Jurnal Aplikasi Sains Data

Vol. 1, No. 2, 2025

This issue showcases five applied data-science studies that translate methods into impact across health, safety, education, and regional development. The contributions span modern machine learning, classical statistics, and interactive analytics each grounded in real Indonesian contexts and 2024–2025 data.

Highlights of the Issue

• CNN for Web-Based Indonesian–to–Sign-Language Translation
  A prototype pipeline leveraging convolutional neural networks delivers real-time sign rendering from Indonesian text, pointing toward inclusive, accessible web interfaces for the deaf community.

• Wilcoxon Analysis of Infant Malnutrition Pre/Post COVID-19 in North Sumatra
  A rigorous nonparametric assessment quantifies shifts in malnutrition cases across pandemic phases, offering statistically defensible evidence for post-pandemic policy targeting.

• Feature-Importance–Guided Ensemble for Thyroid Cancer Recurrence
  An interpretable ensemble framework prioritizes clinically salient predictors to improve recurrence risk stratification in differentiated thyroid cancer.

• XGBoost for 2024 Fire-Risk Classification in Surabaya with Streamlit Maps
  Gradient-boosted models classify urban fire risk and are deployed via an interactive Streamlit dashboard, enabling spatial exploration for first responders and city planners.

• K-Means Clustering of East Java Regencies/Cities by 2024 HDI Indicators
  Unsupervised segmentation reveals development archetypes, supporting differentiated regional interventions and benchmarking.

Editorial Note
Collectively, these papers demonstrate how methodological rigor, CNNs, XGBoost, ensembles, nonparametric inference, and clustering can be paired with usability (web apps, dashboards) to inform decisions. We hope this issue serves researchers, practitioners, and policymakers seeking reproducible, actionable data-science solutions for Indonesia.