Modul 4

Praktik ini membahas proses pembangkitan dan pemodelan data time series menggunakan metode ARIMA. Tahapan analisis dilakukan melalui identifikasi pola data, pengujian kestasioneran, proses differencing, penentuan kandidat model, serta pemilihan model terbaik berdasarkan nilai AIC. Melalui praktik ini, dapat dipahami bahwa model terbaik hasil pemodelan tidak selalu sama dengan model awal pembangkitan, karena dipengaruhi oleh karakteristik data, ukuran sampel, dan unsur acak dalam proses simulasi.

“This project focuses on analyzing IPL 2025 batting and bowling data using R programming. The dataset includes performance metrics such as runs, strike rate, wickets, and economy rate. The project involves data cleaning, merging datasets, and exploratory data analysis. Various visualizations like bar charts, scatter plots, histograms, and pie charts are used to identify trends and patterns. Additionally, regression models are applied to study relationships between variables and predict player performance. The analysis helps in identifying top batsmen, bowlers, and all-rounders, providing meaningful insights into player effectiveness.”

Melakukan klasifikasi segmentasi customer wine menggunakan metode Linear discriminant Analysis (LDA) yang dibandingkan dengan metode Multinomial Logistic Regression (MLR)

Project Progress Report