Open AccessPrediksi Pencapaian Target Kerja Menggunakan Metode Deep Learning dan Data Envelopment Analysis
Author(s) -
David Sanjaya,
Setia Budi
Publication year - 2020
Publication title -
jutisi (jurnal teknik informatika dan sistem informasi)
Language(s) - English
Resource type - Journals
ISSN - 2443-2229
DOI - 10.28932/jutisi.v6i2.2678
Subject(s) - data envelopment analysis , computer science , field (mathematics) , artificial intelligence , envelopment , data mining , machine learning , operations research , engineering , statistics , mathematics , pure mathematics
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.