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All Journal Journal of Applied Data Sciences Blend Sains Jurnal Teknik
Lia Silviana
Universitas Sumatera Utara
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Enhanced Detection of Consumer Behavioral Shifts in E-Commerce Platforms with Transformer-Based Algorithms Syah, Rahmad B.Y; Elveny, Maricha; Darmansyah, Soleh; Silviana, Lia
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.907

Abstract

This research aims to analyze changes in consumer behavior on e-commerce platforms using consumer interaction data such as view, add to cart, and purchase.  Identifying changes in consumer behavior on e-commerce platforms is very important because it can provide deeper insight into consumer motivations and preferences. By better understanding how consumers interact with products, companies can design more targeted strategies to increase conversions, reduce cart abandonment, and improve the overall customer experience. The DistilBERT based prediction model is applied to detect and predict changing patterns of consumer behavior in the purchasing process. DistilBERT was chosen because of its more efficient capabilities compared to previous models which enable faster data processing and lower resource usage, which is very important for real-time applications on e-commerce platforms with big data. The data used includes consumer interactions during a certain period, with model evaluation using precision, recall, F1-score, and accuracy metrics. The results showed that despite an increase in the number of actions such as View and Add to Cart, conversion to Purchase was still hampered, indicating a cart abandonment problem. The model used managed to achieve 90% accuracy, with a precision value of 0.87, recall of 0.85, and F1-score of 0.86, showing excellent performance in predicting changes in consumer behavior. Based on the results of this analysis, companies can optimize marketing strategies by targeting consumers who have added products to their basket but have not yet made a purchase, as well as making price adjustments, discounts, and limited time offers. This research also emphasizes the importance of using real-time data to dynamically adjust marketing strategies and improve customer experience.
Pengelompokan Pernyataan Misogini pada Media Sosial Berbahasa Indonesia Menggunakan TF-IDF dan Algoritma K-Means Umaya Ramadhani Putri Nasution; Ikhwanuddin Nasution; Lia Silviana; Fanindia Purnamasari; Marischa Elveny; Anandhini Medianty Nababan; Stephani Uli Basa Silitonga
Blend Sains Jurnal Teknik Vol. 5 No. 1 (2026): Edisi Juli
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/blendsains.v5i1.1776

Abstract

Penelitian ini bertujuan untuk mengidentifikasi pola tersembunyi pada komentar media sosial berbahasa Indonesia yang berkaitan dengan misogini menggunakan pendekatan clustering. Dataset yang digunakan terdiri atas komentar yang dikumpulkan dari beberapa platform media sosial. Tahapan penelitian meliputi preprocessing teks, ekstraksi fitur menggunakan metode Term Frequency–Inverse Document Frequency (TF-IDF), serta pengelompokan data menggunakan algoritma K-Means. Penentuan jumlah klaster optimal dilakukan melalui evaluasi menggunakan Silhouette Score, Davies-Bouldin Index, dan Calinski-Harabasz Index, yang menghasilkan jumlah klaster optimal sebanyak lima klaster. Hasil analisis top terms menunjukkan bahwa setiap klaster memiliki karakteristik semantik yang berbeda, meliputi objektifikasi seksual eksplisit, diskursus peran gender, benevolent sexism, interaksi relasional, dan objektifikasi fisik yang dinormalisasi. Hasil penelitian menunjukkan bahwa komentar yang berkaitan dengan misogini tidak bersifat homogen, tetapi membentuk beberapa kelompok berdasarkan pola bahasa dan konteks semantik yang berbeda. Temuan ini menunjukkan bahwa kombinasi representasi fitur berbasis TF-IDF dan algoritma K-Means mampu mengungkap struktur semantik laten pada komentar media sosial yang tidak dapat direpresentasikan secara memadai melalui kategorisasi biner misogini dan non-misogini.
Co-Authors Anandhini Medianty Nababan Darmansyah, Soleh Elveny, Maricha Elveny, Marischa Fanindia Purnamasari Rahmad B.Y Syah Stephani Uli Basa Silitonga Umaya Ramadhani Putri Nasution