Este documento fue elaborado como parte del Taller 5 del curso de Herramientas de Análisis y Visualización de Datos de la Maestría en Epidemiología Clínica - Universidad Icesi. Contiene el análisis gráfico y estadístico de diversas variables mediante R y R Markdown.

Kelompok 12 : 1. Rana Safira (5003231170) 2. Vina Alfita Sari (5003231175) 3. Shabila Cahyaning (5003231180) 4. Fitriya Hana P (5003231185)

Dokumen ini berisi hasil preprocessing data Ujian Nasional Mahasiswa , mencakup tahapan data wrangling, pembersihan missing values, penghapusan duplikasi, deteksi outlier, serta pembuatan variabel baru. Visualisasi boxplot dan histogram digunakan untuk melihat distribusi nilai dan validasi hasil preprocessing

PREDIKSI MODERN DAN MACHINE LEARNING SEMESTER 5

PREDIKSI MODERN DAN MACHINE LEARNING SEMESTER 5

PREDIKSI MODERN DAN MACHINE LEARNING SEMESTER 5

PREDIKSI MODERN DAN MACHINE LEARNING SEMESTER 5

PREDIKSI MODERN DAN MACHINE LEARNING SEMESTER 5

KELOMPOK 6_Azzahra(196)_Zumrotus(191)_Dhafa(192)_Nayara(198)

Data Mining and Visualization

Advanced Time Series Analysis and Decomposition Techniques This comprehensive time series analysis demonstrates advanced statistical modeling capabilities across multiple economic datasets, employing sophisticated decomposition methodologies including classical multiplicative decomposition, STL decomposition, and X-11 seasonal adjustment procedures to isolate trend, seasonal, and irregular components with particular emphasis on Australian labour force dynamics (1978-1995) revealing 38% secular growth dominated by trend components. Key technical achievements include systematic Box-Cox transformation analysis determining optimal variance-stabilizing parameters across diverse datasets—Canadian gas production (λ = 0.577), Australian retail series (λ = 0.371), tobacco production (λ = 0.926), airline passengers (λ = 2.0), and pedestrian traffic (λ = 0.273)—using Guerrero method optimization with clear decision frameworks for transformation necessity, alongside advanced outlier detection utilizing X-11 irregular components to identify structural breaks and anomalous periods in retail data including significant outliers during the early 2000s economic expansion while quantifying outlier effects on seasonal adjustment procedures and demonstrating superior detection capabilities compared to classical methods. The analysis employs a comparative analytical framework systematically evaluating transformation effectiveness through before/after visualizations and statistical validation, applying consistent protocols across heterogeneous datasets to demonstrate scalable methodological approaches suitable for production-level forecasting environments that directly support strategic decision-making in economic forecasting, retail planning, and resource allocation optimization. This demonstrated capability to parse complex temporal signals into interpretable components enables evidence-based policy recommendations and risk assessment protocols essential for senior analytical roles in data-driven organizations, showcasing proficiency in R/fpp3, advanced time series modeling, statistical transformation theory, and macroeconomic data analysis with clear business applications for companies requiring sophisticated analytical infrastructure for temporal pattern recognition and forecasting.