It is assumed that the social demographic characteristics of consumers in Palembang City that affect consumer preferences include age, gender, number of family members, education level, occupation, and income level.

Palembang City as the capital city of South Sumatra Province is located between 2⁰ 5' and 3⁰ 5' South latitude and between 104⁰ 37' and 104⁰ 52' East longitude. The area of Palembang City is 40,061 hectares or about 2.65 percent of the total land area of South Sumatra Province. In 2000 there were 14 sub-districts and 103 sub-districts and in 2017 there were 4 additional sub-districts, bringing the total to 18 sub-districts and 107 sub-districts.

This study was undertaken in the city of Palembang, South Sumatra Province, Indonesia (Figure 1). The districts selected were Sako District, Ilir Timur II District, and Kalidoni District. Sako District represents low-income consumers, Ilir Timur II District represents upper-middleincome consumers and Kalidoni District represents highincome consumers. The number of samples in this study was 329 respondents. This city was selected for: (1) residence of households consuming rice with quality reference, (2) place of various types of rice retailers from traditional to modern sellers. Data collection was conducted in May – September 2019. The distribution of the sample is presented in Table1. Figure 2 was given to show the distribution of respondent household income.

The distribution of the demographic profile of respondents is shown in Table 2. Social demographic characteristics of the respondents such as age, gender, education, income, and occupation were hypothesized to positively or negatively influence consumer preferences. The total sample comprises 217 males and 112 females. The age of respondents was grouped into 3 categories; 35 years or below comprises 45.3%, 36 to 49 years old 32.2%, and 50 years or more 22.5%. Respondent's educational background was grouped into 3, elementary to junior high school 7.6%, senior high school 18.9%, and university 73.5%. Respondent's household income was grouped into 3, high income 32.2%, middle income 43.2%, and low income 24.6% (Figure 2).

Multinomial logistic regression analysis is a logistic regression that is used when the dependent variable has a multinomial scale. Logistic regression analysis includes independent test, simultaneous testing, partial testing, model suitability, model goodness, and classification accuracy (Table3).

The response variable in this study is consumer preferences based on the attributes which consist of seven groups, namely group 0 (small broken, chalky grains, broken grains, varieties), group 1 (family reference, friend reference, supplier, advertisement), group 2 (foreign object, residue), group 3 (packaging, brand), group 4 (volume expansion, head rice, flavor, aroma), group 5 (soft texture, durability) and group 6 (whiteness). Simultaneous testing is used to find out a predictor variable that has a significant effect on consumer preferences.

Table 5 shows that the G value of 802.378 is greater than the value 2 tables which are 79.386 and a p-value (0.10) of 0.000 which is smaller than 0.1, which means reject H₀, so it can be concluded that with a confidence level of 90% there is at least one predictor variable (social demographic factors) that has a significant effect on consumer preferences on rice attributes. Simultaneous testing of the consumer preference attribute group in the city of Palembang resulted in a decision to reject H₀ so that it could be continued on a partial test.

Model feasibility testing is carried out to determine whether there is a difference in observation results and predictions. The model is said to be able to predict the value of the observation because it matches the observation data if the value is sig. Chi-square >0.10. The value of goodness of fit test in Table 6 is measured by the Chi-square value in the coefficient of deviation. In the table, it can be seen that the value of the statistical significance of Chi-square is 0.216 which is above 0.10.

Table 6 shows that the Pearsons value obtained was 1,274.793 smaller than the value χ2 table 1,300.131 and the p-value of 0.216 are greater than 0.10 (p-value >α; (0.216 >0.10), so it can be concluded that with a confidence level of 90% the resulting regression model is fit for further analysis, namely Pseudo R² and Classification Test.

Pseudo R² values were measured using the Nagelkerke R Square (Agresti, 2011). Nagelkerke R Square is a modification of the Cox and Snell's coefficients to ensure that the value varies from 0 to 1. This is done by dividing Cox and Snell's R² values by their maximum values. The Nagelkerke R² value can be interpreted as the R² value for multiple regression. The results of the Nagelkerke value can be seen in Table 7.

The Nagelkerke R Square value of 0.224 indicates that the variability of the dependent variable which can be explained by the variability of the independent variable is 22.4%, while the remaining 77.6% is explained by other variables not used in this study.

Model feasibility can also be predicted using a classification matrix that calculates the correct and incorrect estimation values on the dependent variable. The classification matrix shows the predictive power of the regression model. The classification accuracy obtained by the model can be seen in Table 8.

Table 8 shows the classification accuracy of the model, which is 49.2%, which means the model's ability to predict accurately according to observations (real conditions) is 49.2%, while the resulting classification error is 50.8%.

To determine the significance of the influence of the predictor variables on individual consumer preferences, a parameter test was carried out individually using the Wald Test. The test results using the attribute group of small broken, chalky grains, higher broken and varieties as a comparison category for parameter estimates between the attribute groups of rice with age, sex, type of work, and income level can be seen in Table 9.

Table 9 shows that the variables that have a significant effect on consumer preferences are consumer age, gender, occupation and income level, which can be expressed in the six multinomial logistic regression functions as follows:

g1 (X) = 2.545 + 0.778x2 (0) - 0.138x3 - 1.786x5 (0) - 0.006x5 (1) - 0.190x6 g2 (X) = 3.142 - 0.543x2 (0) - 0.094x3 - 2.826x5 (0) - 0.043x5 (1) - 0.021x6 g3 (X) = 2.128 + 0.029x2 (0) - 0.063x3 - 2.361x5 (0) - 0.183x5 (1) - 0.068x6 g4 (X) = 2.487 + 0.437x2 (0) - 0.229x3 + 0.437x5 (0) + 0.919x5 (1) - 0.096x6 g5 (X) = 3.203 + 0.795x2 (0) - 0.067x3 - 0.613x5 (0) + 0.568x5 (1) - 0.128x6 g6 (X) = 0.364 + 0.389x2 (0) + 0.248x3 - 2.464x5 (0) + 0.096x5 (1) - 0.127x6

The first logit covers attributes of family references, friend references, suppliers, and advertisements. Explanatory variables that have a significant effect on the decision choosing this attribute is the level of income with a p-value of 0.007, while gender, number of family members, and type of work do not have a significant effect with a p-value >0.10 at the significance level of α = 10%. In the logit equation 1, the coefficient of the income variable is -0.190 with an odds ratio of 0.827 and the Wald test is significant at the 10% level. This shows if the variables of gender, family member, type of work are constant, then every IDR 1 million increase in terms of income level, the opportunity to choose attributes of family references, friends references, suppliers, and advertisements compared to choosing attributes of whole grains, broken items, groats, and shapes is 0.827. This means that the higher the income of consumers, the more likely it is to prefer the attributes of small broken, chalky grains, higher broken, varieties compared to attributes of family references, friend references, suppliers, and advertisements.

The analysis result that household characteristics that influence consumers' preference for rice attributes were household income and the type of occupation of the household head. This finding is in line with studies (Wahyudi et al., 2019) which found that consumers’ incomes lead to increased demand for quality.

Preferences for rice attributes are found influenced by gender, education levels, household size and income, rice consumption, expenditure share, and purchase frequency (Anang, Adjei Adjetey and Abiriwe, 2011). Increased Consumers’ personal food choices are changing due to a greater variety of food products higher incomes, and better choices of food retailers (Brečić, Mesić and Cerjak, 2017).

Consumers with high household income choosing attributes of whole grains, broken items, groats, and shapes. The physical appearance of the rice is an attraction for consumers to buy. Another support of preference consumers is presented by (Tomlins et al., 2007). Visual characteristics of rice grains are important to search attributes that affect consumers’ purchasing decisions and hence are used as some of the first selection criteria in varietal improvement programs. The appearance of a product can influence consumer choice in different ways. A product’s appearance can have aesthetic and symbolic value for consumers, communicate functional characteristics and give a quality impression (functional value), and communicate the ease of use (ergonomic value) (Creusen and Schoormans, 2005).

High-income consumers have the largest variability in rice grain quality attributes and concurrently appear to have the most pronounced preferences among consumers (Cuevas et al., 2016). Quality attributes of rice are different in the other countries. Guatemala consumers were willing to pay premiums for those desirable qualities.

Specific attributes such are taste, cooking quality, cooking time, and aroma were the quality characteristics that most consumers preferred (Anang, Adjei Adjetey and Abiriwe, 2011). In New Zealand, the top three attributes supported for community, freshness, and seasonality (Hiroki, Garnevska and McLaren, 2016). In Sri Lanka, attributes of rice like rice production, processing, marketing, and value addition (Walisinghe and Gunaratne, 2012).

Food acceptability, choice, and consumption are complex processes influenced by many factors as intrinsic, e.g. color, aroma, flavor, and texture, as well as extrinsic to the product. The extrinsic factors have been included in several types of research aiming at having a better understanding of consumer behavior (Iop, Teixeira and Deliza, 2006). Consumers' tastes are known as factors that affect product demand such as rice. Branded packaged rice produced by local agro-industries is expected to meet the rice multi-attribute (Widayanti et al., 2020).

Family members and age are not significantly influencing consumers' preference for rice attributes in this analysis. A large number of families means a lot of rice is consumed so that the attribute factor is ignored. At this point, the price factor becomes the main determining factor considered by consumers. It’s inline with finding studies (Supriana and Pane, 2018) that characteristics of rice consumers have a positive and significant effect on the amount of rice consumed are age and the number of family members. In other studies (Wahyudi et al., 2019) which analyzed sociodemographic factors that economic and demographic changes such as education, urbanization rates, and female labor force participation rates, along with current transportation and communications advances, influence consumer preferences