Nafis Khuriyati

Kansei’s Physiological Measurement in Small-Medium Sized Enterprises Using Profile of Mood States and Heart Rate

Kansei’s physiological measurement were pursued in 4 (four) production systems of small-medium sized enterprises in in Special Region of Yogyakarta (DIY), Indonesia. These SMEs produces indigenous food product of Bakpia, Cracker, Fish chips and Tempe. Profile of Mood States (POMS) was used as the verbal parameter to measure Total Mood Disturbance (TMD). Heart rate was used as the non-verbal parameter. The measurement was pursued in daily check-in before working and check-out after working. The research results indicated TMD and heart rate are sensible to measure physiological response to workplace environmental parameters. Workplace environment has greater impact to the sensibility of worker mood and heart rate in Bakpia and Tempe’s SMEs, while it has less impact in Cracker and Fish Chips’s SMEs.

Kansei engineering-based artificial neural network model to evaluate worker performance in small-medium scale food production system

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.

Identification of environmental ergonomics control system for Indonesian SMEs

This paper identified the environmental ergonomics control system for Indonesian SMEs (Small Medium-sized Enterprises). The system was defined that workstation environment could be controlled using worker workload and workstation temperature difference. The research objectives were: 1) To analyze the relationship between workstation temperature difference and workload; 2) To identify the environmental ergonomics control system for Indonesian SMEs. 380 data set of heart rate, indoor temperature and temperature set points were collected from Indonesian SMEs. Workloads were classified based on heart rate. Temperature difference were determined using set point temperature (Before working) and indoor temperature (After working). Temperature difference were categorized to 14 quadrants. Actors of system were identified. Research result indicated that temperature difference generated various workload. System identification was expected to support further development of environmental ergonomics control.

Quality Improvement of Product–Service Package of “Mangunan” Agrotourism Using Quality Function Deployment Approach

Mangunan is one of growing agrotourism in the district of Bantul Yogyakarta. Basically, Mangunan had many criteria to support the implementation of product–service package sustainability agrotourism that included product, service, and others. Product–service package is a correlation between the products and services offered simultaneously. Mangunan has a variety of recreational and educative facilities and is supported by a natural landscape as a special attraction. Although it has a wide range of potential, Mangunan has not been able to attract tourists to visit. The study aimed to formulate technical requirements for improving quality of “Mangunan” agrotourism using Quality Function Deployment approach.

Affective Temperature Control in Food SMEs using Artificial Neural Network

ABSTRACT This paper highlights modeling affective temperature control in food small and medium-sized enterprises (SMEs). Modeling defined that workstation temperature set point could be controlled based on worker heart rate and workstation environment using Artificial Neural Network (ANN). The research objectives were: 1) to propose modeling affective temperature control in food SMEs based on heart rate and workstation environment; and 2) to develop an ANN model for predicting workstation temperature set point. Training and validation data were collected from six food SMEs in Yogyakarta Special Region, Indonesia. The data of temperature set points were verified using a simulated confined room. The inputs of the ANN model were worker heart rate, workstation temperature, relative humidity distribution and light intensity. The output was temperature set point. Research results concluded satisfactory performance of ANN. The model could be used to provide environmental ergonomics in food SMEs.