Analysis of Dominant Factors Affecting the Performance of the Small
IMMT Sector in Medan City
Eko Wahyu Nugrahadi
1
, Indra Maipita
1
, Johnson
1
, La Ane
1
, M. Buchori Dalimunthe
1
and Muamar
Rinaldi
1
1
Faculty of Economics, Universitas Negeri Medan, Medan –Indonesia
Keywords: Food and Beverage Industry; household income inequality; SEM; and managerial skills
Abstract: The economic trend in Medan (as measured by Gross Regional Domestic Product / GRDP per sector on the
basis of constant 2000 prices) in the 2012-2016 period experienced a significant increase. However, the results
were not fully enjoyed by all levels of society, because in that period the development of the Medan City Gini
Ratio Index had increased (from 0.33 in 2012 to 0.37 in 2016). This means that there has been income
inequality between household groups in the area. Several studies have been carried out in overcoming this
problem. One of the results of research in North Sumatra has identified that the Small Food, Beverage and
Tobacco Industry (IMMT) is the optimal alternative model of the IMMT sector economic development policy.
The question needs further analysis to get more in-depth information about the development of the sector in
the city of Medan. This study aims to analyze the dominant factors that influence the performance of optimal
economic policies in the Small IMMT sector development. Based on the analysis of the Structural Equation
Model (SEM) with a sample of 50 respondents obtained results, there are three dominant factors that influence
the performance of the Small IMMT sector in Medan, and of the three factors the most dominant are
managerial skill factors.
1 INTRODUCTION
Achieving high economic growth is the main target
that will usually be pursued in an economy to create
a just and prosperous society. The economic
development of Medan, measured by Gross Regional
Domestic Product (GRDP) on the basis of constant
prices in 2000, from 2012 to 2016 experienced a
significant increase. As shown in Figure 1.1, it can be
seen that within 5 years the average growth rate of
North Sumatra is 6.22%. This indicates that the
welfare of the people of Medan city is increasing. As
indicated by the condition of the number of poor
people in this area in that period decreased from
11.31% (2012) to 10.39% (2016).
Although the number and percentage of poor
people from time to time has decreased, but with the
poverty rate in 2016 amounting to 206.87 thousand
people or in the first place, the number of population
in the city of Medan is considered too high, so that the
right solution is needed. that is.
Figure 1: Medan City Gross Domestic Product (in
Million Rupiah) Source:BPS, 2010-2014
One of the root causes of poverty in Indonesia,
especially the city of Medan is the high disparity due
to the uneven distribution of income among
household groups, so that the gap between the rich
households and poor households is widening. One
measure in determining the inequality of household
income is the Gini Ratio index. During the period of
2012-2016 the development of the Medan City Gini
Ratio Index has increased (from 0.327 in 2012 to
0.374 in 2016) so that income inequality among
430
Nugrahadi, E., Maipita, I., Johnson, ., Ane, L., Dalimunthe, M. and Rinaldi, M.
Analysis of Dominant Factors Affecting the Performance of the Small IMMT Sector in Medan City.
DOI: 10.5220/0009500204300434
In Proceedings of the 1st Unimed International Conference on Economics Education and Social Science (UNICEES 2018), pages 430-434
ISBN: 978-989-758-432-9
Copyright
c
 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

household groups in this area tends to increase. In
addition, the magnitude of the Gini ratio shows that
the condition of income inequality between
households is relatively high. Thus the economic
development of the city of Medan which is high and
the tendency to increase is not fully enjoyed by all
levels of society, thus leading to the creation of
income gaps between household groups and poverty.
Therefore the size of the results of economic
development cannot only be reflected by the high
economic growth and the size of regional income, but
also includes the following in terms of human
development (income inequality, the number of poor
people and unemployed). Therefore, in designing an
economic development strategy so that it is not only
aimed at economic growth, it also needs to be
followed by improvement in addition to the decline in
the number of poor people and reducing
unemployment as well as the distribution of income.
From previous research (Nugrahadi, 2013) it was
known that the Food, Beverage and Tobacco Industry
(IMMT) sector and especially the small IMMT
(Nugrahadi, 201) were optimal alternative models of
the IMMT sector economic development policy in
North Sumatra. The question is, what are the
dominant factors that influence the performance of
the economic policies for the development of the
Small IMMT sector in the city of Medan? Thus this
study aims to analyze the dominant factors that
influence the performance of optimal economic
policies in the Small IMMT sector development.
2 THEORICAL FRAMEWORK
As stated by Todaro (2013) that the development
process must be able to bring humanity beyond the
prioritization of material and financial aspects of their
daily lives. This means that the size of the results of
economic development is not only reflected by high
economic growth, but also needs to be followed by
improving income distribution, decreasing the
number of poor people and reducing unemployment.
Very diverse theoretical approaches that can be
adopted by policy makers in the process of economic
development. One of them is known as the concept of
unbalanced growth put forward by A.O. Hirschman,
has an understanding that in economic development
is based on unbalanced growth. According to
Hirschman, investments in strategic industries or
sectors and related to one another through linkage
will generate new investment opportunities and pave
the way for further economic development (Jhingan,
2003).
In essence the concept of unbalanced growth is a
strategy that develops sectors that have strong links.
According to this linkage theory, it means backward
linkage and forward linkage. The proposal to develop
the economic sector which has this connection applies
not only to the industrial sector but also to the
agricultural sector but the entire economic sector.
As explained earlier, the Small is a sector that has
a large linkage in North Sumatra (Nugrahadi, 2015),
so that this sector is recommended as the leading
sector for the development of economic development
in the city of Medan. However, before this policy is
carried out it is necessary to analyze the dominant
factors that affect the performance of this sector.
From the study of Nugrahadi (2013) factors that
influence the performance of the Small IMMT sector
shown by business people (entrepreneurs), namely
competence and business feasibility. According to
Suryana (2003) entrepreneurial competence is
someone who has the knowledge, skills and qualities
of individuals which includes attitudes, motivations,
values and behaviors needed for entrepreneurship.
Furthermore, it is said that the competencies include:
(1) Managerial skills, (2) Conceptual skills, (3)
Human skills, (4) Decision making skills, and (5)
Time Managerial Skills.
While business feasibility or also called a
feasibility study, is an activity to assess the extent to
which benefits can be obtained in carrying out a
business activity. The results of this analysis are used
as material for consideration in making decisions,
whether to accept or reject a business idea. Worthy
understanding in this research is the possibility of the
idea of a business that will be implemented can
provide benefits in the sense of financial and social
benefits. With this feasibility analysis it is expected
that the risk of failure in marketing the product can be
avoided.
Knowing whether a business is feasible or not, it
must be analyzed for each aspect. According to
Mudjiarto (2006) criteria that can be used as an
assessment aspect in developing a business are: (1)
analysis of marketing aspects, with indicators:
consumer needs and desires, market segmentation,
targets, value added, product life span, market
structure, competitive strategy, market size, market
growth, gross profit, and market share), (2) analysis
of aspects of production, with indicators: operating
location, operating volume, machinery and
equipment, machinery and equipment, raw materials
and auxiliary materials, labor, and lay out, (3)
analysis of management aspects, with indicators:
ownership, organization, management team,
employees, and (4) analysis of financial aspects, with
indicators: sources of funds, balance projections,
projections of losses and profits, cash flow
projections and calculation of investment criteria.
Analysis of Dominant Factors Affecting the Performance of the Small IMMT Sector in Medan City
431

3 RESEARCH METHOD
The population in this study were all small
entrepreneurs in the IMMT sector in Medan. The
sample used in this study was 50 respondents
independent entrepreneurs in the city of Medan were
taken by random sampling. The source of data in this
study uses primary data in the form of two variables,
namely competency variables and entrepreneurial
business feasibility analysis capabilities using the
Likert scale with five choices of answers, namely,
Strongly Agree (SS), Agree (S), Disagree (KS),
Disagree (TS), and Strongly Disagree (STS).
To answer the problems in this study, an analysis
based on Linear Structural Relationships (LISREL)
or analysis of the structure of covariance. Analysis of
the Structural Equation Model (SEM) is also called
Linear Structural Relationships (LISREL) or analysis
of covariance structures. This method is used to
analyze the microeconomic aspects with the aim of
finding out the dominant factors that influence the
performance of the optimal economic policy in the
IMMT sector development.
4 RESULTS AND DISCUSSION
4.1. Assumption Analysis Factor Test
Before testing the factor analysis first tested two
assumptions related to factor analysis, namely the
assumption of the normality of univariate variables
(each variable) and the assumption of
multicollinearity with the KMO and Barlett Test tests,
and Anti Image Matrices Test.
1) Assumption of univariate normality
Normality testing was carried out with non-
parametric Smmnrov Smirnov samples. Based on the
results of the analysis it is known that overall the Sig.
> 0.05, which means that all data are normally
distributed.
2) Multicollinearity
a) Test KMO and Barlett’s Test
The results of the multicolinerity test of KMO and
Bartlett's Test show that: (1) the Adequancy KMO
value is 0.715> 0.5, which means that this factor
analysis variable can be further processed, and (2)
Bartlett's test value of 150.667 with a significance of
0.000 <0.05, which means that there is a significant
correlation between the overall factor analysis
variables. Thus the overall variable analysis of this
factor analysis can be tested by factor analysis. But to
find out more about any variable in this factor
analysis it is necessary to test the anti image matrices.
b) Anti Image Matrices-Measure of Sampling
Adequacy (MSA) Test
The anti image matrices correlation test results show
the coefficient of the MSA value> 0.5, which means
that both the variables and the variables in this factor
analysis meet the requirements and are worthy of
further analysis.
4.2. Interpretation of Factor Analysis Results
1) Communalities
Analysis of communalities is basically the amount of
variance in the form of a percentage of each initial
variable that can be explained by existing factors. The
results of the Communalities calculation show that
the overall variance value of the variable is> 0.5,
which means that the level of closeness is strong with
the factors formed, so that factor analysis can be done
without removing the variables in this study.
2) Total Variance Explained
This analysis is intended to determine how many
factors might be formed. Components or factors to be
chosen are factors with eigenvalue values greater than
1 (factors that are able to explain variables well). The
results of the calculation of total variance explained
show that out of 9 components only 3 factors are
formed because the eigenvalue value is greater than
1. From the 3 factors formed, it will be able to explain
the total variance of 68.74 percent of the variables.
3) Component Matrix and Rotated Component
Matrix
The results of calculation of component matrix in this
study are as follows:
Table 1: Component Matrix
Component Matrix
a
Component
1
2
3
Manajerial Skill
.753
-.105
-.034
Time Manajerial Skill
.694
.148
-.291
Marketing
.683
.311
.450
Conceptual Skill
.677
-.534
-.069
Decision Making Skill
.640
.306
-.374
Financial
.598
-.231
.313
Human Skill
.528
.420
-.526
Manajerial Skill
.570
-.694
-.018
Production
.537
.451
.571
Extraction Method: Principal Component Analysis.
a. 3 components extracted.
Source: Research Results, 2018 (Data processed by
SPSS)
Based on Table 1, it can be seen that the variable
correlation value is still very evenly distributed,
where the correlation of a variable in one factor
component is still relatively the same as the variable
correlation in the other factor components. For this
reason rotation is carried out on the dimensions of the
UNICEES 2018 - Unimed International Conference on Economics Education and Social Science
432

factor, so that the rotated component matrix is
obtained as shown in Table 2. Rotation is done by the
varimax method. Determination of variables that
enter into the new factor component based on
correlation values greater than 0.5.
Table 2: Rotated Component Matrix
Rotated Component Matrix
a
Component
1
2
3
Manajerial Skill
.897
.044
-.023
Conceptual Skill
.836
.213
.063
Financial
.562
.044
.438
Manajemen
.548
.425
.314
Human Skill
-.019
.853
.060
Decision Making Skill
.145
.768
.181
Time Manajerial Skill
.305
.674
.202
Production
-.003
.139
.894
Marketing
.193
.245
.817
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 4 iterations.
Source: Research Results, 2018 (Data processed by SPSS)
Based on Table 2. then grouped variables into newly
formed factors as follows,
Factor1: Managerial Skill, Conceptual Skill,
Financial and Management.
Factor 2: Human Skill, Decision Making Skill and
Time Managerial Skill.
Factor 3: Production and Marketing.
4) Component Transformation Matrix
As the last step is to determine the accuracy of the
factors formed from all factor analysis variables used
by using the Component Transformation Matrix. The
calculation results show the overall component/
factor has a correlation> 0.5, which means that the
factor 1, factor 2, and factor 3 formed can be said to
be appropriate to summarize 9 independent variables
used in the factor analysis in this study.
4.3. Hypothesis testing
Hypothesis testing is done by sorting the score scores
from the largest to the smallest. The way to find out
the factor score is:
VarianceTotal
FactorVarianceMatrixComponentRotated
FaktorSkor


The results of calculating factor scores are shown in
Table 3. From Table 3, it can be seen that based on a
factor score of> 0.75 there are three dominant factors
in the Small IMMT sector business in Medan,
namely: managerial skills, human skills and
production.
From the three variables, it is seen that managerial
skills are the most dominant indicator. This shows
that managerial skills are a provision that must be
possessed by the Small IMMT entrepreneur. In this
case the entrepreneur must be able to carry out the
functions of planning, organizing, mobilizing and
controlling so that the business he runs can achieve
the desired goals. In other words, technically he must
have the ability to analyze and develop markets, the
ability to manage human, material, money, facilities
and all resources (Suryana, 2003).
Table 3: Dominant Indicators
No
Variabel
Component
Variance
Total
Variance
Factor
Skor
Factor 1
Factor 2
Factor 3
1
2
3
3.633
1.423
1.133
6.189
1
Manajerial
Skill
0.897
0.044
-0.023
3.259
0.063
-0.026
3.295
0.887
2
Conceptual
Skill
0.836
0.213
0.063
3.037
0.303
0.071
3.412
0.744
3
Financial
0.562
0.044
0.438
2.042
0.063
0.496
2.601
0.441
4
Manajemen
0.548
0.425
0.314
1.991
0.605
0.356
2.951
0.370
5
Human Skill
-0.019
0.853
0.06
-0.069
1.214
0.068
1.213
0.854
6
Decision
Making Skill
0.145
0.768
0.181
0.527
1.093
0.205
1.825
0.460
7
Time
Manajerial
Skill
0.305
0.674
0.202
1.108
0.959
0.229
2.296
0.282
8
Production
-0.003
0.139
0.894
-0.011
0.198
1.013
1.200
0.755
9
Marketing
0.193
0.245
0.817
0.701
0.349
0.926
1.975
0.383
Source: Research Results, 2018 (Data processed by SPSS)
Analysis of Dominant Factors Affecting the Performance of the Small IMMT Sector in Medan City
433

The results of the study are in line with the studies
of Yuliana and Pujiastuti (2018), especially related to
the components of market development as one of the
managerial aspects of skills. According to him
Market orientation (market development) is a
measure of behavior and activity that reflects the
implementation of the marketing concept. Market
orientation as the most effective organizational
culture in creating important behavior for the creation
of superior value for buyers and performance in
business. So that an increase in market orientation
will improve business performance.
5 CONCLUSIONS
Based on the results of SEM, three dominant factors
were found in the small food, beverage and tobacco
industry sectors, namely managerial skills, human
skills and production. Of the three factors, the most
dominant is managerial skill.
Based on the findings of this study indicate that
the small IMMT sector shows a large role in the city.
Thus the sector is one of the leading sectors. This
implies that in spurring economic growth
accompanied by equal distribution of income in the
city of Medan in the future it should be prioritized for
the small IMMT sector.
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