Perancangan Data Warehouse (Studi kasus: Analisis Tren Penyakit Menular)
Keywords:
Data Warehouse, Infectious Diseases, ETL, Fact Constellation Schema, Business Intelligence, Data VisualizationAbstract
Infectious diseases such as COVID-19, Tuberculosis, and Malaria remain significant global health challenges. A major obstacle in mitigating these outbreaks is the fragmentation of healthcare data, which leads to delays in analysis and decision-making. This study aims to design a Data Warehouse capable of integrating surveillance data from various heterogeneous sources to support real-time disease trend analysis. The methodology employed is a bottom-up approach utilizing a three-tier architecture. The data integration process is executed through an Extract, Transform, and Load (ETL) mechanism using Pentaho Data Integration to ensure data quality and consistency. Data storage implements a Fact Constellation Schema within a PostgreSQL database, enabling simultaneous multidimensional analysis of infection cases and mortality rates. The result of this research is a prototype of an interactive dashboard based on Tableau, which presents visualizations of geographic distribution (GIS) and temporal trend graphs. This implementation demonstrates that the centralization of healthcare data can facilitate more effective outbreak monitoring and support evidence-based public health policymaking.
Keywords: Data Warehouse, Infectious Diseases, ETL, Fact Constellation Schema, Business Intelligence, Data Visualization.
Abstrak
Penyakit menular seperti COVID-19, Tuberkulosis, dan Malaria masih menjadi tantangan kesehatan global yang signifikan. Salah satu hambatan utama dalam mitigasi wabah ini adalah fragmentasi data kesehatan yang menyebabkan keterlambatan dalam analisis dan pengambilan keputusan. Penelitian ini bertujuan untuk merancang sebuah Data Warehouse yang mampu mengintegrasikan data surveilans dari berbagai sumber heterogen untuk mendukung analisis tren penyakit secara real-time. Metodologi yang digunakan adalah pendekatan bottom-up dengan arsitektur tiga lapisan (three-tier architecture). Proses integrasi data dilakukan melalui mekanisme Extract, Transform, and Load (ETL) menggunakan Pentaho Data Integration untuk menjamin kualitas dan konsistensi data. Penyimpanan data menerapkan Fact Constellation Schema (Skema Galaksi) pada basis data PostgreSQL, yang memungkinkan analisis multidimensi terhadap kasus infeksi dan mortalitas secara bersamaan. Hasil penelitian ini berupa purwarupa dashboard interaktif berbasis Tableau yang menyajikan visualisasi sebaran geografis (GIS) dan grafik tren temporal. Implementasi ini membuktikan bahwa sentralisasi data kesehatan dapat memfasilitasi pemantauan wabah yang lebih efektif dan mendukung perumusan kebijakan kesehatan masyarakat yang berbasis bukti (evidence-based policy).
Kata Kunci: Data Warehouse, Penyakit Menular, ETL, Fact Constellation Schema, Business Intelligence, Visualisasi Data.
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References
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