Analisis Sentimen Pengguna X terhadap Perempuan di Lingkungan Kerja Menggunakan Algoritma Machine Learning

Abstract

Gender bias against women in the workplace persists, including within digital interactions on social media. This study analyzes user sentiment on Platform X regarding women in professional contexts using three machine learning algorithms: Naïve Bayes, Support Vector Machine (SVM), and Random Forest. A total of 2,336 tweets were collected using 14 gender-related keywords and labeled both automatically using the DistilBERT model and manually through contextual interpretation. The automatic dataset was imbalanced (1,823 negative, 479 positive), while the manual dataset was more balanced (1,196 negative, 1,106 positive). After preprocessing and TF-IDF feature extraction, the data were split using the train_test_split method. Evaluation metrics included accuracy, precision, recall, and F1-score. Random Forest achieved the highest accuracy (79%) on automatic labels but showed class imbalance (F1-score: 0.88 for negative, 0.08 for positive). Meanwhile, models trained on manual labels showed more balanced performance with accuracy between 57% and 59%. A web application prototype was developed using Flask to predict sentiment related to workplace gender issues. The findings highlight the importance of balanced labeling and appropriate algorithm selection to build fair and reliable sentiment analysis models, contributing to more inclusive digital discourse on gender equality.