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All Journal Trigonometri: Jurnal Matematika dan Ilmu Pengetahuan Alam
Nurmardia Abdussamad, Siti
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Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Mathematics Physics

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ANALISIS SENTIMEN TWITTER TERHADAP NYAMUK WOLBACHIA MENGGUNAKAN METODE LSTM DENGAN PENDEKATAN NLTK Lakoro, Tiara; K. Nasib, Salmun; Imansyah Yahya, Nisky; S. Panigoro, Hasan; Nurmardia Abdussamad, Siti
Trigonometri: Jurnal Matematika dan Ilmu Pengetahuan Alam Vol. 6 No. 2 (2025): Trigonometri: Jurnal Matematika dan Ilmu Pengetahuan Alam
Publisher : Cahaya Ilmu Bangsa Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.3483/trigonometri.v6i2.12266

Abstract

Dengue Hemorrhagic Fever (DHF) is one of the major health issues in Indonesia. One of the preventive measures is the Wolbachia mosquito program. However, the implementation of this program has sparked various reactions from the public, which can be observed through social media, particularly Twitter. This study aims to analyze public sentiment towards Wolbachia mosquitoes using the Long Short-Term Memory (LSTM) method and the Natural Language Toolkit (NLTK) approach. Data was collected through a crawling process from Twitter using keywords related to "Wolbachia mosquitoes." Preprocessing was then carried out using NLTK, including tokenization, stopword removal, and stemming. The data was manually labeled into positive, negative, and neutral sentiment categories. The LSTM model was used for sentiment classification with the best parameters, including 100 neurons, a learning rate of 0.001, a sigmoid activation function, a batch size of 32, and 7 epochs. The results indicate that the LSTM model used was able to classify sentiment with an accuracy of 95%, precision of 94%, recall of 97%, and an F1-score of 95%. This demonstrates that the LSTM method with the NLTK approach is effective in analyzing public sentiment towards
Analisis Sentimen Pengguna X (Twitter) Terhadap Kebijakan Tapera Di Indonesia Menggunakan Metode CNN Dan BERT Putri Inombi, Syindikha; Rahmawaty Isa, Dewi; Asriadi; K. Nasib, Salmun; K. Hasan, Isran; Nurmardia Abdussamad, Siti
Trigonometri: Jurnal Matematika dan Ilmu Pengetahuan Alam Vol. 6 No. 2 (2025): Trigonometri: Jurnal Matematika dan Ilmu Pengetahuan Alam
Publisher : Cahaya Ilmu Bangsa Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.3483/trigonometri.v6i2.12267

Abstract

The government’s Housing Savings Program (TAPERA) has sparked various public reactions, particularly on social media platform X (Twitter). This study aims to analyze user sentiment toward the TAPERA policy using the Convolutional Neural Network (CNN) and Bidirectional Encoder Representations from Transformers (BERT) methods. The dataset was collected using a crawling technique on X (Twitter), comprising a total of 1,790 tweets. These data were processed through preprocessing stages, including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. The CNN and BERT models were then trained and tested to classify sentiments as positive or negative. The findings indicate that the BERT model outperformed CNN, achieving a higher accuracy of 86% compared to CNN’s 85%, along with superior recall, precision, and F1-score values. These results suggest that the BERT method is more effective in comprehensively understanding sentiment context.
Co-Authors Asriadi Hasan S. Panigoro K. Hasan, Isran K. Nasib, Salmun Lakoro, Tiara NISKY IMANSYAH YAHYA Putri Inombi, Syindikha Rahmawaty Isa, Dewi