Kansei Engineering-based Artificial Neural Network Model to Evaluate Worker Performance in Small-Medium Scale Food Production System
Penulis/Author
Prof. Dr. Mirwan Ushada, S.T.P., M.App.Life.Sc. (1); Tsuyoshi Okayama (2); Dr. Atris Suyantohadi, S.T.P., MT. (3); Dr. Nafis Khuriyati, S.T.P., M.Agr. (4); Haruhiko MURASE (5)
Tanggal/Date
16 2017
Kata Kunci/Keyword
Abstrak/Abstract
This paper highlighted a new method to evaluate worker performance in small medium-scale food production system. By using Kansei engineering, worker performance can be analysed using verbal parameter of profile of mood states and non-verbal parameter of heart rate in a given workplace environment. Fusing various parameters of worker performance requires a robust modelling tool. An artificial neural network (ANN) model is proposed to evaluate worker performance based on categories of normal, capacity constrained and over capacity workers. The training and inspection data were recapitulated from four types of food production systems as tempe, bakpia, fish chips and cracker. The ANN was trained using back-propagation supervised learning method and inspection data. The trained ANN models produced satisfied correlation between measured and predicted value and minimum inspection error. The research result is applicable not only for building Kansei engineering-based sensor, but also for decision support for production planning and control in food production system.
Rumpun Ilmu
Teknologi Industri Pertanian (dan Agroteknologi)
Bahasa Asli/Original Language
English
Level
Internasional
Status
Dokumen Karya
No
Judul
Tipe Dokumen
Aksi
1
IJISE (Kansei engineering-based ANN model to evaluate worker performance in small-medium___) Korespondensi.pdf
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2
IJISE 2017 (Kansei engineering-based ANN Model__) Full Dokumen.pdf
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3
Kansei Engineering-based Artificial Neural Network Model to Evaluate Worker Performance in Small-Medium Scale Food Production System.pdf
