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All Journal JiTEKH (Jurnal Ilmiah Teknologi Harapan)
Rahmat Shofan Razaqi
Program Studi Pendidikan Teknologi Informasi, STKIP PGRI Situbondo, Situbondo
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering

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Sistem Analisis Sentimen Ulasan Produk Tokopedia Menggunakan Metode VGG16 Untuk Prediksi Rekomendasi Produk Sesuai Minat Pengguna Ndhogal Rakasiwi Dhofisa; Nur Azizah; Rahmat Shofan Razaqi
JiTEKH Vol. 14 No. 2 (2026): September 2026
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35447/jitekh.v14i2.1375

Abstract

Tokopedia, as an e-commerce platform, provides a large number of product reviews containing user opinions on product quality, seller service, and delivery processes. These reviews can be used to assess customer satisfaction and serve as important signals in developing product recommendations. This study proposes a Tokopedia product review sentiment analysis system using a deep learning approach based on Convolutional Neural Networks (CNN) using the VGG16 architecture. To adapt the image-oriented VGG16 to text data, text reviews are rendered into 224x224 images (text-to-image), then VGG16 transfer learning is performed to classify sentiment into positive, negative, and neutral. The resulting sentiment scores are then combined with user interest profiles (frequently viewed and purchased categories) to predict more relevant product recommendations. The system stages include data collection, sentiment labeling, preprocessing, review image formation, VGG16 model training, and evaluation using a confusion matrix and metrics such as accuracy, precision, recall, and F1-score. The system implementation is expected to help users find products that match their interests with the support of more objective review sentiment.
Analisis Sentimen E-Commerce Shopee Menggunakan Metode Support Vector Machine (SVM) untuk Mengevaluasi Kualitas Layanan Penjual Aidil Ramadani; Nur Azizah; Rahmat Shofan Razaqi
JiTEKH Vol. 14 No. 2 (2026): September 2026
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35447/jitekh.v14i2.1376

Abstract

The rapid growth of e-commerce generates a large volume of Shopee customer reviews, making manual evaluation inefficient and inconsistent. This study addresses the need for automated text classification to identify review polarity and support seller service evaluation. A labeled dataset of 800 Shopee reviews (600 positive, 200 negative) was collected from Kaggle. The texts were preprocessed using case folding, tokenization, stopword removal, and stemming to reduce noise and standardize writing. Reviews were converted into TF-IDF feature vectors (max_features=5,000; n-gram range 1-2) and classified using a Support Vector Machine (SVM) with an RBF kernel and class-weight balancing. The data were split with stratified sampling (80:20) to preserve label proportions in training and testing sets. Model performance was measured using accuracy, precision, recall, F1-score, and a confusion matrix. The classifier achieved 83.75% accuracy and produced stronger results for positive reviews, while negative reviews were more frequently misclassified due to class imbalance and diverse complaint expressions. As supporting documentation, a subset of reviews was prepared as screenshots and annotated in Roboflow, while the main sentiment evaluation remained based on TF-IDF feature extraction and SVM classification. Overall, TF-IDF combined with SVM provides a practical approach for monitoring customer sentiment and prioritizing seller service improvements over time.
Co-Authors Aidil Ramadani Ndhogal Rakasiwi Dhofisa Nur Azizah