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All Journal Brilliance: Research of Artificial Intelligence SmartComp
Muflih, M
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mathematics Other

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Analisis Kepuasan Pengguna (User-Centered) Sistem E-Commerce Terhadap Keputusan Pembelian Online Pada Mahasiswa FTI UNISKA Banjarmasin Alamsyah, Nur; Muflih, M; Rosadi, Muhammad Edya; Ariani, Alma; Agustina, Rika
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 13, No 1 (2024): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v13i1.5933

Abstract

Penelitian ini bertujuan untuk menganalisis kepuasan pengguna (user-centered) sistem e-commerce dengan studi kasus pada situs belanja online Shoope, Tokopedia dan Lazada terhadap keputusan pembelian pada mahasiswa FTI UNISKA Banjarmasin. Metode yang digunakan adalah metode survei dengan kuesioner yang dibagikan secara online kepada 127 responden yang terdiri dari mahasiswa FTI UNISKA Banjarmasin. Data yang diperoleh diolah dan dianalisis menggunakan analisis statistik deskriptif dan analisis regresi. Penelitian ini menggunakan jenis Business-to-Consumer (B2C) dan Consumer-to-Consumer (C2C) di mana produk atau jasa dijual oleh perusahaan kepada konsumen akhir dan konsumen bisa juga menjual kembali kepada konsumen lainnya. Contoh: Tokopedia, Bukalapak, Shopee. Setelah melakukan analisis dan pengujian, dapat disimpulkan dari 13 komponen identifikasi terhadap keputusan pembelian secara online, aplikasi Shopee yang lebih Puas atau dibeli oleh responden  sebesar 80 % dan hasil ini dapat dijadikan rujukan oleh pengembang aplikasi marketplace yang ada di Indonesia.
Emotion Detection and Sentiment Analysis of Women’s E-Commerce Clothing Reviews Using DistilBERT Transformer Muflih, M; Karyadiputra, Erfan
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7411

Abstract

Customer reviews on e-commerce platforms have become an essential source of information for understanding user perceptions, satisfaction levels, and product quality. However, most existing studies still focus on sentiment polarity classifying opinions only as positive or negative without examining deeper emotional expressions that may reflect customer experiences more comprehensively. To address this gap, this study applies a Natural Language Processing (NLP) approach using the pre-trained DistilBERT transformer model to detect emotional patterns in women’s fashion product reviews. The dataset, obtained from Kaggle’s Women’s E-Commerce Clothing Reviews, contains approximately 23,000 entries and includes review texts along with demographic and product-related attributes. The research workflow consists of data cleaning, feature engineering, exploratory text analysis, and emotion detection using the DistilBERT-based emotion classifier. All analyses were performed using Python in the Google Colab environment. The results reveal that positive emotions, particularly joy and admiration, dominate customer feedback, indicating strong satisfaction with product fit and quality. Conversely, negative emotions such as anger and sadness appear more frequently in reviews mentioning sizing inconsistencies, fabric issues, or unmet expectations. The combination of sentiment context, emotional tone, and engineered features provides a more nuanced understanding of customer behavior compared to sentiment polarity alone. These findings highlight the potential of emotion-aware analytical approaches to support e-commerce businesses in improving product development, enhancing customer experience, and designing data-driven marketing strategies.
Co-Authors Ariani, Alma Nur Alamsyah, Nur Rika Agustina, Rika Rosadi, Muhammad Edya