Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Viva Medika: Jurnal Kesehatan, Kebidanan dan Keperawatan Journal of Advanced Health Informatics Research
Dina Febrina
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Engineering Health Professions Medicine & Pharmacology Nursing Public Health

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Hybrid Ensemble Learning for Classifying Prescription vs. Over-the-Counter Medicines on Large-Scale Categorical and Textual Data Reina Melani; Dina Febrina
Journal of Advanced Health Informatics Research Vol. 3 No. 1 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v3i1.341

Abstract

The classification of drugs into Prescription (Rx) and Over-the-Counter (OTC) categories is an important aspect of pharmaceutical governance because it has a direct impact on patient safety, drug access, and regulatory compliance. However, large-scale pharmaceutical data often consists of heterogeneous categorical variables and short texts, such as product names or indications, which poses challenges in the form of duplication, inconsistencies, and potential class imbalances. This condition demands a modeling approach that is not only accurate, but also lightweight and explainable. This study proposes a hybrid ensemble model that combines three algorithms, namely CART, Random Forest, and LightGBM, through a weighted soft-voting mechanism. This approach combines decision tree transparency with the reliability of modern boosting techniques. The main contribution of this study is to show that a low-complexity domain-based pipeline can produce accurate, efficient, and easily auditable Rx and OTC classifications for both clinical and regulatory needs. The pre-processing pipeline includes TF-IDF for short text, One-Hot Encoding for categorical features, as well as simple dosage variables. All features were combined into a solid matrix, then trained using weighted ensembles [1,1,8]. Evaluations include Accuracy, Precision, Recall, F1-score, ROC-AUC, Brier score, confusion matrix, and ROC curve. Test results on a dataset of 50,000 balanced samples showed consistent in-sample performance: Accuracy = 0.742; Accuracy = 0.742; Recall = 0.742; F1 = 0.742; ROC-AUC = 0.819; then Brier score = 0.214. The model is able to stably distinguish classes with a balance between False Positive and False Negative errors. In conclusion, this lightweight ensemble is able to present competitive prediction performance as well as interpretation, so that it has the potential to be applied to pharmacovigilance and drug classification. Further studies suggest adding cross-validation, probability calibration, as well as robustness tests to data outside the distribution to strengthen the reliability of the model
Characterization of Pregelatinized, Pentanol and Acetylated Modified Primary Starch Desy Nawangsari; Rani Prabandari; Dina Febrina
Viva Medika Vol 17 No 1 (2024)
Publisher : Universitas Harapan Bangsa Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v17i1.1354

Abstract

Tablets are pharmaceutical preparations consisting of active ingredients and excipients. One of the commonly used excipients is starch. (Colocasia esculenta (L). Schott var, Pratama) is a plant that contains a high source of starch. The use of natural starch in tablets has limitations in the form of less fast flow and poor compressibility. The purpose of this study was to modify natural starch by pregelatinization, pentanol and acetylation. This research was conducted using laboratory experimental methods, starting with sorting the starch tubers of (Colocasia esculenta (L). Schott var, Pratama), starch isolation, modification and characterization of the resulting starch. The characterization results showed that the flow rate of natural starch, pregelatinized, pentanol and acetylated sequentially was 1.9 ± 0.19; 2.15 ± 0.37; 1.35 ± 0.15; and 1.95 ± 0.14 g/s with an angle of repose of 20.49 ± 1.99; 17.12 ± 1.99; 22.92 ± 1.18 and 20.06 ± 1.97°, while the compressibility value is 20.23 ± 5.31; 15.47 ± 3.03; 21, 09 ± 2.8 and 19.09 ± 2.05%.
Co-Authors Desy Nawangsari Rani Prabandari Reina Melani