DOI : https://doi.org/10.18517/ijaseit.14.4.19314

Modeling Consumer Overall Acceptance for Traditional Spice-Based Ready-to-Drink Using Artificial Neural Network and Kansei Engineering

Ririn Nur Alfiani (1), Mirwan Ushada (2), Makhmudun Ainuri (3), Mohammad Affan Fajar Falah (4)
(1) Department of Agro-industrial Technology, Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia
(2) Department of Agro-industrial Technology, Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia
(3) Department of Agro-industrial Technology, Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia
(4) Department of Agro-industrial Technology, Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia
Fulltext View | Download
How to cite (IJASEIT) :
Alfiani, Ririn Nur, et al. “Modeling Consumer Overall Acceptance for Traditional Spice-Based Ready-to-Drink Using Artificial Neural Network and Kansei Engineering”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 4, Aug. 2024, pp. 1178-84, doi:10.18517/ijaseit.14.4.19314.
Citation Format :
Measuring consumer acceptance of food products is challenging, primarily because the process is susceptible to bias. Several studies have reported that this challenge can be addressed through Kansei engineering through verbal and nonverbal response measurements. Therefore, this study aimed to predict consumer overall acceptance using artificial neural networks (ANN) and Kansei engineering. A total of 30 respondents participated in this study to test nine different samples of traditional spice-based ready-to-drink (RTD). The overall acceptance score and Kansei responses, including verbal and nonverbal, were then measured. Each sample was served cold in a 60-mL cup labeled with a three-digit random code, and the panelists were successfully presented with the nine drinks. All participants were asked to rank each Kansei word scale based on the intensity of their feelings during the assessment. The heart rate (HR) and skin temperature (ST) were also measured as nonverbal responses in real time. The results showed that Kansei's responses and respondent background best predicted overall acceptance. The optimal model architecture had ten input neurons, two hidden neurons, and one output neuron (10-2-1). The training, validation, and testing data showed that the performance of ANN was satisfactory, with a low error rate (RMSE) and a high coefficient of correlation value (R2). Based on the findings, the developed model could inspire and motivate further studies and development in industries to develop appropriate products for potential consumers, thereby revolutionizing the food industry.

R. N. Alfiani, M. Ushada, M. Ainuri, and M. A. F. Falah, “Extracting Consumers’ Perceptions for Indonesian Spice Drinks Using Social Media Data Mining and Kansei Engineering,” Agraris, vol. 9, no. 2, pp. 195–218, 2023, doi: 10.18196/agraris.v9i2.78.

M. S. Rajput, N. P. Nirmal, S. J. Nirmal, and C. Santivarangkna, “Bio-actives from Caesalpinia sappan Linn: Recent advancements in phytochemistry and pharmacology,” South African Journal of Botany. Elsevier B.V., 2021. doi: 10.1016/j.sajb.2021.11.021.

R. N. Alfiani, M. Ushada, M. Ainuri, and M. Affan Fajar Falah, “Application of taguchi method in production factors of traditional spice-based ready-to-drink based on sensory responses,” E3S Web Conf., vol. 444, 2023, doi: 10.1051/e3sconf/202344404014.

G. Soltani-Fesaghandis and A. Pooya, “Design of an artificial intelligence system for predicting success of new product development and selecting proper market-product strategy in the food industry,” Int. Food Agribus. Manag. Rev., vol. 21, no. 7, pp. 847–864, 2018, doi: 10.22434/IFAMR2017.0033.

R. P. F. Guiné, S. G. Florença, M. J. Barroca, and O. Anjos, “The link between the consumer and the innovations in food product development,” Foods, vol. 9, no. 9, pp. 3–5, 2020, doi:10.3390/foods9091317.

C. Ruiz-Capillas, A. M. Herrero, T. Pintado, and G. Delgado-Pando, “Sensory analysis and consumer research in new meat products development,” Foods, vol. 10, no. 2, pp. 1–15, 2021, doi:10.3390/foods10020429.

N. Córdoba, F. L. Moreno, C. Osorio, S. Velásquez, and Y. Ruiz, “Chemical and sensory evaluation of cold brew coffees using different roasting profiles and brewing methods,” Food Res. Int., vol. 141, no. January 2021, 2021, doi: 10.1016/j.foodres.2021.110141.

K. Koyama, M. Tanaka, B. H. Cho, Y. Yoshikawa, and S. Koseki, “Predicting sensory evaluation of spinach freshness using machine learning model and digital images,” PLoS One, vol. 16, no. 3 March, pp. 1–16, 2021, doi: 10.1371/journal.pone.0248769.

J. Jin, D. Jia, and K. Chen, “Mining online reviews with a Kansei-integrated Kano model for innovative product design,” Int. J. Prod. Res., vol. 0, no. 0, pp. 1–20, 2021, doi:10.1080/00207543.2021.1949641.

S. Schütte and L. Marco-almagro, “Linking the Kansei Food Model to the General Affective Engineering Model,” Int. J. Affect. Eng., vol. 21, no. 3, pp. 219–227, 2022, doi: 10.5057/ijae.ijae-d-21-00024.

M. Ushada, T. Wijayanto, F. Trapsilawati, and T. Okayama, “Modeling SMEs’ trust in the implementation of industry 4.0 using kansei engineering and artificial neural network: Food and beverage SMEs context,” J. Eng. Technol. Sci., vol. 53, no. 2, 2021, doi:10.5614/j.eng.technol.sci.2021.53.2.3.

V. P. R. Septiani, M. Ushada, and Suharno, “Development of Sago-based Analog Rice Using Kansei and Value Engineering,” Pertanika J. Sci. Technol., vol. 31, no. 6, pp. 2947–2960, 2023, doi:10.47836/pjst.31.6.17.

Ó. López, C. Murillo, and A. González, “Systematic Literature Reviews in Kansei Engineering for Product Design—A Comparative Study from 1995 to 2020,” Sensors, vol. 21, no. 6532, pp. 1–19, 2021, doi: 10.3390/s21196532.

M. Ushada, F. Trapsilawati, G. K. Aji, R. Amalia, and L. Setyowati, “Multiple Affective Attributes for the Customization of Post-Pandemic Food Services,” J. Qual. Assur. Hosp. Tour., vol. 00, no. 00, pp. 1–24, 2023, doi: 10.1080/1528008X.2023.2224126.

M. Ushada, R. Amalia, F. Trapsilawati, and N. A. S. Putro, “Group preference decision-making for the implementation of Industry 4.0 in food and beverage SMEs,” Technol. Anal. Strateg. Manag., vol. 0, no. 0, pp. 1–18, 2022, doi: 10.1080/09537325.2022.2117600.

C. Gonzalez Viejo, D. D. Torrico, F. R. Dunshea, and S. Fuentes, “Development of artificial neural network models to assess beer acceptability based on sensory properties using a robotic pourer: A comparative model approach to achieve an artificial intelligence system,” Beverages, vol. 5, no. 2, 2019, doi:10.3390/beverages5020033.

J. J. Merrigan, J. H. Stovall, J. D. Stone, M. Stephenson, V. S. Finomore, and J. A. Hagen, “Validation of Garmin and Polar Devices for Continuous Heart Rate Monitoring During Common Training Movements in Tactical Populations,” Meas. Phys. Educ. Exerc. Sci., pp. 1–14, Dec. 2022, doi: 10.1080/1091367X.2022.2161820.

A. Kistler, C. Mariauzouls, and K. Von Berlepsch, “Fingertip temperature as an indicator for sympathetic responses,” Int. J. Psychophysiol., vol. 29, no. 1, pp. 35–41, 1998, doi: 10.1016/S0167-8760(97)00087-1.

S. S. Samant and H.-S. Seo, “Influences of sensory attribute intensity, emotional responses, and non-sensory factors on purchase intent toward mixed-vegetable juice products under informed tasting condition,” Food Res. Int., vol. 132, 2020, doi:10.1016/j.foodres.2020.109095.

S. S. Samant and H.-S. Seo, “Using both emotional responses and sensory attribute intensities to predict consumer liking and preference toward vegetable juice products,” Food Qual. Prefer., vol. 73, no. December 2018, pp. 75–85, 2019, doi:10.1016/j.foodqual.2018.12.006.

B. D. Handari, D. Wulandari, N. A. Aquita, S. Leandra, D. Sarwinda, and G. F. Hartono, “Comparing Restricted Boltzmann Machine – Backpropagation Neural Networks, Artificial Neural Network – Genetic Algorithm and Artificial Neural Network – Particle Swarm Optimization for Predicting DHF Cases in DKI Jakarta,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 12, no. 6, pp. 2476–2484, 2022, doi:10.18517/ijaseit.12.6.16226.

A. Yaqin, A. Dwi, and S. Fatonah, “Evaluation of backpropagation neural network models for early prediction of student ’ s graduation in XYZ University,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 11, no. 2, pp. 610–617, 2021, doi: 10.18517/ijaseit.11.2.11152.

R. Amalia, M. Ushada, and A. P. Pamungkas, “Development of Artificial Neural Networks Model to Determine Labor Rest Period Based on Environmental Ergonomics,” Int. J. Technol., vol. 14, no. 5, pp. 1019–1028, 2023, doi: 10.14716/ijtech.v14i5.3854.

U. M. R. Paturi, S. Cheruku, and S. R. Geereddy, “Process modeling and parameter optimization of surface coatings using artificial neural networks (ANNs): State-of-the-art review,” Mater. Today Proc., vol. 38, pp. 2764–2774, 2021, doi: 10.1016/j.matpr.2020.08.695.

M. Fauziyah, S. Adhisuwignjo, B. F. Afandi, and A. D. Risdhayanti, “A Novel Design of Error Backpropagation Algorithm for Ingredient Mixing Process Tamarind Turmeric Herb,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 13, no. 3, p. 1196, 2023, doi:10.18517/ijaseit.13.3.17237.

R. Suneth, H. Sukoco, and S. N. Neyman, “Botnet Detection Model in Encrypted Traffics Software-Defined Network (SDN) Using Deep Neural Network (DNN),” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 13, no. 2, pp. 744–750, 2023, doi: 10.18517/ijaseit.13.2.9370.

R. A. de Wijk, V. Kooijman, R. H. G. Verhoeven, N. T. E. Holthuysen, and C. de Graaf, “Autonomic nervous system responses on and facial expressions to the sight, smell, and taste of liked and disliked foods,” Food Qual. Prefer., vol. 26, no. 2, pp. 196–203, 2012, doi:10.1016/j.foodqual.2012.04.015.

J. Jo and Y. chan Yoon, “Remote Heart Rate Estimation Using Attention-targeted Self-Supervised Learning Methods,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 13, no. 3, pp. 842–847, 2023, doi:10.18517/ijaseit.13.3.17342.

R. A. De Wijk, W. He, M. G. J. Mensink, R. H. G. Verhoeven, and C. De Graaf, “ANS responses and facial expressions differentiate between the taste of commercial breakfast drinks,” PLoS One, vol. 9, no. 4, pp. 1–9, 2014, doi: 10.1371/journal.pone.0093823.

Z. Kalinić, V. Marinković, L. Kalinić, and F. Liébana-Cabanillas, “Neural network modeling of consumer satisfaction in mobile commerce: An empirical analysis,” Expert Syst. Appl., vol. 175, pp. 0–3, 2021, doi: 10.1016/j.eswa.2021.114803.

A. M. Ponsiglione, G. Cesarelli, F. Amato, and M. Romano, “Optimization of an artificial neural network to study accelerations of foetal heart rhythm,” 6th Int. Forum Res. Technol. Soc. Ind. RTSI 2021 - Proc., pp. 159–164, 2021, doi: 10.1109/RTSI50628.2021.9597213.

O. B. Öztürk and E. Başar, “Multiple linear regression analysis and artificial neural networks based decision support system for energy efficiency in shipping,” Ocean Eng., vol. 243, no. October 2021, 2022, doi: 10.1016/j.oceaneng.2021.110209.

S. Ghanta, C. Bhaumik, and M. S. Manna, “Process development for isolation of dietary eugenol from leaves of basil (Ocimum sanctum) in combination of optimization of process variables and modeling by artificial neural network,” J. Indian Chem. Soc., vol. 99, no. 1, p. 100280, 2022, doi: 10.1016/j.jics.2021.100280.

A. Falkeisen, M. Gorman, S. Knowles, S. Barker, R. Moss, and M. B. McSweeney, “Consumer perception and emotional responses to plant-based cheeses,” Food Res. Int., vol. 158, 2022, doi:10.1016/j.foodres.2022.111513.

M. Ushada, F. Trapsilawati, R. Amalia, and N. A. S. Putro, “Modeling Trust Decision-Making of Indonesian Food and Beverage SME Groups in the Adoption of Industry 4.0,” Cybern. Syst., vol. 0, no. 0, pp. 1–17, 2022, doi: 10.1080/01969722.2022.2122011.

I. Kovacs and E. R. Keresztes, “Perceived Consumer Effectiveness and Willingness to Pay for Credence Product Attributes of Sustainable Foods,” Sustain., vol. 14, no. 7, pp. 1–17, 2022, doi:10.3390/su14074338.

E. Wallace, P. Torres, M. Augusto, and M. Stefuryn, “Do brand relationships on social media motivate young consumers’ value co-creation and willingness to pay? The role of brand love,” J. Prod. Brand Manag., vol. 31, no. 2, pp. 189–205, 2022, doi: 10.1108/JPBM-06-2020-2937.

X. Li, J. Su, Z. Zhang, and R. Bai, “Product innovation concept generation based on deep learning and Kansei engineering,” J. Eng. Des., vol. 32, no. 10, pp. 559–589, 2021, doi:10.1080/09544828.2021.1928023.

A. Sayekti, A. Suyantohadi, M. Ushada, and D. Yudianto, “Design of soymilk product development from grobogan soybean variety in Indonesia,” Pertanika J. Sci. Technol., vol. 29, no. 3, pp. 1683–1705, 2021, doi: 10.47836/pjst.29.3.46.

S. N. A. Abd Rashid et al., “Formulation and characterization of the physicochemical, antioxidant activity and sensory attributes of curcuma-based herbal drink,” Mater. Today Proc., vol. 57, pp. 1061–1066, 2022, doi: 10.1016/j.matpr.2021.09.272.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).