JBASE - Journal of Business and Audit Information Systems

Stunting is a serious health issue in Indonesia, including in East Nusa Tenggara Province (NTT), which requires an analysis of public perception to support data-driven policies. This study proposes sentiment analysis using deep learning and machine learning approaches to classify public opinions regarding stunting from social media/online platforms. It aims to evaluate the performance of the BERT (Bidirectional Encoder Representations from Transformers) and SVM (Support Vector Machine) models in identifying sentiment (positive, negative, neutral), compare the advantages of BERT (transformer-based) and SVM (traditional machine learning) for sentiment classification tasks, and analyze the linguistic and contextual factors influencing sentiment polarity through text feature extraction. The research methods include collecting text data from digital platforms, text preprocessing, and model training with BERT embeddings as input features for SVM. The results are compared with traditional baselines (TF-IDF and word2vec) to measure accuracy improvement. The evaluation results show that for Negative Sentiment (86 tweets) Precision: 58%, Recall: 58%, F1-Score: 58%, Accuracy: 100%. Neutral Sentiment (814 tweets) Precision: 30%, Recall: 20%, F1-Score: 25%, Accuracy: 100%. Positive Sentiment (100 tweets) Precision: 60%, Recall: 75%, F1-Score: 68%, Accuracy: 100%. Meanwhile, SVM with various kernel types showed performance differences in sentiment classification.

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A, D. R. & Riti, Y. F., 2024. Analisis Sentimen Ulasan Aplikasi Genshin Impact di Play Store Menggunakan Random Fores. Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika, pp. 578-586. Tersedia pada https://ejournal.itats.ac. id/snestik/article/view/5883

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Andriani, R., Suhrawardi & Hapisah, 2022. Hubungan Tingkat Pengetahuan Dan Sikap Remaja Dengan. Jurnal Inovasi Penelitian, Volume Vol.2 No.10 Maret, pp. 3441-3446. Tersedia pada https://ejournal.stpmataram.ac.id/JIP/article/view/1341

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_Aplikasi_Analisis_Sentimen_Komentar_Pengguna_Genshin_Impact_Di_ Play_Store

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Kurniyawan , B., Aldino, A. A. & Isnain, A. R., 2022. Sentimen Analisis Terhadap Kebijakan Penyelenggara Sistem Elektronik (Pse) Menggunakan Algoritma Bidirectional Encoderre Presentations From Transformers (Bert). Jurnal Teknologi dan Sistem Informasi (JTSI), 3(E-ISSN: 2746-369), pp. 98-106. Tersedia pada https://jim.teknokr at.ac.id/index.php/sisteminformasi/article/view/2204

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Lenggo, G., Yulianti, E. & Sensuse, D. I., 2023. Sentiment Analysis of Tweets Before the 2024 Elections in Indonesia Using IndoBERT Language Models. Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), 9(DOI: 10.26555/jiteki.v9i3.26490), pp. 746-757. Tersedia pada https://journal.uad.ac.id/index.php /JITEKI/article/view/26490

Liu, M. & Chung, J., 2022. Research of popular success factors of game content

. International Journal of Advanced Smart Convergence, Volume 11, pp. 83-87. Tersedia pada https://www.earticle.net/Article/A414550

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Putra, A. B. et al., 2024. Pengenalan Dasar-Dasar HTML dan CSS Pada Siswa/I Yayasan Al- Qaaf Tangerang Banten Dengan Menggunakan Bahasa Pemprograman Visual Studio Code Serta Mampu Membuat Website Sederhana. APPA : Jurnal Pengabdian kepada Masyarakat, 1(3025-0889), pp. 417-422. Tersedia pada https://jurnalmahasiswa.com/index.php/appa/article/view/915

Putri, D. D., Nama, G. F. & Sulistino, W. E., 2022. Analisis Sentimen Kinerja Dewan Perwakilan Rakyat (Dpr) Pada Twitter Menggunakan Metode Naive Bayes Classifier. Jurnal Informatika dan Teknik Elektro Terapan (JITET), Volume 10, pp. 34-40 Tersedia pada https://journal.eng.unila.ac.id/index.php/jitet/article/view/ 2262

Raharjo, S., Wulandari & Dito, H. A., 2024. Pengaruh Promosi Media Sosial Dan Reputasi Perusahaan Terhadap Keputusan Pembelian Produk Virtual Genshin Impact. Jurnal Arimbi (Applied Research In Management And Business), Volume 3, pp. 40-52. Tersedia pada https://unkartur.ac.id/journal/index.php/arimbi/article/view/230

Ridwansyah, T., 2022. Implementasi Text Mining Terhadap Analisis Sentimen Masyarakat Dunia Di Twitter Terhadap Kota Medan Menggunakan K-Fold Cross Validation Dan Naïve Bayes Classifier. KLIK: Kajian Ilmiah Informatika dan Komputer, Volume 2, pp. 178-185. Tersedia pada https://pdfs.semanticscholar. org/2090/abb19c9620e358e3efe0b86c9b6c974173d7.pdf

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Romadhani, F., Mahbubah, N. A. & Kurniawan, M. D., 2021. Implementasi Metode Lean Six Sigma Guna Mengeliminasi Defect Proses Produksi Purified Gypsum Di PT AAA. Jurnal Peradaban Sains Rekayasan Dan Teknologi, Volume 9, pp. 89-103. Tersedia pada https://www.researchgate.net/profile/Nina-Mahbubah/pu blication/357152383

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Supriyanto, J., Alita, D. & Isnain, A., 2023. Penerapan Algoritma K-Nearest Neighbor (K-NN) Untuk Analisis Sentimen Publik Terhadap Pembelajaran Daring. JURNAL INFORMATIKA DAN REKAYASA PERANGKAT LUNAK

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