INTRODUCTION
Indonesia has an archipelagic geographical characteristic
with an estimated number of large and small islands of 17,504 islands and a
productive coastline of 81,000 square kilometers (Arsana, 2014). Thus, it can be concluded that Indonesia has the
largest coastal area in the world. Tegal City is a
city that is traversed by the coastline in the northern coastal area of
Central Java Province so that it has the potential for coastal tourism which
is quite attractive to tourists (Muttaqin
et al., 2015), one of which is the Alam
Indah Beach Tourism Object. The beach which has an area of about 16 ha is
directly adjacent to the fishing port and fish auction, making the beach location
quite strategic. The shoreline is quite sloping and has a variety of facilities
that support natural beauty such as spots forest mangrove, multifunctional
recreation parks, platforms, gazebos, and other public places that make it more
attractive for tourists to visit. This makes Alam
Indah Beach a tourist attraction that has a fairly high intensity of visitors
every year.
All tourism potentials owned by Alam
Indah Beach with characteristics and facilities that are quite capable and high
visitor intensity make the existence of these objects very important and
strategic in the tourism sector by generating Regional Original Income (PAD)
and becoming a sector for the economic development of the surrounding community
in terms of absorption labor. This makes it important to maintain the existence
of Alam Indah Beach tourism to gain economic benefits
in the future. Economic benefits can be provided with a payment mechanism for
environmental services by anyone who has benefited from the tourism object (Ingram et al., 2014).
It is a payment scheme to protect and restore the availability of goods and
services so that they can continue into the future. In addition, this beach
tourism object is a public good that is used specifically by the public so that
there may be an externality impact that can cause market failure of a public
good that has no market value (Horne, 2019).
Understanding the importance of the correlation between
benefits and the costs of environmental services and the impact of
market-destroying externalities on tourism objects, it is necessary to estimate
the valuation of these attractions. The method used to evaluate tourist objects
in the form of beaches is the travel cost method. This method is usually used
to determine the value of area related to ecosystems and natural resources such
as a national park, beach, and other outdoor tourism objects that related to
ecosystems and natural resources (Solikin et al., 2019).
In general, the travel cost method describes the theory of
demand with the concept of consumer surplus in it. Consumer surplus in the form
of the level of consumer willingness to pay (willingness to pay) as a result of
enjoying the benefits of environmental services provided by the tourism object.
The travel cost method uses an individual travel cost approach (Individual
Travel Cost Method). The travel cost approach based on the individual was
chosen because of its advantages in the form of technological advances and the
ability to describe the social and economic characteristics of visitors which
are difficult to find when using the travel cost method based on regional zones
(Blackwell, 2007).
The tourism object that have been appraised are Batu Karas beach in Pangandaran (Zulpikar et al., 2017),
the result of the research are travel costs, distance and duration of visits
have a significant effect on the level of tourist visits to Batu
Karas Beach, while the age of visitors, income, level
of education and number of group members have no significant effect on the
level of visits. This method produced an
estimated value of Batu Karas beach of IDR 86.5
billion. Based on the explanation above, it is
necessary to conduct the valuation
of Pantai Alam Indah using the individual travel cost
method.
Research
objectives of study are:
a) Identifying
the condition of the Alam Indah Beach Tourism Object
related to the intensity of visitors and the contribution of income to the
current Regional Original Income (PAD) compared to the previous year.
b) Knowing
descriptively the analysis of the socio-economic characteristics of visitors at
the Alam Indah Beach Tourism Object.
c) Identify
quantitatively in the form of a relationship between the dependent variable in
the form of the number of visits with several independent variables that
support the Alam Indah Beach Tourism Object.
d) Determine
consumer surplus and estimate the valuation of environmental service-based
tourism objects at the Alam Indah Beach Tourism
Object.
METHOD
A. Types of Data
Data used in this paper is quantitative data in the
form of recap data of visitor questionnaires which are processed to determine
the relationship of variables related to the number of visits and to determine
the economic value of the object.
B.
Data sources
The data sources used by the author in this study
are primary and secondary data sources. Primary data includes data obtained
from observations in the form of surveys and direct interviews with visitors
with the media in the form of questionnaires. Secondary data includes data obtained
directly from related managers such as the accumulated number of visitors and
annual income as well as those obtained indirectly through websites such as annual budget
realization reports and data on entrance fees for tourist objects.
C.
Scope of Research
The research was carried out covering the entire
area of the Alam Indah Beach Tourism Object with
the limitation of tourist locations being only the main tourist attractions and
not considering additional tourism around objects such as water, campgrounds, and urban forest
tours. The research was carried out from January 4, 2021 to April 4, 2021
according to permission from the tourism agency by sampling visitor data every
Saturday and Sunday so that the accumulation of sampling time was 24 days. Data
analysis includes descriptive socio-economic analysis of visitors and
statistical quantitative analysis with multiple linear regression. Multiple linear regression
is a type of regression where the dependent variable shows a linear relationship
with two or more independent variables. Multiple
linear regression was used to determine the estimated economic value of the Alam Indah Beach Tourism Object. Visitor accumulation data
uses projected annual visitor numbers in 2021 using the trend from 2015 to 2020.
D. Sampling
Techniques
Samples were
taken from respondents who visited the Alam Indah
Beach Tourism Object by chance and unintentionally which were suitable to be
used as data sources. The author communicates with respondents directly to tourist sites and then
asks for permission to conduct
research surveys by filling out questionnaires that have been prepared.
Therefore, the author uses a sampling method in the form of accidental sampling. According to Etikan et al. (2016), accidental method is
applicable to both qualitative and quantitative studies and place primary emphasis on generalizability (i.e., ensuring that the
knowledge gained is representative of the population from which the sample was
drawn). Some limitations of
accidental methods according to Etikan et al. (2016), are researcher
subjective and biased in choosing the subjects of the study and impede the
researcher’s ability to draw inferences about a population. According to (Saptutyningsih & Ningrum,
2017) the number of samples that will be used as
research can be found using the Slovin Formula as
follows:
Description:
n = number of samples needed
N = number of population
d = limit of error or absolute precision
Limit of error
or absolute precision (d) or the tolerance value used is 10% because the error
rate can still be considered in the selection of a random sample. Then, the
population uses the average number of daily visitors in 2020 for 24 days, so
the minimum number of samples that can be taken refers to the Slovin Formula as follows:
or 100
E.
Limitation of Sampling
1)
The minimum age
limit of respondents to be sampled is 15 years.
2)
Samples were
obtained from visitors who entered by paying a levy in the form of entrance
tickets and parking tickets assuming the tickets were charged on Saturdays and
Sundays.
3)
Revenue is only
calculated from entrance tickets and parking tickets from Pantai Alam Indah Tourism Object and ignores visits to water parks, campgrounds, and urban
forest tours.
4)
Visitors are
assumed to only visit one tourist attraction, namely Alam
Indah Beach.
5) There are no visitors with an income of IDR 0.
F.
Operational Definition of Research
Variables used
in processing sample data include the dependent variable in the form of the
frequency of visits per tourist within a period of one year and independent variables which include the
following.
1)
Travel costs (travel costs) are costs incurred by
tourists both costs to the location of tourist objects in the form of
consumption costs, transportation costs, transportation rental costs, and other
costs as well as costs while in tourist attractions such as consumption costs,
entrance and parking fees, costs public toilets, equipment rental fees,
documentation fees, and other costs expressed in Rupiah (IDR).
2)
Age is the age
of tourists expressed in years.
3)
Income of
tourists received per month and is expressed in Rupiah (IDR).
4)
Tour duration is the maximum period of
time that can be visited by tourists expressed in hours.
5)
Education level is the last education held by
tourists to date.
6)
Travel duration the maximum period of time
that can be taken to reach the object location by tourists expressed in hours.
RESULTS AND DISCUSSION
A. General overview
1. Description of
Research Objects
Alam Indah Beach is
one of the local tourist destinations in the Tegal
City area. Located on Jalan Sangiran,
Mintaragen Village, East Tegal
District which has an area of about 16 ha. This tourist attraction is located
in an urban environment which is 500 m from the main national road so as to
facilitate accessibility for visitors even though there is no special city
transportation route to get to the tourist area. Visitors can easily use any
transportation because the path is adequate with a width of ± 6 m, has been
accompanied by clear directions, and there is a gate that reads Pantai Alam Indah, making it easier for visitors to access the location.
Geographically, this beach is located at latitude 6°51'6.3" South Latitude
and longitude 109° 08' 34.1" East Longitude. Based on the Regional
Regulation of the City of Tegal Number 4 of 2012
concerning the Spatial Plan of the City of Tegal in
the years 2011-2033, Alam Indah Beach is located in a
tourism zone bordering the following areas.
North side : Java Sea
East side
: Halmahera Street and Gung River
South side : Java Street and settlement
West side : Java Street and Harbor
The location
map for Alam Indah Beach Tourism Object is as
follows:
Figure 1. Map
Location of Alam Indah Beach Tourism Object
Source: (Fatkhussalam, 2013)
Alam Indah Beach
has a total length of about 1000 m located on the North Coast of Java facing
the Java Sea. The beach has physical characteristics in the form of sloping
contours, the waves are not so heavy and high, and abrasion is rare but
experiences sedimentation on average 3 m per year. The beach contains dense
brown sea sand that is easily absorbed by water so it is not easily affected by
flooding. There are several cypress trees and mangroves around the beach so that apart from reducing abrasion,
it also adds to the attraction of tourists to visit. Breakwater stones that are
still solidly standing at certain points and are often used as spots for fishing are a distinctive
feature of this tourist attraction.
Alam Indah Beach
Tourism Object is equipped with several recreational facilities and public
facilities that support tourism potential on the beach. Recreational facilities
currently available include water
parks, marine monuments, mangrove,
tour boats, water tricycles, platforms, giant
letters that read "Pantai Alam
Indah", viewing post, art village, campground, and entertainment stage.
Public facilities currently available include a large parking area for
motorbikes and special cars, houses of worship in the form of a mosque, fairly
clean toilets, stalls along the coast, 6 lighting points, and several huts or
pavilions to gather and rest for a while.
2. Description of
Visitor Intensity
Alam Indah Beach is
a local beach tourist attraction with the highest number of visitors in Tegal City. According to data from the Youth, Sports and
Tourism Office of Tegal City, the number of tourist
visits to tourist attractions in the last five years tends to be stable and
slightly fluctuating. The average is in the range of approx. 500,000 visitors
except in 2020 which tends to decrease drastically due to the Covid-19 with around half of the
normal number of annual tourist visits. In March – May and October 2020, this
beach attraction experienced a temporary closure to reduce the mobility of the
community which then had a significant impact on the decline in the number of
visitors in 2020. The historical data for the annual number of tourists to Alam Indah Beach Tourism Object in 2015-2020 as follows:
Table 1
Number of Annual Visitors 2015-2020
|
Year
|
Number of visitors
|
|
2015
|
523.811
|
|
2016
|
506.480
|
|
2017
|
538.229
|
|
2018
|
498.884
|
|
2019
|
502.096
|
|
2020
|
286.857
|
Source:
Processed from the Youth, Sports and Tourism Office of Tegal
City
Visitors to the
Alam Indah Beach Tourism Object are dominated by
local tourists who come from the City of Tegal
itself. However, it is undeniable that there are also many people from outside
the city who are close to Tegal City such as Tegal Regency, Brebes Regency,
and Pemalang Regency who visit the beach for a moment
to vacation and unwind. During the Lebaran and New
Year holidays, some people from outside the city, both in Central Java Province
and outside Central Java Province, took the time to visit Tegal
City in order to visit their family villages and at the same time take an
excursion to Alam Indah Beach Tourism Object. As a
result, from year to year during the month period, it is natural that there is
a significant surge in visitors. Although the scope of the visiting area is not
too wide with the scale of visits still being local, the enthusiasm of the
local community remains high so that this beach tourism object can remain
productive and able to develop from year to year.
3.
Income Description
The existence of fluctuating visitors affects the
income of the Alam Indah Beach Tourism Object. The
majority of income is obtained from user fees for both entrance tickets and
parking. Based on the Tegal City Regional Regulation
Number 3 of 2019 concerning Business Service Retribution, the entrance fee levy
is distinguished for weekdays and holidays and each is further differentiated
for adults over 12 years old and children 5-12 years old. In addition to the
entrance fee levy, there is a parking fee levy which is differentiated based on
the type of transportation. In addition to the daily entrance ticket, there is
also an entrance ticket that is paid monthly for visitors who subscribe. In
addition to income from entrance tickets and parking, the majority are also
obtained from user fees, which are dominated by kiosk rentals for permanent
traders. In addition to permanent traders, non-permanent traders such as
traveling traders are also charged with different rates.
Thus, the income earned over the last five years
tends to increase except for 2020, which experienced a decline in income due to
a reduction in visitor intensity due to mobility restrictions from the Covid-19
pandemic. The income obtained will later be included in the Regional Original
Income (PAD) of the City of Tegal in the regional
retribution income post. The income generated from the Alam
Indah Beach Tourism Object from year to year tends to contribute to a fairly
increasing PAD except in 2020 itself which only contributed half of the
previous year, which was 0.34% of the total PAD. The historical data for the
annual revenue from the Alam Indah Beach Tourism
Object compared to the total PAD in 2015-2020 is as follows:
Table 2
Total Income to Total PAD in 2015-2020
|
Year
|
Total income
|
Total PAD
|
Percentage of total income to PAD
|
|
2015
|
930.545.200
|
271.601.407.419
|
0,34%
|
|
2016
|
1.058.231.900
|
287.343.889.954
|
0,37%
|
|
2017
|
1.642.130.000
|
306.830.528.135
|
0,54%
|
|
2018
|
1.616.111.175
|
275.021.448.594
|
0,59%
|
|
2019
|
1.617.512.250
|
285.575.788.984
|
0,57%
|
|
2020
|
940.392.500
|
275.042.870.000
|
0,34%
|
Source: Processed from the Tegal
City Youth, Sports and Tourism Office and the 2015-2020 City Budget Realization
Report
B. Descriptive
Socio-Economic
Analysis of
Visitors. Descriptive socio-economic analysis of visitors describes the profile
of tourists who visit the Alam Indah Beach Tourism
Object. The aim is to facilitate the interpretation of the distribution of
large amounts of raw data in the form of visitor characteristics as a basis for
the development of tourism objects in the future. The distribution of visitor
characteristics is based on the results sampling with a questionnaire survey as
follows.
1. Total cost
Table 3
Percentage
of Respondents
|
Total cost (IDR)
|
Percentage (%)
|
|
≤ 50.000
|
9
|
|
50.001 - 100.000
|
57
|
|
100.001 - 150.000
|
14
|
|
150.001 - 200.000
|
15
|
|
≥ IDR200.001
|
5
|
|
Total
|
100
|
Source:
Processed from Visitor Questionnaire Data
It is known that the total cost is dominated by the
group ranges from IDR. 50,001 to IDR. 100,001 with a percentage of 57% of
respondents. On the other hand, the total cost by minority groups is in the
range of more than IDR. 200,001 with a percentage of the number of respondents
being 5%. This indicates that the characteristics of visitors tend to minimize
spending by maximizing the potential profits received from Alam
Indah Beach tourism.
2.
Total income
Table 4
Percentage of
Respondents
|
Total income (IDR)
|
Percentage (%)
|
|
≤ IDR 1.000.000
|
16
|
|
1.000.001 - 2.000.000
|
24
|
|
2.000.001 - 3.000.000
|
31
|
|
3.000.001 - 4.000.000
|
18
|
|
4.000.001 - 5.000.000
|
7
|
|
≥ 5.000.001
|
4
|
|
Total
|
100
|
Source:
Processed from Visitor Questionnaire Data
It is known that the total income is dominated by
the group ranging from IDR2,000,001 to IDR3,000,000 with a percentage of 31% of
respondents. On the other hand, the total cost by minority groups is in the
range of more than 5,000,001 with a percentage of 4% of respondents. This
indicates that visitors tend to have limited funds due to several factors.
First, the cost factor for entrance tickets to tourist objects is relatively
cheap and can be accepted by all circles of society. Second, the distance factor
where the majority of visitors are local people with a position around the City
of Tegal so that it does not take time and travel
costs to travel to tourist sites.
3.
Age
Table 5
Percentage of Respondents
|
Age (years
old)
|
Percentage
(%)
|
|
≤ 20
|
10
|
|
21 – 30
|
36
|
|
31 – 40
|
28
|
|
41 – 50
|
14
|
|
51 – 60
|
10
|
|
≥ 61
|
2
|
|
Total
|
100
|
Source:
Processed from Visitor Questionnaire Data
It is known that the total age group is dominated
by a group ranging from 21 to 30 years with a
percentage of 36% of respondents. On the other hand, age by minority group is
in the range group of more than 61 with a percentage of the number of
respondents being 2%. This indicates that the characteristics of visitors tend
to be at a very productive age and tend to want to take free time more than busy
activities so that it is very suitable for the characteristics of those who
like to travel.
4.
Tour Duration
Table 6
Percentage of respondents’ tour duration
|
Tour duration (hours)
|
Percentage (%)
|
|
≤1
|
4
|
|
2–3
|
56
|
|
4–5
|
40
|
|
≥6
|
0
|
|
Total
|
100
|
Sources: Processed
from Visitor Questionnaire Data
It is known that the length of the tour is
dominated by groups ranging from 2 to 3 hours with a percentage of 56% of
respondents. On the other hand, the length of tourism by minority groups is in
the range of less than 1 with a percentage of 4% of respondents. This indicates
that the characteristics of visitors tend to be able to enjoy tourism objects
to the maximum by various kinds of natural beauty and supporting infrastructure
for the Alam Indah Beach Tourism Object.
5.
Education Background
Table 7
Percentage of respondents’ education background
|
Education background
|
Percentage (%)
|
|
Never attend
formal school
|
0
|
|
Elementary
school
|
0
|
|
Junior high
school
|
6
|
|
Senior high
school
|
32
|
|
Diploma
|
25
|
|
Undergraduate
|
37
|
|
Total
|
100
|
Source:
Processed from Visitor Questionnaire Data
It is known
that the education level is dominated by the undergraduate group with a
percentage of 37% of respondents. On the other hand, the level of education by
minority groups is in the junior high school group with a percentage of 6% of
respondents. This indicates that the characteristics of visitors tend to be at
the level of psychological need for high curiosity about tourist objects and
high motivation to travel because of more knowledge than higher education.
6.
Travel Duration
Table 8
Percentage of
respondents’ travel duration
|
travel duration (hours)
|
Percentage (%)
|
|
≤0,25
|
37
|
|
0,26 - 0,50
|
37
|
|
0,51 - 0,75
|
17
|
|
0,76 - 1,00
|
4
|
|
≥ 1,01
|
5
|
|
Total
|
100
|
Source:
Processed from Visitor Questionnaire Data
It is known
that the travel duration is dominated by groups ranging from less than 0.25
hours and 0.26 to 0.50 hours with a percentage of 37% of respondents. On the
other hand, the travel duration by minority groups is in the range of 0.76 to
1.00 hours and more than 1.01 with a percentage of the number of respondents
respectively 4% and 5%. This indicates that the characteristics of visitors
tend to be in the coverage area around the City of Tegal
so they tend to be quick to get to the location of the Alam
Indah Beach Tourism Object.
C.
Quantitative Analysis of Visitor Data
1.
Determining regression model
To model multiple linear regression into a good and
appropriate equation, firstly, a regression model feasibility test is carried
out with the classical assumption test as follows.
a)
Normality Test
A good
regression model is a model that is normally distributed. To test the normality
of the model can use two ways as follows.
1)
Normality Test Probability Plot
According to Ghozali (2011), the regression model is said to be normally distributed if the plotting (dots) that describe the
actual data follow a diagonal line.
Figure 1. Normality Probability Plot
Source:
Processed from IBM SPSS Application 22
It is known that in general it appears that the plotting (dots) have followed a diagonal line, so it can be
concluded that the regression model can be said to be normally distributed.
2)
Kolmogorov Smirnov Normality
Test
The basis for
decision making on this normality test is if the significance value is more
than 0.05 then the residual value is normally distributed and if the
significance value is less than 0.05 then the residual value is not normally
distributed.
Table 9
One-sample Kolmogorov Smirnov
|
Unstandardized Residual
|
|
N
|
100
|
|
Normal parametersa,b
|
Mean
|
,0000000
|
|
Std. Deviation
|
,48263518
|
|
Most extreme differences
|
Absolute
|
,078
|
|
Positive
|
,073
|
|
Negative
|
-,078
|
|
Test Statistic
|
,078
|
|
Asymp. Sig. (2-tailed)
|
.141c
|
|
a. Test distribution is Normal.
|
|
b. Calculated from data.
|
|
c. Lilliefors Significance Correction.
|
Source:
Processed from IBM SPSS Application 22
It is known
that the significance value is at 0.141
which is greater than 0.05. Therefore, it can be concluded that the residual
value is normally distributed which indicates a good regression model.
b)
Multicollinearity test
The
multicollinearity test is part of the classical assumption test in multiple
linear analysis which aims to determine whether there is an intercorrelation or
a strong relationship between independent variables. A good regression model is
characterized by no intercorrelation between independent variables or
multicollinearity symptoms. One of the most accurate ways to detect the
presence or absence of this multicollinearity symptom is to use the Tolerance and VIF (Variance Inflation
Factor) method. According to (Ghozali,
2018), there is no symptom of multicollinearity if the
tolerance is more than 0.100 and the VIF is less than 10.00.
Table 10
Collinearity Statistics Tolerance and VIF based on
Dependent Variable of visitors number
|
Model
|
Collinearity Statistics
|
|
Tolerance
|
VIF
|
|
(Constant)
|
|
Total cost
|
,183
|
5,474
|
|
Total income
|
,110
|
9,056
|
|
Age
|
,242
|
4,132
|
|
Tour duration
|
,234
|
4,270
|
|
Education background
|
,395
|
2,529
|
|
Time travel
|
,789
|
1,268
|
|
a.
Dependent variable: Visitors number
|
c) Heteroscedasticity
Test
The
heteroscedasticity test aims to determine whether or not there is a similarity
between the variances of the residual values for all observations in the
regression model. A good regression model is characterized by the absence of
heteroscedasticity symptoms. To test the presence or absence of
heteroscedasticity symptoms from a model, two methods can be used as follows.
1)
Heteroscedasticity
Test Scatter Plots
According to (Ghozali,
2018) there is no heteroscedasticity if there is no clear
pattern in the sense of wavy, widening, and narrowing in the scatter plots and the points spread
above and below the number 0 on the Y axis.
Figure 2. Heteroscedasticity Scatter Plots
Source:
Processed from IBM SPSS Application 22 Based on Graph 2 on Heteroscedasticity
Based on Figure 2 regarding
Heteroscedasticity Scatter Plots, it can be seen that there is no clear pattern
in the plotting data (dots) so it can be concluded that the regression model does
not have heteroscedasticity.
2)
Glejser Heteroscedasticity Test
This heteroscedasticity is
if the significance value between the independent variable and the absolute
residual is more than 0.05 then there is no heteroscedasticity and if the
significance value is less than 0.05 then heteroscedasticity occurs.
It is known that the
significance value of the independent variables of total cost, length of
travel, and length of trip is in a position smaller than the significance value
of 0.05 so that it can be concluded that these variables partially affect the
dependent variable of the number of visits. Then, it is also known that the
significance value of the independent variables of total income, age, and
education level is in a position greater than the significance value of 0.05 so
that it can be concluded that these variables partially have no effect on the
dependent variable of the number of visits.
2) Partial t-test based on arithmetic and tables
values
According to Sujarweni (2014) if the t-arithmetic is greater
than, if the t-table, the independent variable partially affects the dependent
variable. To find the t-table, it can use the distribution table for the0.05 of
1.989. To find the 2014), if the t-arithmetic, table ANOVA listed at -18,023
for the total cost variable, -1.375 for the total income variable, 1.371 for
the age variable, 3.511 for travel duration variable, -0.062 for the education
level variable, and -3.309 for the old variable. journey. It can be concluded that
the variables of total cost, tour duration, and length of trip are outside the
t-table between -1.989 and 1.989, so the total cost and travel duration have a
negative effect on the dependent variable of visit, while on the other hand the
travel duration variable has a positive effect on the dependent variable of
visit. For the total income, age, and education level variables are in the
t-table between -1.989 and 1.989, these variables have no partial effect on the
dependent variable the number of visits. For mapping the position of the
variables in the area of influence with the area of uninfluenced, it can
be seen in the graphic image as follows:
Model
Unstandardized
t Sig.
Figure 3.
Mapping of Independent Variable Areas
f)
Coefficient of Determination (R2)
Coefficient of determination or commonly referred
to as the symbol R2 used to find out what percentage of the
influence of the independent variables simultaneously on the dependent
variable. Based on the ANOVA listed,
it is known that the R2 value is 0.965 means that the independent
variable simultaneously has an effect of 96.5% on the dependent variable.
g) Predictor
Contribution
Predictor contribution is a description of the
magnitude of the contribution of influence in percent (%) given by each independent variable to the
dependent variable. Predictor contributions are grouped into 2 types, namely
effective contribution (SE) and relative contribution (SR). The contribution of
the right predictor to find out how much partial influence is independent variables to the dependent variable is the
effective contribution (SE). The formula for calculating SE is as follows:
SE (X) % =
Regression Coefficient (Beta) x Correlation Coefficient x 100%
It is known
that independent variables such as total cost, length of trip, and length of
trip have both positive and negative effects based on the t test partial has
the magnitude of the contribution to the dependent variable the number of
visits, respectively 80.27%, 11.80%, and
1.37%.
A hypothesis
that passes the statistical test can be transformed into a regression equation
as follows:
Y = 12,338 - 3.749E-05 X1 -
1.431E-07 X2 + 0.012 X3 + 0.347 X4 - 0.005 X5 - 0.0589 X6
Information:
Y = Number of Visits
X1 = Total Cost
X2 = Total Income
X3 = Age
X4 = Tour duration
X5 = Education Level
X6 = Travel duration
Then, the
regression equation can be interpreted more clearly as follows.
1)
The constant
coefficient is 12,338 which means that if the number of independent variables
is 0, then the dependent variable in the form of the number of visits is
12,338.
2)
The total cost
coefficient is -3.749E-05 which hypothetically means that the higher the total
cost variable, the lower the number of visits variable. It was concluded from
the results of statistical tests that the total cost variable had a partial
negative influence of 80.27% on the number of visits variable. This result in
line with Dewanta (2010); Solikin et al. (2019); and Zulpikar et al.
(2017).
3)
The coefficient
of total income is -1.431E-07 which hypothetically means that the higher the total
income variable, the lower the number of visits variable, from the statistical
results, but the total income variable had no partial influence on the number
of visits variable.
4)
The coefficient
of total age is 0.012 which hypothetically means that the higher the age
variable, the higher number of visits variable, but from the statistical test
results that the age variable had no partial influence on the number of visits
variable.
5)
Tour duration
coefficient is 0.347, which hypothetically means that the higher the tour
duration, the higher the number of visits. It was concluded from the results of
statistical tests that the
total cost variable had a positive effect of 11.80% on the number of visits
variable. This result in line with Dewanta (2010); Solikin et al. (2019); and Zulpikar et al. (2017).
6)
The education
level coefficient is -0.005 which hypothetically means that the higher the
education level variable, the lower the number of visits variable, but from the
statistical results, the education level variable had no partial influence on
the number of visits variable.
7)
The coefficient
of travel duration is -0.0589 which hypothetically means that the higher the
length of the trip, the lower the number of visits. It was concluded from the
statistical test results that the total cost variable had a negative influence
of 1.37% on the number of visits variable. This result in line with Zulpikar et al. (2017).
h) Determination
of Surplus Consumer
Surplus
consumer is obtained from the econometric approach with the output of the total cost coefficient
from the multiple linear regression equation of -3.749E-05 and also the number
of visits of each individual who is the object of research. The formula for
calculating consumer surplus is as follows:
CS = Consumer surplus
V = Number of visits for
each individual
sample
β1
= Total cost coefficient
Then, from the
consumer surplus of each individual object of research that has been calculated
based on the formula above, it can be calculated in aggregate into a total
consumer surplus with the conclusion that the value is IDR. 130,355,024 per
total number of visits per year of 955 times from 100 samples of research
objects. Then, it can also be seen that the average consumer surplus per
tourist is IDR. 1,303,550 from the distribution of the total consumer surplus
with a total sample of 100 people. Then, the average consumer surplus per
tourist per visit or referred to as willingness
to pay (WTP) is IDR. 136,497 from the distribution of the average
consumer surplus per tourist with the average number of visits per individual
sample in a year of 9.55.
D. Determination
of the Estimated Value Economic Object Research
After knowing
the value of the consumer surplus, then the estimated economic value of the Alam Indah Beach Tourism Object by considering the
projection of the number of visitors in the research year, 2021. The projection
of the number of visitors in 2021 is calculated using the trend historical data on the number of annual visits from 2015 to
2020. The historical data on the number of visits and their increase during
2015-2020 can be seen as follows:
Table 14
Percentage Increase in Number of Visits 2015-2020
|
Year
|
Number of visitors
|
Increase in number of visitors
(people)
|
Increase in number of visitors
(%)
|
|
2015
|
523.811
|
|
|
|
2016
|
506.480
|
-17.331
|
-3,31%
|
|
2017
|
538.229
|
31.749
|
6,27%
|
|
2018
|
498.884
|
-39.618
|
-7,36%
|
|
2019
|
502.096
|
3.485
|
0,70%
|
|
2020
|
286.857
|
-215.239
|
-42,87%
|
|
Average
|
476.014
|
-47.391
|
-9,31%
|
Based on the average
percentage increase in the number of visits by -9.31%, it can be used as a
calculation of projected visits in 2021 with the addition of an average
percentage increase in the number of visits by -9.31% in the number of visits
in the previous year, namely 2020 Then it was found that the projected number
of visits in 2021 was 260,139 people.
From the
results of willingness to pay (WTP)
of IDR. 136,497 and the projected number of visits in 2021 of 260,139 people,
it can be calculated the estimated economic value of the Alam
Indah Beach Tourism Object by multiplying the two results to obtain a
conclusion of a value of IDR. 35,508,314,810.
CONCLUSION
Determination
of the economic valuation of the Alam Indah Beach
Tourism Object using the Travel Cost Method with an Individual Approach (Individual Travel Cost Method) for
the provision of coastal tourism environmental services focuses on descriptive
analysis and quantitative analysis.
Descriptive
analysis includes mapping the socio-economic characteristics of visitors as the
basis for determining the right independent variables in the form of total
costs, total income, age, length of travel, length of trip, and level of
education.
Quantitative analysis
includes determining the right regression model in the form of multiple linear
regression models with the dependent variable in the form of the number of
visits and the independent variables in the form of total costs, total income,
age, length of travel, length of trip, and level of education through
statistical tests and test the influence of variables by generating the
equation feasible regression as follows:
Y = 12,338 - 3.749E-05 X1 - 1.431E-07 X2 + 0.012 X3 + 0.347 X4 - 0.005
X5 - 0.0589 X6
It can be seen that the
variables of total cost, length of travel, and length of trip which only have a
partial significant effect on number of visits with a significance value of 5%.
The
quantitative analysis was continued by determining consumer surplus with a total consumer
surplus of IDR. 130,355,024 and willingness
to pay (WTP) of IDR. 136,497. Then, it was continued with the
determination of the economic valuation of the Alam
Indah Beach Tourism Object in 2021 amounting to IDR. 35,508,314,810.
Based on the aforementioned findings, the paper provided a suggestions for the
development of research and further beach tourism object attraction, TCM method
with categorical regression model was practical to estimate the economic value
of beach tourism object. The result of economic value of beach tourism object
could be a reference for managers and local governments to balance between beach
as a preservation with additional income for local governments and societies.
REFERENCES
Arsana, I. (2014). Challenges and opportunities in the delimitation of
Indonesia’s maritime boundaries: a legal and technical approach. Google Scholar
Blackwell,
B. (2007). The value of a recreational beach visit: An application to
Mooloolaba beach and comparisons with other outdoor recreation sites. Economic
Analysis and Policy, 37(1), 77–98. Google Scholar
Dewanta,
A. S. (2010). Valuation of Mount Merapi National Park: A travel cost analysis. Economic
Journal of Emerging Markets, 2(1), 79–88. Google Scholar
Etikan,
I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience
sampling and purposive sampling. American Journal of Theoretical and Applied
Statistics, 5(1), 1–4. Google Scholar
Fatkhussalam,
K. (2013). Perencanaan lanskap pantai alam indah Kota Tegal sebagai kawasan
ekowisata. Google Scholar
Ghozali,
I. (2018). Aplikasi Analisis Multivariate dengan Program IBM SPSS. Yogyakarta:
Universitas Diponegoro. Edisi 9). Semarang: Badan Penerbit Universitas
Diponegoro, 490. Google Scholar
Horne,
L. C. (2019). Public health, public goods, and market failure. Public Health
Ethics, 12(3), 287–292. Google Scholar
Ingram,
J. C., Wilkie, D., Clements, T., McNab, R. B., Nelson, F., Baur, E. H.,
Sachedina, H. T., Peterson, D. D., & Foley, C. A. H. (2014). Evidence of payments
for ecosystem services as a mechanism for supporting biodiversity conservation
and rural livelihoods. Ecosystem Services, 7, 10–21. Scopus
Muttaqin,
M. F., Anggoro, S., & Purwanti, F. (2015). Strategi Pengembangan Wisata
Pesisir di Kelurahan Muarareja Kota Tegal. Management of Aquatic Resources
Journal (MAQUARES), 4(4), 136–145. Google Scholar
Saptutyningsih,
E., & Ningrum, C. M. (2017). Estimasi Nilai Ekonomi Objek Wisata Pantai Goa
Cemara Kabupaten Bantul: Pendekatan Travel Cost Method. BALANCE: Economic,
Business, Management and Accounting Journal, 14(02). Google Scholar
Solikin,
A., Rahman, R. A., Saefrudin, E., Suboh, N., Zahari, N. H., & Wahyudi, E.
(2019). Forest valuation using travel cost method (TCM): Cases of Pahang
National Park and Srengseng Jakarta urban forest. Planning Malaysia, 17. Google Scholar
Sujarweni,
V. W. (2014). Metode Penelitian: Lengkap, praktis, dan mudah dipahami.
Yogyakarta: Pustaka baru press. Google Scholar
Zulpikar,
F., Prasetiyo, D. E., Shelvatis, T. V., Komara, K. K., & Pramudawardhani,
M. (2017). Valuasi ekonomi objek wisata berbasis jasa lingkungan menggunakan
metode biaya perjalanan di Pantai Batu Karas Kabupaten Pangandaran. Journal
of Regional and Rural Development Planning (Jurnal Perencanaan Pembangunan
Wilayah Dan Perdesaan), 1(1), 53–63. Google Scholar
