CD Skripsi

Perbandingan Metode Seleksi Fitur Dalam Klasifikasi Data Emisi Gas Rumah Kaca

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Classification analysis is a supervised learning method that can be applied to classify greenhouse gas emission levels. Greenhouse gas emissions need to be monitored regularly so that relevant agencies can plan programs to prevent and overcome the impacts of climate change. In classification analysis, increasing model performance is related to the number of features/variables used, so in its application feature selection is needed. In this research, the best feature selection methods for classification of greenhouse gas emission levels will be compared with filter (information gain and relief-f), wrapper (recursive feature elimination and boruta), and embedded (least absolute shrinkage and selection operator) feature selection. random forest). The Support Vector Machine (SVM) algorithm is used to see the classification performance, which in this research is a binary classification with “high” and “low” class.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

2003111098

Penerbit

Pekanbaru : Universitas Riau FMIPA Statistika., 2024

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

2003111098

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

STATISTIKA

Info Detail Spesifik

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Pernyataan Tanggungjawab

Mutia

Versi lain/terkait

Lampiran Berkas

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