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All Journal Journal Health Applied Science And Technology (JHAST) LIMEEMAS: Jurnal Ilmiah Pendidikan
Theo Vhaldino
Universitas Aisyiyah Palembang
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Education Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Other

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Breaking the Data Barrier: Teaching Health Informatics and Big Data Literacy to Health Sciences Students Fitri Yani; Theo Vhaldino; Arpa Pauziah
LIMEEMAS: Jurnal Ilmiah Pendidikan Vol. 4 No. 2 (2026): LIMEEMAS: Jurnal Ilmiah Pendidikan
Publisher : Asosiasi Prodi Manajemen Administrasi Pendidikan Indonesia (APMAPI)

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Abstract

The rapid digitalization of healthcare has increased the need for graduates who can understand, interpret, and responsibly use health-related data. However, students entering health and technology-related programs may encounter barriers when learning health informatics and big data, particularly when technical concepts must be connected with clinical and professional contexts. This qualitative study explored students' experiences of learning health informatics and big data literacy through an interdisciplinary learning approach at Universitas ‘Aisyiyah Palembang. The study involved 30 undergraduate students, consisting of 20 Information Technology students and 10 Nursing students, and was conducted from 12 March to 1 April 2026. Data were collected through classroom observations, students' reflective responses, semi-structured interviews, and learning artifacts, and were analyzed using thematic analysis. The findings revealed five interconnected themes: encountering the data barrier, moving from technical complexity toward meaningful understanding, learning through disciplinary complementarity, connecting healthcare data with professional practice, and developing confidence and responsible data awareness. The findings indicate that students' difficulties were not limited to technical skills but also involved unfamiliar terminology, contextual interpretation, disciplinary differences, and uncertainty about the responsible use of health information. Interdisciplinary learning provided opportunities for Information Technology and Nursing students to exchange complementary perspectives, connecting technological knowledge with clinical meaning. The study concludes that health data literacy should be understood as a multidimensional competency involving data understanding, interpretation, contextualization, communication, and ethical use. Interdisciplinary health informatics education can therefore serve as a meaningful pedagogical pathway for preparing future professionals to participate in increasingly data-driven healthcare environments.
Analisis Komparatif Algoritma Pembelajaran Mesin untuk Prediksi Risiko Hipertensi: Gradient Boosting, Random Forest, XGBoost, Regresi Logistik, dan Support Vector Machine theo vhaldino; Arpa Pauziah; Khoirin; Rudiansyah; Debbi Susita Yuzzaki; Ahmad Syarif
jurnal kesehatan terapan sains dan teknologi Vol 4 No 2 (2026): Journal Health Applied Science And Technology (JHAST)
Publisher : IKesT Muhammadiyah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52523/jhast.v4i2.128

Abstract

Hypertension remains a major contributor to cardiovascular morbidity, yet community screening often relies on late or opportunistic measurements. This study compares Logistic Regression, Random Forest, Gradient Boosting, XGBoost, and Support Vector Machine for hypertension risk prediction using a public dataset of 1,985 patient records and ten demographic, clinical, and lifestyle predictors. Numerical features were standardized, categorical features were one-hot encoded, and missing medication values were imputed using the mode. Models were evaluated using an 80:20 stratified train-test split and 5-fold cross-validation. The best model was optimized with RandomizedSearchCV and examined through feature importance and ablation study. Tuned Gradient Boosting achieved the highest cross-validation F1-score of 0.9994 and ROC-AUC of 1.0000. On 397 test records, it obtained 0.9975 accuracy, 1.0000 precision, 0.9951 recall, 0.9976 F1-score, and 1.0000 ROC-AUC, with only one false negative. Ablation showed that removing blood pressure history reduced test F1-score to 0.7681, confirming its dominant contribution. The model is promising for screening support, but external clinical validation is required before diagnostic use.
Co-Authors Ahmad Syarif Arpa Pauziah Debbi Susita Yuzzaki Fitri Yani Khoirin rudiansyah