Deteksi Dini Status Keanggotaan Industri Kebugaran Menggunakan Pendekatan Supervised Learning
Narabel Julio
Primary Author
mixed material
bibliography
Bandung
Maranatha University Press
2020
Indonesia
hlm : 266-277
JUTISI : Jurnal Teknik Informatika dan Sistem Informasi
Abstract
In the fitness industry, the number of members is a major factor for the sustainability of its business. The ability of managers and trainers to detect members who represent traits to quit membership is critical. Four supervised learning classification methods like Support Vector Machine, Random Forest, K-Nearest Neighbor, and Artificial Neural Network were used to generate early detection using two variants of datasets that have different amounts of data. Classification results are separated into three different zones, which are Green Zone, Yellow Zone, and Red Zone. Artificial Neural Network methods using backpropagation training give 99.90% of accuracy on a dataset which has more amount of data. The evaluation has been done using the confusion matrix and AUC-ROC curves.
Informatika
Sistem Informasi
JUTISI
24432210
Perpustakaan Teknik UPI YAI
JUTISI V6N2 Agustus 2020
JUTISI2-011
Perpustakaan FT UPI YAI
JUTISI V6N2 Agustus 2020
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2023-02-22 14:19:17
2023-02-22 14:19:17
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