Prediksi Pencapaian Target Kerja Menggunakan Metode Deep Learning dan Data Envelopment Analysis
Sanjaya David
Primary Author
mixed material
bibliography
Bandung
Maranatha University Press
2020
Indonesia
hlm : 288-300
JUTISI : Jurnal Teknik Sipil dan Sistem Informasi
Abstract
Along with the rapid development of technology, especially in the computer field, several methods have been developed for target setting. Data Envelopment Analysis (DEA) is commonly employed to analyze efficiency levels based on historical data with static targets. Data Envelopment Analysis results in a low level of efficiency against the use of static targets. A new target setting solution is needed to handle dynamic targets. Based on the need, we propose a method to predict more realistic dynamic targets using Deep Learning Long Short Term Memory (LSTM) approach from the results of the Data Envelopment Analysis (DEA). This study leads to a prediction model with 71.2% average accuracy.
Informatika
Sistem Informasi
JUTISI
24432210
Perpustakaan Teknik UPI YAI
JUTISI V6N2 Agustus 2020
JUTISI2-013
Perpustakaan FT UPI YAI
JUTISI V6N2 Agustus 2020
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2023-02-22 14:23:11
2023-02-22 14:23:11
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