CD Skripsi

Perbandingan metode kalman smoothing dan SEASONAL Trend decomposition using loess untuk mengatasi data hilang pada data iklim di lampung tahun 2001 – 2024

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Missing data refers to a condition where part of the dataset is unavailable due to unrecorded information during the data collection process. Missing data problems must be addressed because they can affect the accuracy of the analysis. This study aims to evaluate the performance of univariate imputation methods, namely Kalman Smoothing and STL Decomposition, on time series data obtained from a weather station in Lampung during the period 2001–2024.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

2103112704

Penerbit

Pekanbaru : Universitas Riau FMIPA Statistika., 2025

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

2103112704

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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