An Intelligent Incentive Model Based on Environmental Ergonomics for Food SMEs


  • Mirwan Ushada Department of Agro-industrial Technology, Faculty of Agricultural Technology, Universitas Gadjah Mada, Jalan Flora No. 1 Bulaksumur Yogyakarta 55281,
  • Nur Achmad Sulistyo Putro Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Sekip Utara Bulaksumur Yogyakarta 55281
  • Nafis Khuriyati Department of Agro-industrial Technology, Faculty of Agricultural Technology, Universitas Gadjah Mada, Jalan Flora No. 1 Bulaksumur Yogyakarta 55281,



fuzzy inference, genetic algorithm, heart rate, incentive index, workstation temperature


In this study, an intelligent incentive model based on environmental ergonomics in food small and medium-sized enterprises (SMEs) was developed. Environmental ergonomics was defined as the impact of temperature and relative humidity within a certain range on a worker's heart rate during work. Optimum environmental ergonomics are highly required as a basic standard for food SMEs to provide fair incentives. Recommendable parameters from a genetic algorithm and fuzzy inference modeling were used to model customized incentives based on optimum heart rate, workplace temperature and relative humidity before and after working. The research hypothesis stated that industries should optimize their workload and workstation environment prior to customizing incentives. The research objectives were: 1) to recommend optimum environmental ergonomics parameters for customized incentives; 2) to determine the incentives at workstations of SMEs based on optimum environmental ergonomics parameters and fuzzy inference modeling. The optimum values for heart rate, workstation temperature and relative humidity used were based on recommendable values from the genetic algorithm. An inference model was developed to generate decisions whether a worker should receive an incentive based on a calculated index. The results indicated that 84.4% of workers should receive an incentive. The results of this research could be used to promote the concept of ergonomics-based customized incentives.


Download data is not yet available.


Hakenes, H. & Katolnik, S., On the Incentive Effects of Job Rotation, European Econ., Rev. 98, pp. 424-441, 2017.

Parsons, K.C., Environmental Ergonomics: A Review of Principles, Methods and Models, App. Ergon.,31(2000), pp. 581-594, 2000.

Angelova, V., Giebe, T. & Ivanova-Stenzel, R., Does a Short-term Increase Boost Performance, Econ. Letters, 166, pp. 31-34, 2018.

Prasad, S. & Tran, H., Work Practices, Incentives for Skills and Training. Labour Econ., 23, pp. 66-76, 2013.

Zou, J. & Lei, Y., Production System Performance Identification Using Sensor Data, IEEE Trans. Syst., Man, and Cybern. Syst., 48(2), pp. 255-264, 2018.

Gong, D.W., Yuan, J. & Sun, X-Y., Interactive Genetic Algorithms with Individual's Fuzzy Fitness, Comp. Hum. Behav., 27, pp. 1482-1492, 2011.

Kolomvatsos, K., Anagnostopoulos, C. & Hadjiefthymiades, S., Data Fusion and Type-2 Fuzzy Inference in Contextual Data Stream Monitoring, IEEE Trans. Syst., Man, and Cybern. Syst., 47(8), pp. 1839-1853, 2017.

Zhou, J., Wang, Q., Tsai, S.B, Xue, Y. & Dong, W., How to Evaluate the Job Satisfaction of Development Personnel, IEEE Trans. Syst., Man, and Cybern. Syst., 47(11), pp. 2809-2816, 2017.

Hsu, H.P., A Fuzzy Knowledge-Based Disassembly Process Planning System Based on Fuzzy Attributed and Timed Predicate/Transition Net. IEEE Trans. Syst., Man, and Cybern. Syst., 47(8), pp. 1800-1813, 2017.

Garcia-Nunes, P.I., Souza, R.M. & da Silva, A.EA., Mental Models Analysis and Comparison Based on Fuzzy Rules: A Case Study of The Protest of June and July 2013. IEEE Trans. Syst., Man, and Cybern. Syst., 47(8), pp. 2021-2033, 2017.

Chen, C., Parthal in, N.M., Li, Y., Price, C. & Quek, C., Rough-Fuzzy Rule Interpolation, Inf. Sci., 351, pp. 1-17, 2016.

Cheshmehgaz, H.R., Haron, H., Kazemipour, F. & Desa, M.I., Accumulated Risk of Body Postures in Assembly Line Balancing Problem and Modeling Through a Multi-Criteria Fuzzy-Genetic Algorithm, Comp. & Ind. Eng., 63, pp. 503-512, 2012.

Tsuchiya, T., Maeda, T., Matsubara, Y. & Nagamachi, M., A Fuzzy Rule Induction Method Using Genetic Algorithm, Int. J. of Ind. Ergon., 18, pp. 135-145, 2017.

Ushada, M., Aji, G.K., Okayama, T. & Khidir, M., SME Worker Affective (SWA) Index Based on Environmental Ergonomics, Conference Proceeding of the International Conference on Industrial and System Engineering (ICONISE), August 29th - 31st, 2017.

Ushada, M., Okayama, T., Khuriyati, N. & Suyantohadi, A., Affective Temperature Control in Food SMEs using Artificial Neural Network. App. Art. Intell., 31(7-8), pp. 555-567, 2017.

Ushada, M., Okayama, T. & Murase, H., Development of Kansei Engineering-Based Watchdog Model to Assess Worker Capacity in Indonesian Small-Medium Food Industry. Eng. in Agric., Environ. and Food., 8(4), pp. 241-250, 2015.

Ushada, M., Mustika, H.F., Musdholifah, A. & Okayama, T., An Optimization Model for Environmental Ergonomics Assessment in Bioproduction of Food SMEs, HAYATI J Biosci. Status: Under Review.

Guillaume, S. & Charnomordic, B., Learning Interpretable Fuzzy Inference Systems with Fispro, Inf. Sci., 181(2011), pp. 4409-4427, 2011.

Guillaume, S. & Charnomordic, B., Fuzzy Inference Systems: An Integrated Modeling Environment for Collaboration between Expert Knowledge and Data Using Fispro, Expert Syst. App. , 39(2012), pp. 8744-8755, 2012.

American Industrial Hygiene Association (AIHA), Ergonomic Guide to Assessment of Metabolic and Cardiac Costs of Physical Work (1971) in Kolus, A., Imbeau, D., Dube, V. & Dubeau, D. Classifying Work Rate from Heart Rate Measurements Using an Adaptive Neuro-Fuzzy Inference System, App. Ergon., 54(2016), pp. 58-168, 2016.

Kolus., A., Imbeau, D. Dube, P-A. & Dubeau, D., Classifying Work Rate from Heart Rate Measurements Using an Adaptive Neuro-Fuzzy Inference System. App. Ergon., 54(2016), pp. 158-168, 2016.

Ministry of Energy and Mineral Resources of Republic Indonesia, Regulation of Ministry of Energy and Mineral Resources of Republic Indonesia No.13: Electricity Saving, 2013. (Text in Indonesian)

National Standar of Indonesia (SNI) No. 16-7063-2004: Threshold Values of Workplace Environment, Noise Level, Vibration of Hand-upper Arm and Ultraviolet Radiation Indonesia, 2003. (Text in Indonesian)

Zomorodian, Z.S., Tahsildoost, M. & Hafezi, M., Thermal Comfort in Educational Buildings:A Review Article, Renewable and Sustainable Energy Reviews, 59, pp. 895-906, 2016.

Ministry of Health Republic Indonesia, Regulation of Ministry of Health Republic Indonesia No. 1077: Healty Indoor Air at Home, 2011. (Text in Indonesian)

Ushada, M., Mulyati, G.T., Guritno, A.D. & Murase, H., Combining Drum-Buffer-Rope Algorithm and Kansei Engineering to Control Capacity Constrained Worker in a Bioproduction System, IFAC Proceedings 2014, 46(4), pp. 384-389, 2014.

Dam, K., Job Assignment, Market Power and Managerial Incentives, The Quarterly Review of Economics and Finance, Quart. Rev. Eco. Finance, 57, pp. 222-233, 2015.

Jaworski, C., Ravichandran, S., Karpinski, A.C. & Singh, S., The Effects of Training Satisfaction, Employee Benefits, and Incentives on Part-Time Employees Commitment, Int. J. of Hosp. Manag., 74, pp. 1-12, 2018.

Ushada, M., Digital Ergonomics Study to Support Work System of Food Agroindustry, J. Teknologi dan Industri Pertanian Indonesia, 11(1), pp. 6-8, 2019. (Text in Indonesian)

Agassi, T.A., Ushada, M. & Suyantohadi, A., User Needs Analysis for Industrial Design of Kansei Engineering-Based Sensor for Agroindustry (KESAN), International Conference of Science and Technology, pp. 7-8 Agustus 2018, Universitas Gadjah Mada, IEEE Xplore, 2019.

Ushada, M., Suyantohadi, A., Khuriyati, N. & Putro, N.A.S., Ergonomic Assesment System for Food Production Process, Patent Pending (Patent Application Indonesia No: P00201804325 on 8 Juni, 2018), 2018. (Text in Indonesian)

Ushada, M., Suyantohadi, A., Khuriyati, N. & Okayama, T., Identification of Environmental Ergonomics Control System for Indonesian SMEs, 2017 The 3rd International Conference on Control, Automation and Robotics (ICCAR 2017), IEEE Xplore, 2017.

Itri, J.N., Bruno, M.A., Lalwani, N., Munden, R.F., Tappouni, R., The Incentive Dilemma: Intrinsic Motivation and Workplace Performance, J. Am. Coll. Radiol. 16(1), pp. 39-44, 2019.




How to Cite

Ushada, M., Putro, N. A. S., & Khuriyati, N. (2019). An Intelligent Incentive Model Based on Environmental Ergonomics for Food SMEs. Journal of Engineering and Technological Sciences, 51(6), 839-854.