Corresponding Author: Maulidia
Tri Yuliani
E-mail: [email protected]
INTRODUCTION
Indonesia as a country with a large
population and a very large area, food security is an important agenda in
economic development. Food insecurity is a very sensitive issue in the dynamics
of social life, therefore Indonesia needs to be able to realize national,
regional, household and individual food security based on self-sufficiency in
domestic food supply ("Analysis of Community Level Food Consumption
Supports the Achievement of Food Diversification, � 2010) Food
is the most basic human need, so the availability of food for the community
must always be guaranteed. In the development of community civilization to meet
the quality of life that is advanced, independent, in a peaceful atmosphere,
and physically and mentally prosperous, food consumption is increasingly
demanded to be provided in sufficient quantities, quality, safe, and evenly
distributed.
Food sufficiency for a nation is a very
strategic matter to support the development of healthy, active, and productive
human resources, this condition can be fulfilled and is reflected in the
availability of sufficient food, both in quantity and quality, distributed at affordable
prices and safe for consumption to support daily activities around the clock (Faradina et al., 2018).
The development of food security in Indonesia is emphasized in Food Law number
7 of 1996 concerning Food and Government Regulation Number 68 of 2002
concerning Food Security (Rachman & Ariani, 2016).
The diversity and balance of food consumption at the family level will
determine the quality of consumption at the regional, district/city, provincial
and national levels. The quality of food consumption of the population at the
regional (macro) level is reflected in the Expected Food Pattern (PPH) score,
which meets the nutritional needs of the population, it can be determined by
conducting an assessment of food consumption, through a portion calculation
approach (Purwati, 2021).
Currently, the PPH score has become a fairly strategic indicator and is a
performance indicator in the field of food security listed in the 2009 - 2014
RPJMN and 2015 - 2019 RPJMN.
The importance of achieving the PPH
score is also mandated by Law (UU) Number 18 of 2012 concerning Food and
Government Regulation No. 17 of 2015 concerning Food Security and Nutrition(Suryana et al., 2017).
Article 60 of Law No. 18 of 2012 it is stated that the Government and Regional
Governments are obliged to realize the diversification of food consumption to
meet the nutritional needs of the community (Ariani, 2015).
The achievement of diversification in food consumption is measured through the
achievement of value, composition, food patterns, and balanced nutrition, using
the Food Expectation Pattern (PPH) approach (Pangan, 2015).
The Hope Food Pattern (PPH) is the composition or composition of food or food
groups based on their energy contribution, both absolute and relative, which
fulfills nutritional needs in quantity, quality, and diversity by considering
social, economic, cultural, religious, and taste aspects (Cahyani, 2008).
The purpose of making PPH is to produce a normal composition or (standard) food
to meet the nutritional needs of the population that considers nutritional balance supported by taste (Portability), digestibility (digestability), and acceptance of the
community (Acceptability), quality
and affordability (Affordability), so
that PPH is expected to provide usefulness as an instrument to assess the
availability and consumption of food in the form of the amount and composition
of food by type of food, as a basis for calculating the PPH score which is used
as an indicator of food nutritional quality and diversity of food consumption
both at the level of availability and level of consumption and for planning
consumption and food availability (Sukesi & Shinta, 2011).
The higher the PPH score, the more diverse people's food consumption will be
towards the Expected Food Pattern (PPH).
Indonesia's PPH score nationally in 2017
was 90.4 out of an ideal score of 100, although this score is high but has not
yet reached the maximum score, meaning that Indonesia is not yet ideal in
meeting food needs and food consumption nationally(Nur Azizah, 2022). In
detail, the value of 90.4 is the total value of food consumption which consists
of 9 food groups, among others: the grains group of 25.0 in the ideal category,
the tubers group of 1.7 from the ideal score of 2.5 with the category not yet
ideal, animal food 22.3 out of an ideal score of 24 with not yet ideal
category, oil and fat 5.0 in the ideal category, oily fruit or seeds 0.9 out of
an ideal score of 1.0 which was categorized as not yet ideal, nuts 6, 2 out of
an ideal score of 10 in the not yet ideal category, sugar 2.5 ideal, vegetables
and fruit 26.8 from ideal 30.0 in the not yet ideal category and finally for
other groups 0, so that out of 9 food consumption groups only 3 food
consumption groups the ideal category are grains, oils and fats, and sugar
consumption group and the remaining 6 groups such as root food consumption
group, animal food, oily fruit or seeds, nuts and vegetables, and fruit are
still in the not ideal category. The South Sumatra Province PPH score in 2017
was 89.3 out of an ideal score of 100 and below the national PPH score with a
difference of 1.1. (Food, 2015) .
The Banyuasin Regency PPH score is 87.20 for 2020.
According to (Rafiq, 2016) the
costs incurred to buy and meet food needs are called household food consumption
expenditures, this cost is the value of spending made by households to buy
various types of needs at a certain time. Consumption is one of the determinants
of economic growth which is also an indicator of the welfare of the population.
People's consumption patterns based on the allocation of their use can be
classified into user groups, namely spending on food and non-food (Sarimunding & Aisyah, 2018).
Based on the source, food ingredients are divided into staple foods, animal
side dishes, vegetable side dishes, vegetables, and fruits. The type of food
consumed should ideally meet the quality and quantity requirements. The quality
of the food consumed must be able to meet all nutritional needs. Food that is
consumed if it can provide all the types of nutrients needed, the food can be
called quality (Normalita, 2018).
Nationally, the average monthly per
capita expenditure for the food group is 603,236 rupiah, each for rural areas
is 518,073 and for urban areas is 670,304 rupiah. Based on food commodity
groups, there are 5 highest groups consumed in rural and urban areas such as
prepared food and beverages (34.27 percent), cigarettes and tobacco (12.17
percent), grains (11.07 percent), fish/shrimp/ squid/shellfish (7.72 percent),
and vegetables 7.52 percent (Istighfarin, 2022). Meanwhile,
27.24 percent of other commodity groups consist of eggs and milk, fruits, meat,
beverage ingredients, oil and coconut, nuts, spices, tubers, and other food
ingredients. South Sumatra is one of
the provinces in Indonesia whose food consumption expenditure costs are 517,928
rupiahs with each for urban areas of 566,869 rupiahs and rural areas of 488,761
rupiahs per month in 2019.
The factors that influence consumption
expenditure are income, tastes, socio-cultural factors, wealth, government
debt, capital gains, interest rates,
price levels, money illusion,
distribution, age, geographical location, and income distribution (Takahindangen et al., 2021).
Income is one element that can reflect the socio-economic status of the
community. Socio-economic status can also be interpreted as the level of
prestige that a person has based on the position he holds in a society based on
work to meet needs or circumstances that describe the position or position of a
family in society based on material ownership (Taluke et al., 2021).
Based on the description of the
background explanation above and seen from the achievement of the PPH score of
Banyuasin Regency in 2020 of 87.20 this value is considered quite high from the
ideal PPH score of 100, but the PPH score of Banyuasin Regency is still below
the national PPH score of Indonesia in 2017 of 90.4. Therefore, researchers are
interested in analyzing and calculating the PPH score in one of the
sub-districts in Banyuasin Regency, precisely in Sumbawa District. With
research objectives 1). Analyzing the factors that influence household food
consumption expenditures in high, medium, and low-income communities in Sumbawa
District, Banyuasin Regency, 2). Analyzing the correlation of household food
consumption expenditure with the PPH value score in Sumbawa District, Banyuasin
Regency, 3). Formulating recommendations for efforts to achieve the ideal PPH
score in Sumbawa District, Banyuasin Regency.
Argandi et al. conducted research in 2019 regarding
Factors Affecting Hopeful Food Patterns (PPH) in Bandung Regency (Argandi et al., 2018). One way to determine food independence is through
the quality of diversity in food consumption as measured by the Expected Food
Pattern (PPH) score. PPH can be used as a measure of nutritional balance and
food diversity consumed by residents in an area. The maximum PPH score, 100,
indicates a situation of diverse food consumption, good composition and
nutritional quality (Baliwati, 2007). In practice, food quality and quantity indicators in
Bandung Regency have not been achieved. This study aims to determine the size
of the family, education level, and income level of PPH in Bandung Regency. The
primary method of this research is the method of explanation (Explanatory
Research). The determination of the Paseh and Pasirjambu sub-districts was
carried out by purposive sampling. Namely, the highest and lowest PPH
sub-districts were determined.
Furthermore, the size of the respondents using the Slovin
technique. To find out the factors that affect PPH in Bandung Regency using
multiple regression analysis techniques, the test is carried out using the SPSS
20 program. The results show that family size, education level and income level
positively affect Bandung Regency PPH. This means that the higher the family
size, education level and income level, the higher the PPH in Bandung Regency.
Research conducted by Khirul Anwar and Hardinsyah
regarding Food Consumption and Nutrition and Expected Food Patterns in Adults
aged 19-49 Years in Indonesia (Anwar &
Hardinsyah, 2014). This study aimed to assess food consumption,
Nutritional Quality of Food Consumption (MGP), an Expected Food Pattern (PPH)
score, and the correlation between PPH value and MGP for adults aged 19-49
years. This study uses Riskesdas 2010 as consumption data taken through a 24
-hour recall method. Based on the study's results, it was found that the most
significant consumption of grains was (99.4%), while the minor consumption of
oily seeds (2.0%). The mean PPH score was 53.1�9.3 (54.6�9.5 in males and
51.7�9.1 in females). The mean of MGP of 4 nutrients was 62.8�20.6, MGP of 10
nutrients was 51.1�15.4, and MGP of 14 nutrients was 54.1�16.1. The PPH and MGP
scores obtained a correlation of 0.65-0.72, so the PPH scoring system can be used
for the diversity and nutritional quality of adult food consumption
METHOD
This
research was conducted in Sumbawa District, Banyuasin Regency, precisely in 3
villages, namely Lalang Sembawa Village, Harapan Island, and Limau. The
selection of this location was carried out purposively
with the consideration that in this location the community groups supported
the research carried out because this study required groups of respondents with
different income levels, namely high, medium and low-income people. This group
difference aims to see the results of research regarding the achievement of PPH
scores in high, medium, and low-income communities, whether there are
differences that are influenced by several factors such as occupation,
household income, number of household members, non-food expenditure and length
of education and also for looking for formulations of recommendations for
efforts that can be given to support the achievement of the ideal PPH score,
penelitian ini dilakukan pada Oktober 2021.
The
sampling method used in this research is the disproportionate stratified random sampling method. The reason for
using this technique is because this study uses heterogeneous strata, namely
high, medium and low-income people with a total sample of 90, with each income
group of 30 samples which refers to the basic principles of statistics in
quantitative research with a minimum sample size of 30 samples so that the
distribution of the data obtained can be carried out by statistical tests.
The
data used in this study are primary data and secondary data. Primary data is
data obtained directly in the field by interviewing respondents to fill out
questionnaires that have been prepared regarding household food consumption in
high, medium, and low-income communities, and secondary data is obtained from
related agencies or agencies such as the Food Security Agency of the Indonesian
Ministry of Agriculture. Central Bureau of Statistics, Ministry of Health,
Widya Karya National Nutrition and Food, and other sources.
Find
out the value of household food consumption expenditure can be seen in people's
eating habits. Information about eating habits and the amount of food consumed
can be obtained by several methods, namely:
1. The
24 Hours Food Recall method is a
method used to estimate the amount of food consumed during the past 24 hours in
household size (URT) and then converted into grams (Khomsan, 2003).
2. The Food Records method
is a method that asks respondents to record all food and drinks consumed during
the week with URT units (Khomsan, 2003)
3. The Food Weighting method
is a method that asks respondents to weigh and record all the food consumed in
a certain period (Faridi et al., 2022)
4. The Food History method
is a method that aims to find the core pattern of daily food over a long period
and to see the relationship between food patterns and certain diseases (Khomsan, 2003).
5. The Food Frequency method
is a method used to obtain information on food consumption patterns and the
frequency with which a person consumes the food (Khomsan, 2003)
The method used to see the eating habits
of respondents in this study uses the 24
Hours Food Recall method, which means that respondents are asked to provide
information on their eating habits based on 24-hour memory from the time of the
last meal in URT (Household Size) which will later be converted into grams to
makes it easy to calculate the value of the PPH score.
The
data obtained from the field are presented in tabulation and continued with
mathematical and statistical data processing and described descriptively in the
discussion, data processing is assisted by Microsoft
Office Excel and SPSS software.
To
answer the first objective, the factors that influence household food
consumption were analyzed using Multiple Linear Regression and the results were
interpreted descriptively. Before performing multiple linear regression
analysis, several steps must be carried out, including:
1. Classic
assumption test
This
test aims to see the quality of the data so that the processed data has clear
validity and to avoid bias in the processed data. The use of classical
assumption test used, among others:
a. Normality
Test
The normality
test aims to test whether in the regression model the data in question is
normally distributed or not. Normality tests can be done using the Normal
P-Plot Test and the Kolmogorov-Smirnov test. With a decision rule
where the data can be said to be normally distributed if the Asymp value. Sig
(2-tailed) 0.05, then the data is normally distributed. If the Asymp
value. Sig (2-tailed) 0.05, then the data is not normally distributed.
b. Multicollinearity
Test
The
multicollinearity test was used to test whether the
regression model found a correlation between the independent variables. A good
regression model is characterized by the absence of multicollinearity symptoms.
One way to determine the presence or absence of multicollinearity symptoms is
to use the Tolerance and VIP (Variance Inflation Factor) methods.
As for the
decision rule, if the Tolerance value is 0.10 and the VIF value 10 then
multicollinearity occurs. On the other hand, if the Tolerance value is
0.10 and the VIF value is 10, multicollinearity does not occur.
c. Heteroscedasticity
Test
The
heteroscedasticity test aims to test whether in the regression model there is
an inequality of variance in the residual value from one observation to another
observation. A good regression model is
characterized by the absence of heteroscedasticity symptoms. This study is to
test the presence or absence of heteroscedasticity symptoms by using the scatterplot
test and the lesser test. As for the decision
rule, if the Sig value < 0.05 then heteroscedasticity symptoms occur,
and conversely if the Sig value > 0.05 then there are no heteroscedasticity
symptoms.
2. Multiple Linear Regression Analysis
Multiple linear regression analysis is used to determine
the pattern of changes in the value of a variable (dependent/bound
variable) caused by other variables (independent/independent variable). This
regression analysis uses a mathematical model in the form of a straight-line
equation that can define the relationship between variables according to the
research objectives. In this study, multiple linear regression models are used
to see how the dependent
variable is household food consumption expenditure which
is associated with independent or independent
variables, namely (X1) the number of household members, (X2)
non-food expenditure, (X3), length of education, (D1)
high income (D2) medium income, (D3) self-employed. Then
the multiple linear regression model used is (Iqbal, 2015):
Y =
+ 1 X 1 + 2 X 2 + 3 X 3
+ 4 D 1 + 5 D 2 + 6 D
3 +�
Information:
Y��� : household food consumption expenditure
(Rp/month)
�� : Interceptsor constants
β
1, β2, β3� β9
: Regressioncoefficient
X 1� : Number of household members (Persons)
X 2� : Non-food Expenditure (Rp/Month)
X 3� : Length of education (Years)
D 1
= 1 : for
high income
= 0:
for other income
D 2 = 1 : for medium
income
= 0:
for other income
D 3
= 1: for Self-employed Workers
= 0:
for Non-Self employed Jobs
�
� = intruder error
To
see the relationship between the dependent variable and the independent
variable used in the multiple linear regression test, several tests must be
carried out, including:
1. Coefficient
of Determination Test (r2)
The coefficient of determination (r2) is a
test that measures how far the regression model's ability to explain the
variation of the dependent variable (dependent). The value of the coefficient
of determination ranges between 0 and 1. If the value of the coefficient of
determination is getting closer to 1 or equal to 1, then the independent
variable (independent) can explain or provide all information on the dependent
(dependent) variable (Ghozali, 2005).
The weakness in the use of this coefficient of
determination is the bias towards the number of independent (independent)
variables included in the model. With each addition of 1 independent variable,
the coefficient of determination will increase regardless of whether the
variable has a significant effect or not on the dependent variable (dependent ).
2. T
Test (Partial)
T-test (partial) in multiple linear regression is used to seethe
magnitude of the relationship between each independent
variable on the
dependent variable and whether an independent
variable affects or not the dependent variable. This test aims to test partially or individually the effect of independent variables (age of housewife, housewife's education, number of
household members, household income group, and snacks consumed) on the dependent variable (household food diversification). To partially
test the variables that influence X 1, X 2,
X 3, D 4, D 5 on Y, the T-test is used. The formula used is as follows :
Description :�����
T ���������� = Calculated value
b 1 �������������� =
Coefficient value of independent variable (Variable X)
sb 1 ������������ =
Standard error value of the independent variable (variable)
t -test decision rules are as follows:
1.
If it is
significant < 0.05 then Ho is rejected. Ha is accepted. This means that there is a significant effect of independent variables (occupation, household income, number of household members, non-food expenditure, and education level) partially or individually on the dependent variable (household
food consumption).
2.
If significant
> 0.05 then Ho is accepted and Ha is rejected. This means that there is no significant effect of independent variables (occupation, household income, number of household members, non-food expenditure, and education level) partially or individually on
the dependent variable (household food consumption).(Suharyadi.,
et al ., 2014).
3. F
test (simultaneous)
F (simultaneous) test was
conducted to determine whether all independent variables simultaneously
(simultaneously) affected the dependent variable. The F test aims to show
whether all the independent variables that are included in the model
simultaneously or together have an influence or not on the dependent variable. This study shows whether the independent variables consisting of
employment variables,
household income, number of household members, non-food expenditure, and education level affect the
dependent variable, namely household food consumption. The formula
used is as follows:
Description :�����
K ���������� = Number of
independent variables
R 2 �������������� =
Coefficient of determination
nk-1 ����� = Degrees of freedom in the denominator
Rule of decision is as
follows:
1.
If F count >
F table then the independent variables used in this study together
(simultaneously) have a significant effect on the dependent variable.
2. By comparing the calculated F value with the F table if F
arithmetic < F table, then the independent variables used in this study
simultaneously (simultaneously) do not have a significant effect on the
dependent variable.
The second objective is about the
relationship between the value of household food consumption and the
achievement of the PPH score. Then the calculation is carried out first to
determine the PPH value score. The steps used to calculate the PPH score based
on the rules (Food, 2015) include:
1.
Food grouping: The food
consumed is grouped into 9 (nine) food groups according to the Expected Food
Pattern (PPH) standard, as follows:
Table 1. Food Grouping 2015
|
No
|
Food Group
|
Commodity Type (PPH Group)
|
|
1
|
Grains
|
rice and its products, corn and its products, wheat and
its products.
|
|
2
|
Tubers
|
cassava and its products, sweet potatoes, potatoes, taro,
sago, and starchy foods.
|
|
3
|
Animal Food
|
meat and its products, fish and their products, eggs,
and milk, and their products.
|
|
4
|
Oil and fat
|
coconut oil, palm oil, margarine, and animal fats
|
|
5
|
Oily Fruits/Seeds
|
coconut, candlenut, walnut and chocolate, and various
kinds of fruit
|
|
6
|
Nuts
|
peanuts, soybeans, green beans, kidney beans, peas, cashews,
cowpeas, other nuts, tofu, tempeh, taco, oncom, soy milk, soy sauce
|
|
7
|
Sugar
|
granulated sugar, brown sugar, syrup, finished drinks
in bottles/cans
|
|
8
|
Vegetable and fruit
|
fresh vegetables and their processed products, fresh
fruits and their products, including chips
|
|
9
|
etc
|
various spices and beverage ingredients such as shrimp
paste, cloves, coriander, pepper, nutmeg, tamarind, cooking spices, tea, and
coffee
|
2.
Convert from, type and unit
Food
consumed by households is in various forms, and types with different units.
Therefore, the unit of weight needs to be standardized by converting it into
the same (agreed) unit and type of commodity using a conversion factor so that
the weight can be added up, preferably the food consumed is converted into raw
weight. Things that need to be considered in converting the form, type, and
unit of food consumed are:
a.
If the food consumption data
is a type of processed food made from several types of food ingredients, then
first describe it into several types of single food constituents with the
amount according to the unit weight of each food. For example, for 1 portion of
chili fried liver, the main ingredients are 8 potatoes and 300 grams of beef
liver.
b. If
the unit of weight is in household size (URT), then convert the weight of each
type of food from URT to grams. For example, 8 potatoes are equivalent to 400
grams, concerning the agreed URT conversion list applicable in their respective
regions.
c. If
what is known is the cooking weight, it is necessary to calculate the raw
weight by multiplying the cooking weight by the raw conversion factor. For
example, 200 grams of the fried liver is equivalent to 200 x 1.5 = 300 grams of
beef liver.
d. If
food is processed using oil, then the weight of the oil absorbed by the food
needs to be calculated by multiplying the raw weight of the food by the percent
oil absorption factor. For example, 300 grams of beef liver absorbs as much as
300 x 4.8 percent = 15 grams of cooking oil.
3.
Calculating the sub-total
energy content by food group at this stage, the energy content of each type of
food consumed is calculated with the help of the list of food ingredients
composition (DKBM). The energy column in the DKBM shows the energy content
(kcal) per 100gram of edible parts (BDD). Example: 50 g of rice = energy
content of rice x percent BDD 360 kcal x 100/100 180 kcal x 50 g x 100/50 g
Next, the amount of energy for each type of food is added up according to the food
group.
4.
Calculating the actual total
energy of all food groups at this stage, what is done is to add up the total
energy of each food group so that the total energy of all food groups will be
known. The total energy of 9 food groups = Energy of grains + tubers
+................+ energy of other groups.
5.
Calculating the energy
contribution of each food group to the actual total energy (percent) At this
stage is to assess the energy pattern/composition of each food group by
calculating the energy contribution of each food group divided by the total
actual energy of all food groups and multiplied by 100 percent.
6.
Energy contribution per food
group (percent) 100 percent Food group energy Total actual energy x Example:
100 percent 52.6 percent Actual energy contribution for the rice - grain group
Energy for the rice - grain group Total actual energy x 1150 2185 6.
Calculating the energy contribution of each group of food to the Energy
Adequacy Rate (percent AKE). At this stage, it is a step to assess the level of
energy consumption in percent (percent) by calculating the energy contribution
of each food group to the AKE (AKE consumption for the 2012 national average is
2,150 kcal/cap/day). percent AKE) = 100 percent Energy in the food group AKE
Consumption x Example: The energy contribution of the grains group to the AKE
is x 100 percent = 53.5 percent.
7.
Calculating the actual score
at this stage, what is done is by multiplying the actual contribution of each
food group by its respective weight. Actual score = actual energy contribution
of each food group x weight of each food group.
8.
Calculating the AKE score at
this stage is done by multiplying the AKE contribution (percent AKE) of each
food group by its respective weight. AKE score = percent AKE of each food group
x weight.
9.
Calculating the PPH Score
The actual PPH score is calculated by comparing the AKE score with the maximum
score. The maximum score is the maximum score limit for each food group that
meets the ideal composition. The calculation of the PPH score for each food
group is subject to the following conditions: v If the AKE score is higher than
the maximum score, the maximum score is used.
10.If
the AKE score is lower than the maximum score, then the AKE score is used. The
PPH score for each food group shows the composition of the population's food
consumption at a certain time/year. For example, the AKE score for the grains
group is 26.8 compared to the maximum score for the grains group of 25.0, so
the PPH score for the grains group is 25.0. Calculating the Total Score of
Expected Food Patterns. The total score of the Expected Food Pattern (PPH)
which is known as the quality of food consumption is the sum of the scores of 9
food groups, namely the number of the grain group to the score of the other
groups. This figure is called the food consumption PPH score, which shows the
level of diversity in food consumption. PPH score = PPH score for grains +
tubers + ..... + PPH score for other groups.
To see the relationship
between household food consumption and the achievement of the PPH score, a test
was conductedon parametric correlation. That is Pearson's test. Pearson
correlation is a statistical analysis tool used to see the close linear
relationship between 2 variables whose data scale is interval or ratio(Kurniawan, 2016).
Which can be formulated, as follows:
Where r XY is a correlation
coefficient that can be positive (+) or negative (-) and is in the range of -1
and 1. If r XY is close to -1 or 1 then the close relationship between
the two variables is getting stronger. If the value is close to 0, then the
relationship between the two variables is getting weaker. The following is an
interpretation of the value of the correlation coefficient.
� 0
- 0.2 indicates a very weak relationship,
� 0.2
- 0.4 indicates a weak close relationship,
� 0.4
- 0.7 indicates a fairly strong close relationship,
� 0.7
- 0.9 indicates a strong close relationship,
� 0.9
� 1 indicates a very strong close relationship.
RESULTS AND DISCUSSION
1.
Factors
Affecting Household Food Consumption Expenditure����������
The
factors that influence household food consumption expenditures in this study
are the number of household members, non-food expenditures, and length of
education as the main variables supported by a dummy variable, namely the job
variable used as a dummy for self-employed workers and non-self-employed
workers, and income, namely income. high, medium, and low, because in this
research the researcher sees from 3 sides of the income group. For this
purpose, the research conducted multiple linear regression tests, namely the
classical assumption test consisting of normality, heteroscedasticity, and
multicollinearity tests and multiple linear regression consisting of
determination test (r), simultaneous f test, and partial T-test. The following
multiple linear regression model can be formulated:
Y =
+ 1 X 1 + 2 X 2 + 3 X 3
+ 4 X 4 + 5 X 5 + 6 D
1 + 7 D 2 + + 8 X 1 +�
Information:
Y��� : Household Consumption Expenditure
(Rp/Month)
�� : Intercepts or constants
β
1, β2, β3� β9
: Coefficient regression
X1� : number of household member (Person)
X 2� : Household income (Rp /month)
X 3� : Number of family members (soul)
X 4
: Non-food Expenditure (Rp/Month)
X 5� : Education level (Years)
D 1
= 1 : for
high income
= 0:
for other income
D 2
= 1 : for
medium income
= 0:
for other income
D 3 = 1 : for low income
= 0: for other income
��� = intruder error
The following are the results of the
classical assumptions that have been carried out with the help of the SPSS
application.
1. Classic
assumption test
This
test aims to see the quality of the data so that the processed data has clear
validity and to avoid bias in the processed data. The use of classical
assumption test used, among others:
a. Normality
test
The normality test aims to test whether in the regression model
the data in question is normally distributed or not. The
results of the normality test on this goal are met because the data is the Asymp value. Sig (2-tailed) 0.05, and the points
on the Normal P-plot are on the diagonal line. It can be seen in Figure 1.
Following.
Figure
1. Normality
Test
b. Multicollinearity
Test
The multicollinearity test was used to test whether the regression model found a correlation between
independent variables. In this study, there were no symptoms of multicollinearity
because the results of the tolerance value were> 0.10 and the VIF value <
10. More clearly it can be seen in Table 2. The following Multicollinearity
Test.
Table 2. Multicollinearity Test
|
Model
|
Collinearity
Statistics
|
|
Tolerance
|
VIF
|
|
(Constant)
|
|
|
|
JART/ORG
|
.944
|
1.060
|
|
PNP/RP.BLN
|
.549
|
1,822
|
|
LP/THN
|
.641
|
1,561
|
|
PT
|
.274
|
3,655
|
|
PS
|
.476
|
2.103
|
|
PW
|
.499
|
2003
|
|
|
|
|
|
T
c. Heteroscedasticity
Test
The
heteroscedasticity test aims to test whether in the regression model there is
an inequality of variance in the residual value from one observation to another
observation. In this study, there were no symptoms of heteroscedasticity
because there was no clear pattern on the scatterplot, and the dots spread
above and below the number 0, which more clearly can be seen in Figure 2.
Figure 2.
Heteroscedasticity test
2. Multiple Linear Regression Analysis
Multiple linear regression analysis was used to determine
the pattern of changes in the value of a variable (dependent/bound variable)
caused by other variables (independent/independent variable). To
see the relationship between the dependent variable and the independent
variable used in the multiple linear regression test, several tests must be
carried out, including:
1. Coefficient
of Determination Test (r2)
The coefficient of determination (r2) is a
test that measures how far the regression model's ability to explain the
variation of the dependent variable (dependent). The value of the coefficient
of determination ranges between 0 and 1. In this study, the
value of r2 is 0.263, meaning that only 26.3 percent of the
variables affect each other. It can be seen more clearly in Table 3.
Coefficient of Determination Test (r2).
Table. 3.
Coefficient of Determination Test (r2)
|
Model Summary b
|
|
Model
|
R
|
R Square
|
Adjusted R Square
|
Std. The error in the Estimate
|
|
1
|
.559 a
|
.313
|
.263
|
398444.253
|
2. T
Test (Partial)
T-test ( partial ) in multiple linear regression is used to seethe
magnitude of the relationship between each independent variable
on the dependent variable and whether an independent variable affects or not the dependent
variable. partially tested with the T-test, the results
are significant only on the high-income dummy variable, which is worth 0.23
which can be interpreted that households with high incomes have higher
household consumption expenditures as much as 393,336.718 compared to food
consumption expenditures of medium and low-income households. It can be seen
more clearly in Table 4. T Test (Partial).
Table 4. T Test (Partial)
|
Coefficients a
|
|
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
T
|
Sig.
|
|
B
|
Std. Error
|
Beta
|
|
1
|
(Constant)
|
693017.380
|
288681.325
|
|
2.401
|
.019
|
|
JART/ORG
|
73841,218
|
45125.852
|
.153
|
1,636
|
.106
|
|
PNP/RP.BLN
|
.230
|
.255
|
.111
|
.902
|
.370
|
|
LP/THN
|
16016,787
|
16400,848
|
.111
|
.977
|
.332
|
|
PT
|
393336,718
|
170339,621
|
.402
|
2,309
|
.023
|
|
PS
|
168497,865
|
129203427
|
.172
|
1.304
|
.196
|
|
PW
|
-40650.454
|
119361.868
|
-.044
|
-.341
|
.734
|
|
PW
|
-40650.454
|
119361.868
|
-.044
|
-.341
|
.734
|
3. F
Test (Simultaneous)
F (simultaneous)
test was conducted to determine whether all
independent variables simultaneously (simultaneously) affected the dependent
variable. The F test aims to show whether all the independent variables that
are included in the model simultaneously or together have an influence or not on the
dependent variable. Simultaneously, the factors that affect
food consumption expenditures have an effect simultaneously because the
significant value of f is less than 0.005, which is 0.00. It can be seen more
clearly in table 5. F-test (simultaneous).
Table 5. F test
(simultaneous)
|
ANOVA a
|
|
Model
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
|
1
|
Regression
|
5998499186188.850
|
6
|
999749864364.808
|
6.297
|
.000 b
|
|
Residual
|
13176899294366,705
|
83
|
1587578228233.695
|
|
|
|
Total
|
19175398480555.555
|
89
|
|
|
|
2.
Correlation
of Household Food Consumption Expenditure Value with PPH Value Score (Hopeful
Food Pattern)
To
see the relationship between household food consumption expenditure and the
achievement of the PPH score, a test was carried outon parametric correlation. That
is Pearson's test. Pearson correlation is a statistical analysis tool used to
see the close linear relationship between 2 variables whose data scale is
interval or ratio. The results obtained show that the correlation value between
food consumption expenditure and the achievement of the PPH score is 0.665,
which means that it is strongly correlated. Clear results can be seen in Table 6.
Parametric correlation test (Pearson test).
Table 6.
Parametric correlation test (Pearson test).
|
Correlations
|
|
|
PPH
|
PKP
|
|
PPH
|
Pearson
Correlation
|
1
|
.665 **
|
|
Sig. (2-tailed)
|
|
.000
|
|
N
|
90
|
90
|
|
PPK
|
Pearson
Correlation
|
.665 **
|
1
|
|
Sig. (2-tailed)
|
.000
|
|
|
N
|
90
|
90
|
**. Correlation is significant at the 0.01 level
(2-tailed).
3.
Formulating
Effort Recommendations to Support Achieving the Ideal PPH Score
The
formulation of recommendations for efforts that can be given to support the
achievement of an ideal PPH score is that households must pay attention to the
content of food that is spent and cooked and served to the family starting from
the content of energy, protein, vitamins, minerals and the content of
substances contained in foodstuffs that are consumed. What is needed by the
body does not only focus on one staple food because our bodies need a variety
of nutrients and nutrients to support health and energy in activities. The following
is the result of the PPH score in high, medium, and low-income households,
which can be seen in Table 7. PPH Score Score.
Table 7. PPH Value
Score
|
Low income
|
|
Food Group
|
E. Act
|
% Actual
|
% AKE
|
Weight
|
Actual Score
|
AKE score
|
Max Score
|
PPH Score
|
|
Rice
|
1196.92
|
34.83
|
57
|
0.5
|
17.42
|
28.5
|
25
|
25
|
|
Tubers
|
104.96
|
3.05
|
5
|
0.5
|
1.53
|
2.5
|
2.5
|
2.5
|
|
Animal Food
|
300.32
|
8.74
|
14.3
|
2
|
17.48
|
28.6
|
24
|
24
|
|
Oil & Fat
|
790.49
|
23
|
37.64
|
0.5
|
11.5
|
18.82
|
5
|
5
|
|
Oily Fruits/Seeds
|
5.22
|
0.15
|
0.25
|
0.5
|
0.08
|
0.12
|
1
|
0.12
|
|
Nuts
|
147.38
|
4.29
|
7.02
|
2
|
8.58
|
14.04
|
10
|
10
|
|
Sugar
|
377.32
|
10.98
|
17.97
|
0.5
|
5.49
|
8.98
|
2.5
|
2.5
|
|
Vegetables & Fruits
|
127.4
|
3.71
|
6.07
|
5
|
18.54
|
30.33
|
30
|
30
|
|
Etc
|
386.31
|
11.24
|
18.4
|
0
|
0
|
0
|
0
|
0
|
|
Total
|
3436.32
|
100
|
163.63
|
|
80.61
|
131.9
|
100
|
99.12
|
|
Medium income
|
|
Food Group
|
E. Act
|
% Actual
|
% AKE
|
Weight
|
Actual Score
|
AKE score
|
Max Score
|
PPH Score
|
|
Rice
|
1237.6
|
54.85
|
58.93
|
0.5
|
27.42
|
29.47
|
25
|
25
|
|
Tubers
|
95.35
|
4.23
|
4.54
|
0.5
|
2.11
|
2.27
|
2.5
|
2.27
|
|
Animal Food
|
286.72
|
12.71
|
13.65
|
2
|
25.41
|
27.31
|
24
|
24
|
|
Oil & Fat
|
0.06
|
0
|
0
|
0.5
|
0
|
0
|
5
|
0
|
|
Oily Fruits/Seeds
|
0.39
|
0.02
|
0.02
|
0.5
|
0.01
|
0.01
|
1
|
0.01
|
|
Nuts
|
140.07
|
6.21
|
6.67
|
2
|
12.42
|
13.34
|
10
|
10
|
|
Sugar
|
246.19
|
10.91
|
11.72
|
0.5
|
5.46
|
5.86
|
2.5
|
2.5
|
|
Vegetables & Fruits
|
138.42
|
6.13
|
6.59
|
5
|
30.67
|
32.96
|
30
|
30
|
|
Etc
|
111.61
|
4.95
|
5.31
|
0
|
0
|
0
|
0
|
0
|
|
Total
|
2256.4
|
100
|
107.45
|
|
103.5
|
111.21
|
100
|
93.78
|
|
High income
|
|
Food Group
|
E. Act
|
% Actual
|
% AKE
|
Weight
|
Actual Score
|
AKE score
|
Max Score
|
PPH Score
|
|
Rice
|
1362.02
|
41.14
|
64.86
|
0.5
|
20.57
|
32.43
|
25
|
25
|
|
Tubers
|
97.75
|
2.95
|
4.65
|
0.5
|
1.48
|
2.33
|
2.5
|
2.33
|
|
Animal Food
|
366.83
|
11.08
|
17.47
|
2
|
22.16
|
34.94
|
24
|
24
|
|
Oil & Fat
|
593.72
|
17.93
|
28.27
|
0.5
|
8.97
|
14.14
|
5
|
5
|
|
Oily Fruits/Seeds
|
2.77
|
0.08
|
0.13
|
0.5
|
0.04
|
0.07
|
1
|
0.07
|
|
Nuts
|
137.01
|
4.14
|
6.52
|
2
|
8.28
|
13.05
|
10
|
10
|
|
Sugar
|
290.99
|
8.79
|
13.86
|
0.5
|
4.39
|
6.93
|
2.5
|
2.5
|
|
Vegetables & Fruits
|
153.09
|
4.62
|
7.29
|
5
|
23.12
|
36.45
|
30
|
30
|
|
Etc
|
306.75
|
9.26
|
14.61
|
0
|
0
|
0
|
0
|
0
|
|
Total
|
3310.94
|
100
|
157.66
|
|
89
|
140.32
|
100
|
98.89
|
It is known from table 6. The core value
of PPH achieved by households of all income groups is close to ideal but the
one with the closest results is the low-income group of 99.12 but the high
achievement of the PPH value is not in line with the AKE score of each food
group, because the amount AKE has been regulated by the Ministry of Health
nationally and the amount determined has been adjusted based on the energy
needs of the human body. So if the AKE score is not
met, the human body and body lack the energy and strength to support daily
activities, but if the AKE score exceeds the predetermined standard, it will
have side effects of disease on the body.
Based on research by Asmara, et al. 2009
regarding the Effect of Economic and Non-Economic Factors on Food Diversification
Based on Expected Food Patterns. The analysis results through the calculation
of the Expected Food Pattern showed that the PPH score in the research area was
52.83, with an AKE value of 1911.6 kcal/cap/day. This result is still quite far
compared to the normative PPH score 100 and normative AKE 2200. The regression
analysis results showed that of the 6 independent variables contained in the
model, only 2 variables had a significant influence on food diversification,
namely the education of housewives and the number of household members. For
housewife, education positively affects PPH scores, with a regression
coefficient of 4.529. Meanwhile, the number of household members negatively
affects the regression coefficient of -2.765 (Asmara et al., 2009).
Based on research conducted by Manurung,
et al., 2012 regarding the Expectation of Food Patterns for the Community of
Tejosari Village, Metro City. The energy adequacy rate (RDA) used is the AKG set
at the VIII Food and Nutrition Widya in 2004, which is 2,000 kcal. The food
consumed is still less than the need, namely the root food group, animal food,
fruit, sugar, etc. The animal food group showed the most "less"
difference, 109.69 Kcal/cap/day. Foods that must be increased in consumption
are tubers, animal foods, fruit and sugar. Based on the PPH score of 86.52, the
food eaten has not met expectations and is less diverse (Manurung et al., n.d.).
Based on research conducted by Mewa Ariani, 2010. People's food consumption
patterns are increasingly diverse, with higher PPH scores. However, to achieve
the PPH food pattern, rice consumption must be reduced; on the other hand,
tubers, animal foods, vegetables and fruit still need to be significantly
increased (Ariani, 2014).
CONCLUSION
Based on the analysis of the results of
the research conducted, the following conclusions can be drawn: 1) The most influential
factors in determining food consumption expenditure are employment and income,
especially in high-income households, which are also supported by a length of
education and the number of family members based on multiple linear regression
tests that have been carried out. 2) There is a strong correlation between food
consumption expenditure and the PPH value score, meaning that what households
eat is a strong food consumption expenditure with efforts to achieve expected
food patterns. 3) For the formulation of recommendations for efforts to support
the achievement of the ideal pph value score, households, especially mothers as
those who buy, provide, and cook food for the family can pay more attention to
the nutritional and nutritional content of each food group that is processed to
be given to households to meet energy values, protein, minerals, and vitamins
that are needed by the body of household members to support daily activities.
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Received:
02-09-2022 ������������������ ������������� Accepted: 06-10-2022 �������������������� ����������� Published: 18-10-2022������ 
