Biplot Analysis Methods for Selecting the Consumer's Preferences of Primary Needs in Java Island Indonesia

Jajang Jajang, Supriyanto Supriyanto, Sri Maryani, Icuk Rangga Bawono, Weni Novandari, Diah Setyorini Gunawan, Rifda Naufalin

Abstract


The effect of COVID-19 pandemic in February 2020 had changingchanged human consumption pattern. Most people especially for lower and middle communitycommunities, they only be able to fulfils the primary needs. The COVID-19 pandemic had been made some companies done a work termination. Therefore, people is required to sort out and choose needs that are on a priority scale. This article used biplot methods to analyze behavior of the consumers consumer's primary needs during the COVID-19 pandemic. Respondents number of this research are 100 respondents from 4 districts in Java Island who filled out the questioner. In some references, biplot analysis methods focus on agriculture field such as determining the best genotypes and habitats of plants. Rarely of them cosider in economic point of view for example in consumers’ preferences. As we known that biplot analysis is a valuable technique for identifying environtmental condition. It is superior to other statistical methodologies because of its superior predictive accuracy. This method represent a grapics of multivariate data that plot information between the observation and variables in cartesian coordinates. Therefore, the goal of this study examines the consumers' preferences in the Java Island, Indonesia, using biplot analysis to assess preferences of primary needs such rice, cooking oil and margarine in four districts, Bekasi, Madiun, Tasikmalaya, Banyumas, in Java Island were conducted. Regarding to the result of principal component analysis, it shows that consumers have same priority to choose the brand of the cooking oil. It was shown from score of PC1 and PC2 values. The result provide helpful information about the consumer preferences of primary needs during COVID-19 from four districts in Java Island.

 

 


Keywords


Biplot analysis methods; Primary needs; Preferences; Principal component.

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References


Ansarifard, I., Mostafavi, K., Khosroshahli, M., Reza Bihamta, M., & Ramshini, H. (2020). A study on genotype–environment interaction based on GGE biplot graphical method in sunflower genotypes (Helianthus annuus L.). Food Science & Nutrition, 8(7), 3327–3334. https://doi.org/10.1002/fsn3.1610

Bagchi, B., Chatterjee, S., Ghosh, R., Dandapat, D., Bagchi, B., Chatterjee, S., Ghosh, R., & Dandapat, D. (2020). Impact of COVID-19 on global economy. Coronavirus Outbreak and the Great Lockdown: Impact on Oil Prices and Major Stock Markets Across the Globe, 15–26. https://doi.org/10.1007/978-981-15-7782-6_3

Cooper, M., & DeLacy, I. H. (1994). Relationships among analytical methods used to study genotypic variation and genotype-by-environment interaction in plant breeding multi-environment experiments. Theoretical and Applied Genetics, 88(5), 561–572. https://doi.org/10.1007/BF01240919

Cooper, M., Delacy, I. H., Byth, D. E., & Woodruff, D. R. (1993). Predicting grain yield in Australian environments using data from CIMMYT international wheat performance trials. 2. The application of classification to identify environmental relationships which exploit correlated response to selection. Field Crops Research, 32(3–4), 323–342. https://doi.org/10.1016/0378-4290(93)90040-T

Cooper, M., Stucker, R. E., DeLacy, I. H., & Harch, B. D. (1997). Wheat breeding nurseries, target environments, and indirect selection for grain yield. Crop Science, 37(4), 1168–1176. https://doi.org/10.2135/cropsci1997.0011183X003700040024x

da Silva, K. J., Teodoro, P. E., da Silva, M. J., Teodoro, L. P. R., Cardoso, M. J., Godinho, V. de P. C., Mota, J. H., Simon, G. A., Tardin, F. D., & da Silva, A. R. (2021). Identification of mega‐environments for grain sorghum in Brazil using GGE biplot methodology. Agronomy Journal, 113(4), 3019–3030. https://doi.org/10.1002/agj2.20707

Farshadfar, E., Rashidi, M., Jowkar, M. M., & Zali, H. (2013). GGE Biplot analysis of genotype× environment interaction in chickpea genotypes. European Journal of Experimental Biology, 3(1), 417–423. https://www.primescholars.com/articles/gge-biplot-analysis-of-genotype--environment-interaction-in-chickpea-genotypes.pdf

Frutos, E., Galindo, M. P., & Leiva, V. (2014). An interactive biplot implementation in R for modeling genotype-by-environment interaction. Stochastic Environmental Research and Risk Assessment, 28(7), 1629–1641. DOI https://doi.org/10.1007/s00477-013-0821-z

Gabriel, K. R. (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58(3), 453–467. https://doi.org/10.1093/biomet/58.3.453

Gallego-Álvarez, I., Galindo-Villardón, M. P., & Rodríguez-Rosa, M. (2015). Analysis of the sustainable society index worldwide: A study from the biplot perspective. Social Indicators Research, 120(1), 29–65. DOI: https://doi.org/101007/s11205-014-0579-9

Gauch Jr, H. G., & Zobel, R. W. (1997). Identifying mega‐environments and targeting genotypes. Crop Science, 37(2), 311–326. https://doi.org/10.2135/cropsci1997.0011183X003700020002x

Gedif, M., & Yigzaw, D. (2014). Genotype by environment interaction analysis for tuber yield of potato (Solanum tuberosum L.) using a GGE biplot method in Amhara region, Ethiopia. Agricultural Sciences, 2014. DOI: 10.4236/as.2014.54027

Gholizadeh, A., Dehghan, H., Amini, A., & Akbarpour, O. A. (2018). Study on trait relations of wheat genotypes using the biplot method. Iranian Journal of Field Crop Science, 49(3). 121-136. 10.22059/IJFCS.2017.224744.654246

Gholizadeh, A., & Dehghani, H. (2016). Graphic analysis of trait relations of Iranian bread wheat germplasm under non-saline and saline conditions using the biplot method. Genetika, 48(2), 473–486. DOI: 10.4236/as.2014.54027

Jolliffe, I. T. (1990). Principal component analysis: a beginner’s guide—I. Introduction and application. Weather, 45(10), 375–382. https://doi.org/10.1002/j.1477-8696.1990.tb05558.x

Kaya, Y., Akçura, M., & Taner, S. (2006). GGE-biplot analysis of multi-environment yield trials in bread wheat. Turkish Journal of Agriculture and Forestry, 30(5), 325–337. https://journals.tubitak.gov.tr/agriculture/vol30/iss5/3/

Kendal, E. (2019). Comparing durum wheat cultivars by genotype× yield× trait and genotype× trait biplot method. Chilean Journal of Agricultural Research, 79(4), 512–522. http://dx.doi.org/10.40667/S0718-58392019000400512

Kendal, E. (2020). Evaluation of some barley genotypes with geotype by yield* trait (GYT) biplot method. Poljoprivreda i Sumarstvo, 66(2), 137–150. DOI: 10.17707/AgricultForest.66.2.13

Mahmud, A. Al, Hassan, M. M., Alam, M. J., Molla, M. S. H., Ali, M. A., Mohanta, H. C., Alam, M. S., Islam, M. A., Talukder, M. A. H., & Ferdous, M. Z. (2021). Farmers’ preference, yield, and GGE-Biplot analysis-based evaluation of four sweet potato (Ipomoea batatas L.) varieties grown in multiple environments. Sustainability, 13(7), 3730. https://doi.org/10.3390/su13073730

Maia, M. C. C., Araújo, L. B. de, Dias, C. T. dos S., Oliveira, L. C. de, Vasconcelos, L. F. L., Carvalho Júnior, J. E. V. de, Simeão, M., & Bastos, Y. G. M. (2016). Selection of mango rosa genotypes in a breeding population using the multivariate-biplot method. Ciência Rural, 46(10), 1689–1694. https://doi.org/10.1590/0103-8478cr20130722

Omrani, A., Omrani, S., Khodarahmi, M., Shojaei, S. H., Illés, Á., Bojtor, C., Mousavi, S. M. N., & Nagy, J. (2022). Evaluation of grain yield stability in some selected wheat genotypes using AMMI and GGE biplot methods. Agronomy, 12(5), 1130. https://doi.org/10.33990/agronomy12051130

Oyedele, O. F. (2021). Extension of biplot methodology to multivariate regression analysis. Journal of Applied Statistics, 48(10), 1816–1832. https://doi.org/10.1080/02664763.2020.1779192

Sadabadi, M. F., Ranjbar, G. A., Zangi, M. R., Tabar, S. K., & Zarini, H. N. (2018). Analysis of stability and adaptation of cotton genotypes using GGE Biplot method. Trakia Journal of Sciences, 16(1), 51. DOI: 10.15547/tjs.2018.01.009

Torres‐Salinas, D., Robinson‐García, N., Jiménez‐Contreras, E., Herrera, F., & López‐Cózar, E. D. (2013). On the use of biplot analysis for multivariate bibliometric and scientific indicators. Journal of the American Society for Information Science and Technology, 64(7), 1468–1479. ttps://doi.org/10.1002/asi.22837

Wang, K.-L., Yao, S.-W., Liu, Y.-P., & Zhang, L.-N. (2020). A fractal variational principle for the telegraph equation with fractal derivatives. Fractals, 28(04), 2050058. https://doi.org/10.1142/S0218348X20500589

XU, N., Fok, M., Zhang, G.-W., Jian, L. I., & ZHOU, Z. (2014). The application of GGE biplot analysis for evaluat ng test locations and mega-environment investigation of cotton regional trials. Journal of Integrative Agriculture, 13(9), 1921–1933. https://doi.org/10.1016/S2095-3119(13)60656-5

Yan, W. (2013). Biplot analysis of incomplete two‐way data. Crop Science, 53(1), 48–57.https://doi.org/10.2135/cropsci2012.05.0301

Yan, W. (2019). LG biplot: a graphical method for mega-environment investigation using existing crop variety trial data. Scientific Reports, 9(1), 7130. DOI: https://doi.org/10.1038/s41598-019-43683-9

Yan, W., Cornelius, P. L., Crossa, J., & Hunt, L. A. (2001). Two types of GGE biplots for analyzing multi‐environment trial data. Crop Science, 41(3), 656–663. https://doi.org/10.2135/cropsci2001.413656x

Yan, W., & Hunt, L. A. (2001). Interpretation of genotype× environment interaction for winter wheat yield in Ontario. Crop Science, 41(1), 19–25. https://doi.org/10.2135/cropsci2001.41119x

Yan, W., Hunt, L. A., Sheng, Q., & Szlavnics, Z. (2000). Cultivar evaluation and mega‐environment investigation based on the GGE biplot. Crop Science, 40(3), 597–605. https://doi.org/10.2135/cropsci2000.403597x

Yan, W., & Rajcan, I. (2002). Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Science, 42(1), 11–20. https://doi.org/10.2135/cropsci2002.1100




DOI: https://doi.org/10.31764/jtam.v8i3.22264

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