ANALISIS VISUAL DAN KLASTERISASI MULTIDOMAIN MENGGUNAKAN PYTON: KEUANGAN, GIZI, DAN POLITIK - Abu Bakar Riziq; Ahmad Fadillah; Fahmi Firmansyah; Gumilang Ali Prayogi; Defri Sulaeman; Intan Kumalasari (Journal of Research and Publication Innovation, 2025)
This study applies data visualization and machine learning techniques to explore patterns across three distinct domains: corporate financial reports, food nutritional content (amino acids), and vote distribution in regional elections. Using Python and libraries such as pandas, scikit-learn, matplotlib, and seaborn, this study utilizes the KMeans algorithm, Principal Component Analysis (PCA), and linear regression. The results are evaluated using the Silhouette Score to assess cluster quality. This study demonstrates that an exploratory approach with Python is effective in uncovering insights from cross-domain data and supporting data-driven decision-making.