitian menunjukkan bahwa integrasi Big Data Analytics mampu meningkatkan akurasi prediksi tren hingga 85% dibanding metode konvensional, sehingga dapat mendukung strategi bisnis e-commerce di Indonesia.
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Kata kunci: Big Data Analytics, E-commerce, Machine Learning, Prediksi Tren, Indonesia
Unduhan
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