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All Journal Journal of Governance and Local Politics (JGLP) Journal of System and Computer Engineering Journal Pharmacy and Application of Computer Sciences
Delilah, Eva
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Computer Science & IT Decision Sciences, Operations Research & Management Medicine & Pharmacology Social Sciences

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Kajian Persepsi Masyarakat Indonesia terhadap Obat Kimia dan Tradisional melalui Analisis Linguistik dan NLP dalam Konteks Farmasi Irmawati, Irmawati; Aziz, Firman; Delilah, Eva; Ishak, Pertiwi; Jafar, Jafar
Journal Pharmacy and Application of Computer Sciences Vol. 3 No. 1: Februari: 2025: JOPACS
Publisher : Arlisaka Madani Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59823/jopacs.v3i1.76

Abstract

Persepsi masyarakat terhadap obat kimia dan tradisional di Indonesia memiliki pengaruh signifikan terhadap pilihan pengobatan serta tingkat kepatuhan pasien dalam menjalani terapi. Kedua jenis obat ini, meskipun digunakan secara luas, kerap kali dipandang berbeda oleh masyarakat berdasarkan berbagai faktor seperti budaya, nilai-nilai kepercayaan, dan pengalaman personal dalam penggunaannya. Dalam konteks modern yang ditandai dengan berkembangnya teknologi digital, media sosial telah menjadi ruang publik yang penting bagi masyarakat untuk mengekspresikan opini, berbagi pengalaman, dan membentuk narasi kolektif tentang obat-obatan. Artikel ini bertujuan untuk menganalisis persepsi masyarakat Indonesia terhadap obat kimia dan obat tradisional melalui pendekatan interdisipliner yang menggabungkan analisis linguistik dan pemrosesan bahasa alami (Natural Language Processing/NLP). Data dikumpulkan dari platform media sosial seperti Twitter serta forum-forum kesehatan daring selama periode Januari hingga Maret 2024, menghasilkan lebih dari 50.000 unggahan dan komentar. Proses analisis mencakup pra-pemrosesan teks, analisis sentimen menggunakan model IndoBERT, topic modeling dengan BERTopic, dan analisis linguistik untuk menggali kedalaman makna bahasa yang digunakan masyarakat. Hasil penelitian menunjukkan bahwa obat tradisional lebih sering diasosiasikan dengan persepsi positif, terutama terkait sifatnya yang dianggap alami dan aman. Sebaliknya, obat kimia sering dipandang negatif, terutama karena isu efek samping dan ketergantungan. Temuan ini memberikan wawasan penting dalam menyusun strategi komunikasi kesehatan dan edukasi masyarakat yang lebih adaptif terhadap persepsi dan pola bahasa publik di Indonesia.
Sentiment Analysis of Government Policies Using LSTM: The Role of the Indonesian Language in Shaping Public Opinions Delilah, Eva; Syam, Rahmat Fuady; Aziz, Firman
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2574

Abstract

Social media has become the primary arena for the public to express opinions on government policies. This study aims to analyze public sentiment toward government policies using the Long Short-Term Memory (LSTM) model, while also examining the role of language in shaping public opinion. Data were collected from social media posts related to economic, social, and health policies, followed by preprocessing stages including text cleaning, tokenization, stopword removal, and word embedding with Word2Vec. The LSTM model was compared with Support Vector Machine (SVM) and Naïve Bayes to evaluate accuracy and performance. The results indicate that public opinion is dominated by negative sentiment (45%), particularly regarding economic policies. The LSTM model outperformed the benchmarks with an accuracy of 86.9%, surpassing SVM and Naïve Bayes. Linguistic analysis revealed the frequent use of emotional diction, sarcasm, and economic burden narratives that reinforced public resistance, while colloquial language was found to be an effective tool for engaging younger generations. This study contributes to the advancement of sentiment analysis in the Indonesian language using deep learning and provides practical recommendations for policymakers to design more persuasive and participatory communication strategies.
Sentiment Analysis of Indonesian Government Policies Using the LSTM Model for Public Opinion Mapping Rijal, Muhammad; Aziz, Firman; Tenriana, Nuzul; Delilah, Eva
Jurnal Pemerintahan dan Politik Lokal Vol 8 No 1 (2026): JGLP, MAY 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47650/jglp.v8i1.2336

Abstract

Social media has evolved into a primary arena for citizens to express and negotiate opinions regarding government policies, creating vast opportunities for data-driven policy evaluation. This study aims to map public sentiment toward Indonesian government policies by integrating deep learning–based sentiment classification with linguistic and governance analysis. A dataset of approximately 50,000 Indonesian-language posts was collected from Twitter (X) and Facebook between January and June 2024. The data were processed through text cleaning, tokenization, stopword removal, and word embedding using Word2Vec and FastText, and subsequently classified into positive, negative, and neutral sentiments using a Long Short-Term Memory (LSTM) model. The results indicate that public opinion is predominantly negative (45%), particularly in relation to economic and taxation policies, while positive sentiment (34%) is mainly associated with education and health sectors. The LSTM model achieved an accuracy of 86.9%, outperforming Support Vector Machine (SVM) and Naïve Bayes models. Furthermore, linguistic analysis reveals that emotive and sarcastic expressions play a significant role in shaping critical public discourse, whereas colloquial language enhances engagement, especially among younger users. This study contributes by bridging computational sentiment analysis with linguistic interpretation and public policy evaluation within a unified framework. The findings provide practical implications for evidence-based policymaking by enabling governments to monitor public sentiment in real time, improve policy communication strategies, and foster more participatory and responsive governance.
Co-Authors Firman Aziz Irmawati Irmawati Ishak, Pertiwi Jafar Jafar Rijal, Muhammad Syam, Rahmat Fuady Tenriana, Nuzul