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Margaretha Ari Anggorowati
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Metode Penanganan Multikolinieritas pada RLB: Perbandingan Partial Least Square dengan Ridge Regression Yulia Atma Putri; Margaretha Ari Anggorowati
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 8 No 2 (2016): Journal of Statistical Application and Computational Statistics
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (730.155 KB) | DOI: 10.34123/jurnalasks.v8i2.55

Abstract

Multicolinierity between variable predictor in multiple regression is assuming violation for ordinary least square estimator (OLS). Ridge Regression (RR) and Partial Least Square Regression (PLSR were used to handle the multicolinierity problem. RR modify OLS by adding subjective bias consatant, while PLSR, generalize and combine Principal Component Analisis and multiple regression. The efficiency of these two methods will be compared based on the value of RMSE. This study simulated generating data in different level of multicolinearity, the number of variabel, and number of observation were controlled. This study results that, overall, both method equally efficient.
Aplikasi Model Functional ARMA Process Terhadap Data Arus Lalu Lintas Margaretha Ari Anggorowati; Anugerah Karta Monika
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 11 No 1 (2019): Journal of Statistical Application and Computational Statistics
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1546.442 KB) | DOI: 10.34123/jurnalasks.v11i1.185

Abstract

Model lalu lintas makroskopik merupakan kombinasi model Principal Component Analysis (PCA) dengan timeseries ARMA (Autoregressive Moving Average). Pemodelan lintas mikroskopik dapat digunakan dalam solusi permasalahan prediksi kemacetan lalu lintas di Indonesia. Penelitian ini bertujuan untuk mengaplikasikan functional ARMA(p,q) processes dan model berbasis vektor pada functional PCA guna memprediksi kemacetan lalu lintas pada jalan tol. Data yang digunakan merupakan high dimensional data yang diambil dari Road Traffic Microwave System (RTMS) Jasa Marga pada ruas Jalan Tol Cikampek. Variabel yang digunakan untuk memprediksi kemacetan adalah kecepatan, arus, dan kepadatan dengan rentang waktu jam. Dari hasil functional data analysis, dilakukan pemodelan ARMA dengan hasil model terbaik adalah ARMA(1,1).
Co-Authors Anugerah Karta Monika Yulia Atma Putri