A ridgeline plot visualizing the density and distribution of ESG disclosure scores across global companies from 2016 to 2023 using Bloomberg panel data. The visualization highlights a clear shift in corporate sustainability reporting behavior showing a transition from predominantly low, highly concentrated scores in 2016 toward a broader distribution with higher overall compliance by 2023.

Kelompok 9: 1. Muhammad Ramadhan Alba'ary Putra (24031554161) 2. Kafka Praya Firmansyah (24031554182) Link Dataset: https://www.kaggle.com/datasets/samira1992/credit-card-data-intermediate-dataset

An analysis of corporate ESG transparency from 2016 to 2023 using Bloomberg data. The graphic illustrates how different sectors are progressively adopting and improving their ESG disclosures over time, with sectors like Utilities (55) and Materials (15) consistently leading the scores. (Note: 2024 data was dropped due to incomplete data reporting). Sector Codes: 10=Energy, 15=Materials, 20=Industrials, 25=Consumer Discretionary, 30=Consumer Staples, 35=Health Care, 40=Financials, 45=IT, 50=Communication Services, 55=Utilities, 60=Real Estate.

Aula 8. Tutorial para o curso especial de R para análise de dados geoquímicos ministrado no programa de pós-graduação em Geologia e Geoquímica da UFPA.

Penelitian ini bertujuan untuk mengelompokkan perilaku pengguna website e-commerce menggunakan metode clustering pada dataset Online Shoppers Intention. Proses analisis meliputi preprocessing data, deteksi outlier menggunakan Mahalanobis Distance, serta penentuan jumlah cluster optimal dengan metode Elbow dan Silhouette. Selanjutnya, dilakukan penerapan lima metode clustering yaitu K-Means, K-Median, DBSCAN, Mean Shift, dan Fuzzy C-Means. Hasil clustering kemudian dievaluasi menggunakan Silhouette Score, Dunn Index, dan Adjusted Rand Index (ARI) untuk membandingkan performa masing-masing metode dalam mengelompokkan data pengguna.