Sistem Analisis Sentimen Ulasan Produk Tokopedia Menggunakan Metode VGG16 Untuk Prediksi Rekomendasi Produk  Sesuai Minat Pengguna

Ndhogal Rakasiwi Dhofisa; Nur Azizah; Rahmat Shofan Razaqi

doi:10.35447/jitekh.v14i2.1375

Authors

Ndhogal Rakasiwi Dhofisa Program Studi Pendidikan Teknologi Informasi, STKIP PGRI Situbondo, Situbondo
Nur Azizah Program Studi Pendidikan Teknologi Informasi, STKIP PGRI Situbondo, Situbondo
Rahmat Shofan Razaqi Program Studi Pendidikan Teknologi Informasi, STKIP PGRI Situbondo, Situbondo

DOI:

https://doi.org/10.35447/jitekh.v14i2.1375

Keywords:

VGG16, Tokopedia, Sentimen Analysis, Transfer Learning, product recommendations

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.

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References

K. Khairy Dan F. Candra, “Evaluasi Kinerja Yolov8 Dalam Klasifikasi Kualitas Telur Berbasis Warna Dan Tekstur Cangkang”.

N. Rumui Dkk., “Analisis Komparasi Model Deep Learning Cnn Dengan Vgg16 Dalam Klasifikasi Jenis Bunga,” 2025.

Mahfud Hadi, N. Azizah, Dan Rahmat Shofan Razaqi, “Implementasi Dan Perancangan Sistem Informasi Akademik Berbasis Framework Sublime Text,” Juktisi, Vol. 4, No. 2, Hlm. 976–989, Agu 2025, Doi: 10.62712/Juktisi.V4i2.505.

M. I. Putri Dan I. Kharisudin, “Analisis Sentimen Pengguna Aplikasi Marketplace Tokopedia Pada Situs Google Play Menggunakan Metode Support Vector Machine (Svm), Naïve Bayes, Dan Logistic Regression,” Vol. 5, 2022.

X. Zhang, Y. Mao, Q. Yang, Dan X. Zhang, “A Plant Leaf Disease Image Classification Method Integrating Capsule Network And Residual Network”.

S. Efendi Dan N. Azizah, “Rancang Bangun Sistem Informasi Sekolah Sebagai Media Pendataan Berbasis Web Di Smp Islam Darussalam”.

Afis Julianto, Andi Sunyoto, Dan Ferry Wahyu Wibowo, “Optimasi Hyperparameter Convolutional Neural Network Untuk Klasifikasi Penyakit Tanaman Padi,” Teknimedia, Vol. 3, No. 2, Hlm. 98–105, Des 2022, Doi: 10.46764/Teknimedia.V3i2.77.

N. Azizah, R. Jannah, M. Sudur, Z. Rahman, Dan J. Muhammad, “Meningkatkan Efetifitas Penggunaan Absensi Digital Dalam Rekapitulasi Guru Di Sekolah Dasar (Sd) Desa Trebungan,” Mardika, Vol. 2, No. 1, Hlm. 1–9, Mar 2024, Doi: 10.55377/Mardika.V2i1.9732.

S. Uyun, R. P. Rosalin, L. V. Sari, Dan H. H. Sucinta, “A Hybrid Classification Model Based On Bert For Multi-Class Sentiment Analysis On Twitter,” Vol. 11, No. 2, 2025.

Indah Hairunisah, Ida Nurhaida, Dan Revaldo Ilfestra Metsi Zen, “Smartscan-Dfu: Sistem Deteksi Dini Luka Kaki Diabetes Menggunakan Deep Convolutional Neural Network (Cnn),” Rabit, Vol. 11, No. 1, Hlm. 653–666, Jan 2026, Doi: 10.36341/Rabit.V11i1.7101.

C. U. Khasanah, A. K. Pertiwi, Dan F. Witamajaya, “Implementasi Data Augmentation Random Erasing Dan Gridmask Pada Cnn Untuk Klasifikasi Batik”.

A. F. Hidayah Dan A. H. Hasugian, “Rekomendasi Klasifikasi Dan Desain Otomatis Menu Restoran Kopi Xyz Berbasis Web Menggunakan Metode Naïve Bayes,” Vol. 12, No. 6, 2025.

M. Rihamzah, G. A. Pradipta, Dan R. R. Huizen, “Diabetes Mellitus Classification Using Cnn-Based Plantar Thermogram Analysis,” Vol. 11, No. 3, 2024.

D. I. G. Hts, F. Edi, R. S. Hayati, Dan H. S. Ginting, “Prediksi Harga Mobil Global Menggunakan Machine Learning Dengan Algoritma Naive Bayes,” Vol. 12, No. 6, 2025.

K. A. Tompunu Dan Ari Wedhasmara, “Analisis Sentimen Masyarakat Pada Komentar Instagram Terhadap Program Pemerintah Kota Palembang Dalam Pencapaian Sdg 6 Menggunakan Algoritma Naïve Bayes,” Rabit, Vol. 11, No. 1, Hlm. 476–493, Jan 2026, Doi: 10.36341/Rabit.V11i1.6992.

S. K. E. Putri, F. H. Adiba, Dan A. K. Sari, “Implementasi Algoritma Cnn Dalam Pengenalan Wajah Menggunakan Vgg16,” Vol. 4, 2025.

A. Suciati Dan I. Budi, “Ui At Semeval-2020 Task 8: Text-Image Fusion For Sentiment Classification,” Dalam Proceedings Of The Fourteenth Workshop On Semantic Evaluation, Barcelona (Online): International Committee For Computational Linguistics, 2020, Hlm. 1195–1200. Doi: 10.18653/V1/2020.Semeval-1.158.

M. C. Leong, D. K. Prasad, Y. T. Lee, Dan F. Lin, “Semi-Cnn Architecture For Effective Spatio-Temporal Learning In Action Recognition,” Applied Sciences, Vol. 10, No. 2, Hlm. 557, Jan 2020, Doi: 10.3390/App10020557.

B. Sun, L. Yang, P. Dong, W. Zhang, J. Dong, Dan C. Young, “Super Characters: A Conversion From Sentiment Classification To Image Classification,” Dalam Proceedings Of The 9th Workshop On Computational Approaches To Subjectivity, Sentiment And Social Media Analysis, Brussels, Belgium: Association For Computational Linguistics, 2018, Hlm. 309–315. Doi: 10.18653/V1/W18-6245.

S. Ulya, A. Ridwan, W. C. Wahyudin, Dan F. M. Hana, “Text Mining Sentimen Analisis Pengguna Aplikasi Marketplace Tokopedia Berdasar Rating Dan Komentar Pada Google Play Store”.

B. Ahmad, R. Perviaz, A. Qureshi, S. M. Shah, Dan M. Riaz, “Visual Sentiments Analysis Using Deep Learning,” 2023.

M. I. Putri Dan I. Kharisudin, “Analisis Sentimen Pengguna Aplikasi Marketplace Tokopedia Pada Situs Google Play Menggunakan Metode Support Vector Machine (Svm), Naïve Bayes, Dan Logistic Regression,” Vol. 5, 2022.

A. Jadon Dan A. Patil, “A Comprehensive Survey Of Evaluation Techniques For Recommendation Systems,” 12 Januari 2024, Arxiv: Arxiv:2312.16015. Doi: 10.48550/Arxiv.2312.16015

S. Zhang, L. Yao, A. Sun, and Y. Tay, “Deep Learning Based Recommender System: A Survey and New Perspectives,” ACM Computing Surveys, vol. 54, no. 1, pp. 1–38, 2021, doi: 10.1145/3436296

S. Minaee, N. Kalchbrenner, E. Cambria, N. Nikzad, M. Chenaghlu, and J. Gao, “Deep Learning Based Text Classification: A Comprehensive Review,” ACM Computing Surveys, vol. 54, no. 3, pp. 1–40, 2021, doi: 10.1145/3439726.

G. Xu, Y. Meng, X. Qiu, Z. Yu, and X. Wu, “Sentiment Analysis of Product Reviews Based on BERT,” IEEE Access, vol. 8, pp. 116130–116138, 2020, doi: 10.1109/ACCESS.2020.3003306.