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All Journal JSEH (Jurnal Sosial Ekonomi dan Humaniora) Jurnal Bumigora Information Technology (BITe) Jurnal Teknik Informatika (JUTIF) Journal of Electrical Engineering and Computer (JEECOM) Jurnal Pengabdian kepada Masyarakat IPTEKS Jurnal Pengabdian Pada Masyarakat IPTEKS
Fahry, Fahry
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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
OPTIMIZING SENTIMENT ANALYSIS OF PRODUCT REVIEWS ON MARKETPLACE USING A COMBINATION OF PREPROCESSING TECHNIQUES, WORD2VEC, AND CONVOLUTIONAL NEURAL NETWORK Fahry Maodah; Ema Utami; Sudarmawan Sudarmawan
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 1 (2023): JUTIF Volume 4, Number 1, February 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.1.815

Abstract

This research attempts to identify the most accurate and effective model in performing sentiment analysis on product reviews in marketplaces using preprocessing techniques, word2vec, and CNN. We collected 20,986 reviews from 720 products in a marketplace using scrap method, then cleaned and labeled the data to include 515 positive reviews, 490 negative reviews. We then performed preprocessing on the data using four different scenarios and identified word vector representation using word2vec. Subsequently, we applied the results of word2vec to the CNN architecture to classify sentiment in product reviews. After trying various variations of each technique, we found that a combination of the third preprocessing technique (case folding, punctuation removal, word normalization, and stemming), the second word2vec parameter combination (size 50, window 2, hs 0, and negative 10), and the fourth CNN parameter combination (kernel size 2, dropout 0.2, and learning rate 0.01) had the best accuracy of 99.00%, precision of 98.96%, and recall of 98.96%. We also found that the word normalization technique greatly helped to increase model accuracy by correcting improperly written or incorrect words in the reviews. Based on the evaluation of word2vec, the hs 0 method produced a higher average accuracy compared to the hs 1 method because the hs 0 method used negative sampling which helped the model understand the context of the trained words. In the CNN parameter, higher learning rates can cause the model to learn faster, but can also cause the model to be unstable, while lower learning rates can make the model more stable but can also cause the model's learning process to be slower.
Analyzing Marketplace Reviews Using Word2Vec, CNN, and Deep K-Means with Sociolinguistic Approaches Fahry, Fahry; Miswaty, Titik Ceriyani; Harun, Harun
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5340

Abstract

This study investigates the effectiveness of deep learning methods in analyzing linguistically diverse customer reviews on Shopee to generate actionable product insights. By integrating Word2Vec, Convolutional Neural Networks (CNN), and Deep K-Means clustering, the proposed workflow moves beyond simple polarity detection toward aspect-based sentiment analysis. Customer reviews were preprocessed and represented using Word2Vec (skip-gram) to capture semantic proximity across informal registers, slang, abbreviations, and code-switching. A one-dimensional CNN then classified reviews into positive and negative sentiments, achieving 93–94% accuracy with balanced F1-scores across both classes. To extract aspect-level insights, reviews were projected into a latent space via an autoencoder and clustered using K-Means, with evaluation metrics (Silhouette ≈ 0.6; DBI ≈ 0.5) confirming adequate cohesion and separation. Positive clusters highlighted product design, durability, and ease of use, while negative clusters emphasized material quality, packaging, and delivery issues. These findings demonstrate that deep learning can adapt to sociolinguistic variation in Indonesian e-commerce discourse while providing structured, socially meaningful insights. This research is significant for the field of Informatics as it advances Natural Language Processing techniques for multilingual and code-switched data, addressing a key challenge in real-world text mining applications. The approach offers practical value for sellers in improving product quality, enhancing customer satisfaction, and refining marketing strategies.
Co-Authors Adam, M. Awaludin Ega Dwi Putri Marswandi Elisa, Nopa Ema Utami Hairani Hairani Harun Harun Khasnur Hidjah Komala, Rina Miswaty, Titik Ceriyani Muhammad Agus Muljanto Muharis, Muharis Ondi Asroni RR. Ella Evrita Hestiandari Sudarmawan Sudarmawan Teguh Iman Pribadi Widia Febriana Widia Febriana, Widia YASYIFA DIAN URFINA