Skripsi

 PENINGKATAN AKURASI IMPUTASI DATA YANG HILANG PADA DATA DERET WAKTU MULTIVARIAT MENGGUNAKAN DEEP LEARNING.

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Missing data is a common and complex issue in the industrial world, making data processing more challenging. Imputation methods, whether conventional or using neural networks, are employed to address this issue by estimating or computing the missing values. Deep learning is chosen for its ability to unearth hidden information within data, significantly enhancing the data imputation process. This study utilizes three deep learning methods: LL-CNN, EDR-CNN, and MIRNet. The performance of these methods is evaluated based on root mean squared error (RMSE), mean absolute error (MAE), and R-squared (R²) on eight different datasets: MIMIC-IV, MIMIC III, Beijing Multi-Site Air Quality, Air Quality Italy, Air Quality India, US Pollution, Beijing PM2.5, and Guangzhou. The results of the study show that EDR-CNN provides the best performance across all eight datasets.

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Availability

Detail Information

Series Title

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Call Number

T1559152024

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xiii, 131 hlm.; ilus.; tab, 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

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Subject(s)

Pemrosesan data
Prodi Sistem Komputer

Specific Detail Info

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Statement of Responsibility

SEW

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