Publisher

ATMALUHURATMALUHUR

Journal Jurnal Sisfokom (Sistem Informasi dan Komputer)Jurnal Sisfokom (Sistem Informasi dan Komputer) Abstract

The advancement of information technology and the widespread use of social media have provided a platform for individuals to express their views on various social issues, including to Lesbian, Gay, Bisexual, and Transgender (LGBT) topics. This study aims to assess public sentiment towards LGBT issues on Twitter by employing the Naïve Bayes classification algorithm. Relevant tweets were collected through web scraping based on specific LGBT-related keywords within a defined time frame. The collected data underwent several preprocessing stages, including data cleaning, tokenization, stopword removal, and stemming. The processed data were then categorized into three sentiment classes: positive, negative, and neutral. Naïve Bayes was chosen for its effectiveness and efficiency in handling large-scale textual data. The analysis revealed that negative sentiment toward LGBT issues was predominant, although a considerable portion of tweets expressed neutral and positive sentiments. These findings offer valuable insights for policymakers, social activists, and academics in understanding public perception and formulating more effective communication strategies related to LGBT discourse in Indonesia. The classification model achieved an accuracy of 57%, precision of 52%, recall of 100%, and an F1-score of 68%. While the Naïve Bayes approach proved capable in sentiment classification, the models accuracy could be further enhanced through improved data preparation or the application of more advanced algorithms.

Conclusion

The study successfully developed a sentiment analysis system to capture Indonesian public opinion on LGBT issues through Twitter.The system, built using the Naïve Bayes algorithm, involved data collection, text preprocessing, sentiment labeling, and classification.The evaluation results indicate that the Naïve Bayes algorithm can classify tweets into positive, negative, and neutral categories, achieving an accuracy of 57%, a precision of 52%, a recall of 100%, and an F1-score of 68%.While the Naïve Bayes method shows promise, there is still room for improving overall accuracy through model refinement or exploring more advanced algorithms.

Future Research

Penelitian lebih lanjut dapat dilakukan dengan membandingkan kinerja algoritma Naïve Bayes dengan algoritma machine learning lainnya, seperti Support Vector Machine (SVM) atau deep learning, untuk melihat apakah terdapat peningkatan akurasi dalam klasifikasi sentimen. Selain itu, studi dapat diperluas dengan menganalisis sentimen publik terhadap isu LGBT di platform media sosial lainnya, seperti Instagram atau TikTok, untuk mendapatkan gambaran yang lebih komprehensif mengenai persepsi masyarakat. Terakhir, penelitian dapat fokus pada identifikasi faktor-faktor linguistik dan kontekstual yang memengaruhi sentimen terhadap isu LGBT, seperti penggunaan bahasa sarkastik atau ironi, serta pengaruh peristiwa sosial dan politik terhadap opini publik, sehingga dapat menghasilkan model analisis sentimen yang lebih akurat dan relevan.

Metadata

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Pages	7
DMCA	Report