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All Journal Journal of Mathematics, Computation and Statistics (JMATHCOS) Teknobuga : Jurnal Teknologi Busana dan Boga
Muhammad Syafiq
IPB University
Engineering Mathematics

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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.
Clustering of Central Java Districts Based on Educational Indicators: A Comparison of K-Means and Hierarchical Methods Muhammad Syafiq; Nabila Fida Millati; Muh Akbar Idris; Anwar Fitrianto; Kevin Alifviansyah; Erfiani Erfiani
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/xen35m31

Abstract

This study aims to cluster districts and municipalities in Central Java based on educational indicators and to compare the clustering performance of K-Means and Hierarchical methods. The analysis uses secondary data from the Statistical Publication of Education in Central Java Province 2024, covering eight indicators related to educational facilities, participation, and attainment. The data were standardized, explored using descriptive statistics, and analyzed using K-Means and Hierarchical clustering methods. The evaluation results show that both methods produced broadly comparable clustering structures. However, Hierarchical Clustering demonstrated slightly stronger performance in terms of cluster separation and compactness, with a higher Silhouette Index (0,591) and Dunn Index (0,320) and a lower Davies–Bouldin Index (0,501) compared with K-Means (SI 0,584, Dunn 0,225, DBI 0,562). Meanwhile, K-Means produced a more balanced partition and a higher Calinski–Harabasz Index (48,63) than Hierarchical Clustering (44,30). The clustering results reveal a clear pattern of educational disparities across the region. A small group consisting of Sukoharjo Regency and the cities of Semarang, Surakarta, Salatiga, and Magelang forms a higher-performing cluster characterized by stronger educational indicators, while most rural districts belong to a lower-performing group. These findings indicate that educational disparities in Central Java remain spatially concentrated and highlight the need for targeted policies to strengthen educational investment and improve progression to higher levels of education in less developed districts.
Co-Authors Bagus Sartono Carlya Agmis Aimandiga Cici Suhaeni Erfiani Erfiani Fitrianto, Anwar Gerry Alfa Dito Kevin Alifviansyah Muh Akbar Idris Nabila Fida Millati Wawan Saputra