Dental Service Strategy of Hospitals in Indonesia Based on Big Data Analysis ofSocial Media Users X - Alfiatuz Zahro Al Istiqlaliyah; Muhammad Basir; Abd. Malik Iskandar; Muhammad Idris Paratai; Hairuddin K.; Saparuddin Latu; Onyekwere, Chrisogonus K.; Wicaksono, Yudi Krisno (Universitas Negeri Semarang in collaboration with Ikatan Ahli Kesehatan Masyarakat Indonesia (IAKMI Tingkat Pusat) and Jejaring Nasional Pendidikan Kesehatan (JNPK), 2026)
This study analyzes public perception of hospital dental polyclinic services in Indonesia through big data sentiment analysis of 3,075 tweets from the X platform (January–November 2024), using keywords like “dental hospitals” and “dental poly hospitals.” Employing a descriptive design, data was crawled via Google Colab, followed by preprocessing steps: duplicate removal, non-essential character cleanup, normalization, tokenization, stopword elimination, and stemming. The Naïve Bayes Classifier algorithm classified sentiments, revealing 61.54% negative sentiment (1,571 tweets) and 38.46% positive sentiment (982 tweets), with 73% model accuracy, 0.73 negative precision, and 0.90 negative recall. Negative sentiments highlighted issues, such as long waiting times, inadequate facilities, and BPJS-related administra- tive delays, while positive sentiments praised professional staff and comprehensive facilities. Field data from tweet analysis showed frequent complaints about procedural discomfort (e.g., “pain,” “pluck”) and appreciation for competent doctors. SWOT analysis, triangulated with multi-stakeholder assessments (hospital administrators, directors, dentists), yielded an IFAS score of 7.04 and an EFAS score of 6.84, indicating strong internal capabilities but operational inefficiencies. Strategic recommendations include optimizing workflows, standardizing services, and leveraging public awareness to enhance specialized dental care, addressing public dissatisfaction for sustainable service improvement.