Analisis tren dan musiman jumlah kendaraan menyeberang di Pelabuhan Merak-Bakauheni tahun 2023-2024

OHS-GIS

Syntax Analisis Data Eksploratif Magnitudo Gempa Kelompok 3

This report is a quick progress update on my Capstone project. I explored three English text datasets — blogs, news, and Twitter — and I'm sharing what I found so far, plus my early plan for a prediction algorithm and a Shiny app. The short version: word frequencies are heavily concentrated.

Nama: Sofi Fakidhotul Mutafida NIM: 2507016014 Kelas: A

A critical analysis presented at the "Promises and Perils: Generative AI in Education" symposium. This presentation explores the tension between the efficiency gains of using Large Language Models (LLMs) for automated scale construction and the inherent fairness risks of relying on synthetic respondents. It details how synthetic data, despite showing high parameter correlations, suffers from severe structural misfit, narrow proficiency distributions (range restriction), and defaulting to Western-centric norms due to safety alignment (RLHF) procedures. Contains dynamic ggplot2 and Mermaid.js visualizations illustrating the "Reliability Illusion."

Analisis data runtun waktu (time series) jumlah kendaraan yang menyeberang di Pelabuhan Merak–Bakauheni periode Januari 2023–Desember 2024, meliputi pembuatan objek ts, identifikasi komponen tren, identifikasi bulan-bulan puncak kunjungan (Lebaran & Nataru), serta analisis pola musiman melalui seasonal plot dan boxplot.

Con corte al 09 de septiembre de 2026