tugas komputasi kelompok 5

Sebuah penelitian yang bertujuan untuk mengetahui perbedaan jam belajar mahasiswa dari tiga jurusan berbeda, yaitu Matematika, Statistika, dan Aktuaria.

This presentation showcases a comprehensive sales forecasting analysis using the public Rossmann drugstore dataset from the 2015 Kaggle competition. The case study demonstrates how machine learning transforms retail operations by analyzing 1+ million historical sales records from 1,115 stores across Germany. [Keys] - 48-day sales forecasting with 88% accuracy vs. 65-70% from traditional intuition-based methods - €237M total revenue forecast with €39.9M at-risk revenue identification due to store closures - Business impact analysis revealing €2.1M annual ROI improvement potential through better forecasting Actionable insights including inventory optimization guidelines, underperforming store identification, and weekly sales patterns Implementation roadmap with practical recommendations for operation and store managers The analysis proves that leveraging large-scale historical data (1,017,209 sales records) significantly outperforms traditional forecasting methods based on historical averages or experienced intuition. This case study serves as a practical example of how data science can drive measurable business value in retail operations, providing concrete strategies for inventory management, staffing optimization, and revenue growth.

Oleh Aditya Taufiqur Rahman

analisis simulasi data jam kedatangan mahasiswa antara pukul 07.00-08.00 pada hari senin, rabu, dan jumat. data dibangkitkan dari distribusi uniform dengan 30 observasi per hari, dilengkapi dengan statistik deskriptif dan visualisasi.

Melakukan Uji Normalitas berdasarkan studi kasus lama waktu tidur jurusan Matematika, Biologi dan Kimia - Komputasi Statistik

di sini perintah nya yaitu menghitung angka proporsi nilai yang lebih sama dengan 75 antara kelas A dan c