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All Journal PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Teknobuga : Jurnal Teknologi Busana dan Boga
Wawan Saputra
IPB University
Computer Science & IT Engineering

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Making Sense of Fashion Feedback : Comparing Two Popular Text Analysis Tools Muhammad Syafiq; Wawan Saputra; Carlya Agmis Aimandiga; Cici Suhaeni; Bagus Sartono; Gerry Alfa Dito
TEKNOBUGA: Jurnal Teknologi Busana dan Boga Vol. 13 No. 1 (2025)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/teknobuga.v13i1.25930

Abstract

The rapid expansion of the fashion industry, propelled by digital technology and e-commerce, has resulted in a significant volume of customer-generated reviews. These reviews serve as a valuable source for understanding customer satisfaction and behavior. This study aims to (1) analyze customer sentiment, (2) predict product recommendations, and (3) examine the relationship between sentiment classification and recommendation decisions using text embeddings from Word2Vec and GloVe. The research utilized over 23,000 fashion product reviews sourced from Kaggle. Text data were preprocessed and vectorized using Word2Vec and GloVe, followed by classification and prediction tasks using six machine learning models: Random Forest, SVM, Naïve Bayes, LSTM, Logistic Regression, and Gradient Boosting. The results revealed that Word2Vec consistently outperformed GloVe across all models and tasks, with the Word2Vec-LSTM combination achieving the highest accuracy of 87.35% and F1 score of 92.35% in imbalanced data scenarios. Correlation analysis also confirmed a strong and statistically significant relationship between sentiment and recommendation labels, with Spearman’s Rho of 0.8340 and Kendall’s Tau of 0.8120. These findings suggest that high-quality sentiment representation can effectively support product recommendation systems. This study contributes to the understanding of embedding effectiveness in fashion-related text analysis and opens avenues for hybrid and transformer-based representations in future research.
Regional Clustering of Food Insecurity to Support the Attainment of SDG 2: Zero Hunger through Machine Learning Approaches Siti Nuradilla; Wawan Saputra; Muhammad Rizal
Joint International Conference 2026: The 2nd Indonesia Forum on Labour and Productivity (IFLP) & The 4th International Conference on Data Science and Official Statistics (ICDSOS) Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.475

Abstract

Food security remains a persistent development challenge in Indonesia, with regional disparities posing significant barriers to achieving equitable access to nutritious and sufficient food. This study aims to classify and cluster districts and cities in Indonesia based on their food security vulnerability levels, thereby supporting the attainment of SDG 2: Zero Hunger. We employed a machine learning approach using a dataset of 514 regions and nine food security indicators sourced from national databases. The classification phase compared three algorithms, Random Forest, XGBoost, and LightGBM, under multiple data preprocessing scenarios, including outlier handling (IQR and Isolation Forest) and class balancing (SMOTE). LightGBM with IQR preprocessing delivered the best performance, achieving an accuracy and F1-score of 0.984. For clustering, DBSCAN and HDBSCAN were applied using the six most important features identified by the classifier. DBSCAN showed slightly better performance based on Silhouette Score (0.5639), resulting in three regional groupings: food-secure, highly vulnerable, and outlier regions. The analysis revealed that socio-economic factors and access to basic infrastructure remain critical determinants of food insecurity. The results underscore the importance of data-driven approaches in policy formulation and highlight the value of machine learning in producing more targeted, efficient, and adaptive food security interventions in Indonesia.
Co-Authors Bagus Sartono Carlya Agmis Aimandiga Cici Suhaeni Gerry Alfa Dito Muhammad Rizal Muhammad Syafiq Siti Nuradilla