ANALISIS VISUAL DAN KLASTERISASI MULTIDOMAIN MENGGUNAKAN PYTON: KEUANGAN, GIZI, DAN POLITIK
Abstract
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.
References
Harahap, S.S. (2018). Analisis Kritis atas Laporan Keuangan. RajaGrafindo.
McKinney, W. (2017). Python for Data Analysis. O'Reilly.
Pedregosa et al. (2011). Scikit-learn: Machine Learning in Python. JMLR.
Subramanyam, K.R., & Wild, J.J. (2014). Financial Statement Analysis. McGraw-Hill.
Sugiyono. (2015). Metode Penelitian Kombinasi. Alfabeta.
Brigham, E.F., & Houston, J.F. (2021). Fundamentals of Financial Management. Cengage.
Piketty, T. (2014). Capital in the Twenty-First Century. Harvard Press.
Kiyosaki, R.T. (2017). Why the Rich Are Getting Richer. Plata Publishing.
Sulistyo, E. (2023). Angsa Hitam Menyalakan Jakarta.
