Tracer Study 2024/2025 memetakan penyebaran, peran, dan kepuasan kerja lulusan S2 Statistika Terapan, serta menghimpun masukan kurikulum terkait kebutuhan AI dan data modern.

Multinomial and Ordinal Logit Model

Introducing Spatial Econometrics

Support Vector Machine (SVM) adalah algoritma pembelajaran mesin yang digunakan untuk klasifikasi dan regresi, dengan tujuan menemukan hyperplane optimal yang memisahkan data ke dalam kelas-kelas berbeda. Dalam R, SVM dapat diimplementasikan menggunakan paket seperti e1071, caret, atau kernlab, yang menyediakan fungsi-fungsi untuk membangun dan melatih model SVM. SVM bekerja dengan memaksimalkan margin antara kelas-kelas data, dan dapat menangani data yang tidak terpisahkan secara linear melalui penggunaan fungsi kernel seperti radial basis function (RBF). Algoritma ini efektif dalam berbagai aplikasi, termasuk pengenalan pola, klasifikasi teks, dan bioinformatika.

"Data Cleaning: From Unstructured to Structured Data (with R)" is the process of transforming messy, raw data into a clean, organized format that can be easily analyzed. Using R, we remove errors, fill in missing values, and restructure the data into tidy tables ready for analysis.

Contoh Kriging

UserStatisfaction

n the rapidly evolving landscape of data science and analytics, the importance of a robust and adaptive curriculum for a Master’s in Applied Statistics cannot be overstated. As we navigate the complexities of the 5.0 society—a term that encapsulates the fusion of digital, physical, and biological realms driven by technological advancements—the necessity for an education system that remains relevant, responsive, and innovative is paramount. To this end, we undertake a rigorous and systematic evaluation of our Master’s in Applied Statistics curriculum to ensure it meets the ever-changing demands of our stakeholders and aligns with the latest technological developments.

The "Advanced Spatial Statistics" major focuses on the sophisticated analysis and interpretation of spatial data. Students learn to create detailed maps, develop spatial models, and perform spatiotemporal forecasting using advanced statistical techniques, preparing them for research and professional roles in various fields requiring spatial data expertise.