AbstractThis paper highlighted a daily worker evaluation model for small medium-scale food production system. The model consist of worker capacity assessment and worker performance evaluation sub-models. The model measures the relationship between Total Mood Disturbance (TMD), heart rate of worker and workplace parameters using Kansei Engineering approach.However, the rapid measurement of TMD is difficult and full of bias since using the paper-based questionnaire of Profile of Mood States (POMS). Therefore, a rapid measurement method was developed using Artificial Neural Network to support the application of daily evaluation model. The inputs of the model were heart rate, workplace temperature, relative humidity, light intensity and noise level, which were measured before and after working. The output was TMD score.The training and inspection data for ANN was collected from workers of food production system as Tempe, Bakpia, Fish Chips and Crackers industries in Yogyakarta Special Region.ANN model were tested successfully predicted TMD score using back-propagation supervised learning method. The trained ANN model generated satisfied root mean square error value. ANN model is possible to substitute conventional data acquisition of POMS. The daily evaluation model is applicable to assist industrial management for providing the appropriate worker assignment for shift schedulling and environmental set point for the workplace comfortability

Khuriyati, Nafis

Murase, Haruhiko

Okayama, Tsuyoshi

Suyantohadi, Atris

Ushada, Mirwan

Elsevier - Publisher Connector 

 Agriculture and Agricultural Science Procedia  3 ( 2015 )  84 – 88 
2210-7843 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license 
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of Jurusan Teknologi Industri Pertanian, Fakultas Teknologi Pertanian, Universitas Gadjah Mada
doi: 10.1016/j.aaspro.2015.01.018 
ScienceDirect
The 2014 International Conference on Agro-industry (ICoA): Competitive and sustainable Agro-
industry for Human Welfare 
Daily Worker Evaluation Model for SME-Scale Food Production 
System using Kansei Engineering and Artificial Neural Network 
Mirwan Ushadaa,*, Tsuyoshi Okayamab, Atris Suyantohadia, Nafis Khuriyatia, Haruhiko 
Murasec 
aUniversitas Gadjah Mada, Dept. of Agro-Industrial Technology Jl. Flora, Bulaksumur, Yogyakarta 55281, Indonesia  
bIbaraki University, College of Agriculture, 3-21-1, Chuuo, Ami, Inashiki, Ibaraki, ZIP 300-0393, Japan 
 cOsaka Prefecture University, Graduate School of Engineering, 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka, ZIP 599-8531, Japan 
Abstract 
This paper highlighted a daily worker evaluation model for small medium-scale food production system. The model consist of 
worker capacity assessment and worker performance evaluation sub-models. The model measures the relationship between Total 
Mood Disturbance (TMD), heart rate of worker and workplace parameters using Kansei Engineering approach. However, the 
rapid measurement of TMD is difficult and full of bias since using the paper-based questionnaire of Profile of Mood States 
(POMS). Therefore, a rapid measurement method was developed using Artificial Neural Network to support the application of 
daily evaluation model. The inputs of the model were heart rate, workplace temperature, relative humidity, light intensity and 
noise level, which were measured before and after working. The output was TMD score.The training and inspection data for 
ANN was collected from workers of food production system as Tempe, Bakpia, Fish Chips and Crackers industries in 
Yogyakarta Special Region. ANN model were tested successfully predicted TMD score using back-propagation supervised 
learning method. The trained ANN model generated satisfied root mean square error value. ANN model is possible to substitute 
conventional data acquisition of POMS. The daily evaluation model is applicable to assist industrial management for providing 
the appropriate worker assignment for shift schedulling and environmental set point for the workplace comfortability. 
© 2015 The Authors. Published by Elsevier B.V. 
Peer-review under responsibility of Jurusan Teknologi Industri Pertanian, Fakultas Teknologi Pertanian, Universitas Gadjah 
Mada. 
Keywords: artificial neural network; daily evaluation; profile of mood states; rapid measurement; total mood disturbance 
 
 
* * Corresponding author. Tel.:+62-274-551-219; fax: +62-274-551-219 
      E-mail address: mirwan@tip-ugm.org 
  i  y Elsevier B.V. This is an open access article under the CC BY-NC-ND license 
(http://creativecommons.org/licenses/by- c-nd/4.0/).
Peer-review under responsibility of Jurusan Teknologi Industri Pertanian, Fakultas Teknologi Pertanian, 
Universitas Gadjah Mada
Available online at www.sciencedirect.com
85 Mirwan Ushada et al. /  Agriculture and Agricultural Science Procedia  3 ( 2015 )  84 – 88 
Nomenclature 
AH AngerˉHostility    LI Light intensity (Lux) 
C Confusion    NL Noise level (Decibel)   
D DepressionˉDejection   RH Workplace relative humidity (%) 
F Fatigue     T Workplace temperature (oC) 
FR Friendliness    TA TensionˉAnxiety 
HR  Heart rate (Pulse per minute)  V Vigor 
1. Introduction 
Small medium-scale production system is a fundamental production system in Indonesian industry. Small-
medium scale indicated that the amount of worker is from 5 to less than 100 persons (Indonesian Central Agency in 
Statistics, 2011). There is more than 54.6 million small medium-scale industry who use the man power almost 97.2% 
(Indonesian Central Agency in Statistics, 2011). There is 26.4 million of small medium-scale food industry in 
Indonesia. This kind of industry contributes to the Indonesian food sovereignty (Indonesian Ministry of Cooperatives 
and Small-Medium Enterprises, 2011). 
In a production system, worker evaluation is increasingly important since humans are sensible (Nagamachi, 2002; 
Ushada et al., 2014c). Worker sensibility or known as Kansei is a worker workload response to a different amount of 
production target which are measurable using the verbal and non-verbal parameters (Nagamachi, 2002; Ushada et 
al., 2014b). Evaluating the worker in a food production system is important to decrease error of work method, 
achieve high quality product and minimizing production lead time with a good sensibility. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 1. Daily worker evaluation model for small medium-scale food production system. 
This research proposed a daily worker evaluation model for small medium-scale food production system as 
shown in Fig. 1. The model consist of worker capacity assessment (Ushada et al., 2014a) and worker performance 
evaluation sub-models (Ushada et al., 2014 b). The model measures a relationship between worker and workplace 
parameters using Kansei Engineering approach. Kansei Engineering approach is applicable to analyze the worker 
sensibility using comparison between verbal and non-verbal parameters (Nagamachi, 2002; Ushada et al., 2013). 
Total Mood 
Disturbance
Heart Rate 
Workplace 
Temperature 
Relative 
Humidity 
Light 
Intensity 
Noise 
 Level 
Worker 
Performance 
Evaluation 
Workplace 
Maintenance 
Environment 
Set Point 
Before Working 
After Working 
Rapid 
Measurement 
Daily Check-in/ 
Check-out 
Workplace 
Measurement 
Worker 
Measurement 
Worker Shift 
Scheduling 
Worker 
Assignment 
Data Logger1 
Data Logger2 
Data Logger3 
Data Logger4 
Data Logger5 
86   Mirwan Ushada et al. /  Agriculture and Agricultural Science Procedia  3 ( 2015 )  84 – 88 
Kansei Engineering is a method to extract the verbal and non-verbal response, to help human identifying the needs, 
preference or achieving satisfaction for product/system (Nagamachi, 1995).  
In daily check-in before working and check-out after working, the Total Mood Disturbance (TMD) and heart rate 
were measured using the paper-based questionnaire of Profile of Mood States (POMS) (McNair et al., 1992). In the 
same time, workplace temperature, relative humidity, light intensity and noise level were measured. Heart rate were 
rapidly measured using wrist blood pressure data logger. Workplace temperature and relative humidity were rapidly 
measured using Thermo Recorder (Extech RH520 Data Logger). Light intensity and noise level were rapidly 
measured using Luxmeter and Multifunctional Environment data logger. However, the rapid measurement of TMD 
is difficult since using the paper-based questionnaire of POMS. The paper-based questionnaire was already applied 
as a calibration method in food production system (Ushada et al., 2013). 
Artificial Neural Network (ANN) is applicable for rapid measurement of TMD. It is due to its capability to model 
large number of non-linear parameters in biosystems and Kansei Engineering (Reed and Marks II, 2002; Ushada et 
al., 2014c). Up to date, Ujita and Murase (2006) has developed POMS Software for application of baum test 
characteristic. This software was limited for a relationship between non-linguistic emotional display and data of 
questionnaire method (POMS). This software is not applicable for worker in small-medium scale food production 
system. 
The research objectives are: 1) To propose a daily worker evaluation model for small medium-scale food 
production system; 2) To develop a rapid measurement model for total mood disturbance using Kansei Engineering 
and Artificial Neural Network. The research advantage is to support the development of a daily worker evaluation 
model for small-medium scale food production system. The daily evaluation model is expected to assist industrial 
management for providing the appropriate worker assignment for shift schedulling and environmental set point for 
the workplace comfortability as shown in Fig. 1.  
2. Materials and Methods 
2.1. Case studies 
4 (four) cases study of workers in food production systems were evaluated as Tempe, Bakpia, Fish Chips and 
Crackers. These industries were selected due to the popularity of food product in Indonesian consumer and the 
unique worker profile. 
2.2. Profile of Mood States 
Profile of Mood States (POMS) was selected as verbal parameter because it measures the affective mood state 
fluctuation in a wide variety of populations (McNair et al., 1992; Motohashi et al., 2013; O’Connor, 2014). POMS 
are proposed to measure five identifiable negative moods or affective states of worker as Tension-Anxiety, 
Depression, Anger-Hostility, Fatigue, Confussion and two positive moods as Vigor and Friendliness . POMS are 
measured using questionnaire of 65 words declaring the worker mood. Questionnaire result is analyzed using Total 
Mood Disturbance (TMD) score in equation 1 (McNair et al., 1992) : 
 
TMD  = TA + D + AH + F + C – V- FR                                                                                   (1) 
2.3. Artificial Neural Network 
A rapid measurement model is defined as a black box relationship between non-verbal parameter of heart rate, 
workplace environmental parameters and verbal parameter of mood. Its function for predicting TMD score can be 
defined as dynamic variation of heart rate in a given workplace environmental parameters. Thus, these relationships 
were modelled using a three layered ANN as shown in Fig.2a. The ANN source code was built using Microsoft 
Visual Basic Application for Microsoft Excel (Okayama, 2014). The inputs of the model were total heart rate, 
workplace temperature, relative humidity, light intensity and noise level, which were measured before and after 
working. The output was TMD score. 
87 Mirwan Ushada et al. /  Agriculture and Agricultural Science Procedia  3 ( 2015 )  84 – 88 
 
Fig. 2. Artificial neural network model for rapid measurement of TMD: (a) ANN Structure; (b) Learning performance. 
3. Results and Discussions 
The case study of food production system was Tempe, Bakpia, Fish Chips and Crackers industries in Special 
Region of Yogyakarta. The data was recapitulated in to 572 data set. The 544 data set were used in training ANN 
model, while the remains were used in inspection data. Table 1 indicates an input and output range of ANN training 
data. ANN model was trained using these training data.  
     Table 1. Sensitivity analysis for ANN model 
Range Input Output 
HR (pulse per minute) T (oC) RH (%) LI (Lux) NL (Decibel) TMD Score 
Minimum 63 25 30 1 16 4 
Maximum 123 46 92 12237 3644 12 
 
Based on the sensitivity analysis of output error by trial and error basis (Table 2), seven neurons in the hidden 
layer were determined. The architecture of network consisted of five neurons in the input layer, seven neurons in the 
hidden layer and one neuron in the output layer (5-7-1). 
Table 2. Sensitivity analysis for ANN model 
No Hidden Neuron Learning 
Coefficient 
Momentum Iteration RMSE 
1 5 0.1 0.9 10,000 0.282 
2 5 0.1 0.9 20,000 0.278 
3 6 0.1 0.9 10,000 0.277 
4 6 0.1 0.9 20,000 0.277 
5 7 0.1 0.9 10,000 0.274  
6 7 0.1 0.9 20,000 0.277 
7 7 0.2 0.8 20,000 0.2691 
8 8 0.1 0.9 10,000 0.274 
9 8 0.1 0.9 15,000 0.277 
                              1Minimum error 
The training converged after approximately 20,000 iterations, learning coefficient of 0.1 and momentum of 0.9 
(Fig. 2b). The root mean square error values of training and inspection data were 0.269 and 0.208 respectively. ANN 
model were tested successfully predicted TMD score using back-propagation supervised learning method. 
 
 
 
0
0,2
0,4
0,6
0,8
1
0 5000 10000 15000 20000
R
oo
t M
ea
n 
Sq
ua
re
 E
rr
or
Learning Iteration
RH 
LI 
NL 
HR 
T 
TMD 
a b 
88   Mirwan Ushada et al. /  Agriculture and Agricultural Science Procedia  3 ( 2015 )  84 – 88 
4. Conclusions 
This paper proposed a daily worker evaluation model for small medium-scale food production system using 
Kansei Engineering and Artificial Neural Network. The model consists of worker capacity assessment (Ushada et 
al., 2014a) and worker performance evaluation sub-models (Ushada et al., 2014b). Worker heart rate, workplace 
temperature, relative humidity, light intensity and noise level could be measured daily, before and after working 
using the available data logger while the total mood disturbance could be measured daily using rapid measurement 
of profile of mood states. Kansei Engineering is applicable as a new methodology for developing an ANN model as 
a rapid measurement of total mood disturbance using back-propagation supervised learning method. The trained 
ANN model generated satisfied root mean square error value. The ANN model is applicable to substitute the 
conventional data acqusition of paper-based POMS questionnaire. For further improvement it is suggested that the 
data of POMS are measured precisely minimizing the bias of workplace condition and work methods. The precise 
data will assist the easier training process and stucture selection of ANN model. 
Acknowledgements 
We would like to express our sincere gratitute and acknowledge the financial support from Directorate General 
for Higher Education, Indonesian Ministry of Education and Culture for 2014 International Collaboration 
Competitive Research Grant for International Publication of Universitas Gadjah Mada No: LPPM-
UGM/1008/LIT/2014. We would like to express our sincere gratitute to industries of Tempe, Bakpia, Fish Chips and 
Crackers. Finally, we would like to acknowledge the support for data acquisition from Arum Baashitu, Reza 
Purnama Triendy and Dwita Rahmawati. 
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Daily Worker Evaluation Model for SME-scale Food Production System Using Kansei Engineering and Artificial Neural Network 

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Daily Worker Evaluation Model for SME-scale Food Production System Using Kansei Engineering and Artificial Neural Network

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