Jurnal Ekonomi & Studi Pembangunan 
Volume 19, Nomor 2, Oktober 2018, hlm. 142-151 
DOI: 10.18196/jesp.19.2.5005 

GROSS REGIONAL DOMESTIC PRODUCT  
FORECASTS USING TREND ANALYSIS: 

 CASE STUDY OF BANGKA BELITUNG PROVINCE 

Nurmalita Oktaviana1, Nurisqi Amalia2 

Diploma in Applied Economics, Faculty of Economics and Business, Vocational School,  
Universitas Gadjah Mada 

Jl. Notonegoro No.1 Bulaksumur, Yogyakarta 55281 
1nurmalitaoktaviana98@gmail.com 

2nurisqiamalia@gmail.com 

Correspondence E-mail: nurmalitaoktaviana98@gmail.com 

Received: May 2018; Accepted: October 2018 

Abstract: Gross Regional Domestic Product (GRDP) is one of the important indicators to determine 

the economic conditions in the region. This study aims to forecast the Gross Regional Domestic 

Product (GRDP) of the Province of Bangka Belitung Islands which is dominated by tourism sector. 

This forecasting to be expected to give information to formulate a type of policy action that will be 

conducted by decision makers based on GRDP data. GRDP data are from the first quarter of 2010 to 

the fourth quarter of 2017 on the basis of constant prices in 2010. Data sources are obtained from 

Central Bureau of Statistics (BPS) of the Province of Bangka Belitung Islands. The forecasting method 

used is the research is trend analysis. The results of the GRDP forecasting of Bangka Belitung 

Province in the first quarter of 2018 to the fourth quarter of 2022 shows an increasing trend. It can be 

seen from historical data that shows an increasing trend as evidenced from the graph on linear trends. 

The increasing trend in GRDP of the Bangka Belitung Islands Province for the next five years is sup-

ported by government policies that prioritize the tourism sector. Consequently, by prioritizing the 

tourism sector, this will increase economic growth and can reduce GRDP dependency on mining sec-

tor, especially tin that has been continuously decreased. 

Keywords : Forecasting; PDRB; Time Series; Trend Analysis 
JEL Classification: E17, C53, C22 

INTRODUCTION 

The main variable used to determine the 

economic performance of a region is the level of 

economic growth over time (Gregory and Stu-

art, 1992). The value of Gross Domestic Product 

(GDP) at constant prices is used to calculate na-

tional economic growth (Rahardja and Ma-

nurung, 2001). GDP is defined as the value of 

goods and services produced in an area of the 

economy for a certain period of time (Abel, An-

drew B, and Ben S. Bernake, 2001). An economy 

experiences growth if the amount of production 

of goods and services produced increases. A 

high and sustainable level of economic growth 

is a condition that is desired by every country 

and region. High growth rates will improve the 

welfare and prosperity of society as a whole. 

This is indicated by an increase in the availabil-

ity of employment and income. Gross Regional 

Domestic Product (GRDP) is one of the im-

portant indicators for knowing the economic 

conditions in a country in a certain period of 

time. GRDP calculation becomes a very im-

portant part in macroeconomic, especially about 

the economic analysis of a region. The results of 

this GRDP calculation are used as a basis for 

measuring economic activity in an economic 

activity. These GRDP figures are used as mac-



 

Gross Regional Domestic Product Forecasts… (Nurmalita Oktaviana, Nurisqi Amalia)   143 

roeconomic indicators, as a basis for evaluating 

economic performance and formulating various 

policies. Gross Regional Domestic Product 

(GRDP) data is one of the important indicators 

to determine the condition of an area within a 

certain period, based on current prices and on 

the basis of constant prices. The movement of 

GRDP values over time is strongly influenced 

by political factors and government policies. 

GRDP data is based on a certain time sequence 

which is presented on an annual basis (long-

term time series). Based on this indicator, it will 

be obtained an overview of the level of pros-

perity of people in a region. 

The direction of movement of the Gross 

Regional Domestic Product (GRDP) is one of 

the government's ingredients in setting eco-

nomic targets. One way to know the direction of 

movement can be done through forecasting. 

Forecasting is a way of projecting values in the 

past (known value) into the future. The method 

uses both mathematical models and subjective 

estimates. This often causes errors caused by the 

limitations of human abilities (Makridakis, et 

al., 1982). Forecasting is carried out to deter-

mine the movement of a data in the coming pe-

riod as done by Jiang, et al. (2017) forecasting 

the growth of Gross Domestic Product (GDP) in 

China by using dynamic predictors and mixed 

frequency data. Dias, et al. (2015) estimate GDP 

growth before and after the economic and fi-

nancial crisis in Portugal. Portugal's economy 

was badly hit by the crisis. Forecasting is very 

important in assessing relative performance 

during periods of economic crisis. Xu, et al. 

(2018) forecasting using the GP-U-MIDAS 

model which results are very useful for policy-

makers in the US. In the same country, Modis 

(2013) states that GDP growth is considered a 

natural growth process that can be explained by 

the logistical growth equation. The S-shaped 

logistics pattern provides a good description 

and forecast for nominal and real GDP per cap-

ita in the US over the past 80 years. Jansen, et al. 

(2016) conducted a comparison between short-

term estimates of 12 statistical models and pro-

fessional analysis using a set of monthly indi-

cators. The results of these studies contain val-

uable information that can be used to improve 

predictions. Maity and Chatterje (2012) make 

predictions regarding the estimated growth rate 

of Indian GDP by using ARIMA. The results of 

the study indicate that only one period in auto-

regressive and moving averages are statistically 

significant. Furthermore, the absolute value of 

GDP that is estimated to show an increasing 

trend and its growth rate each reveals the oppo-

site trend in the future. Wabomba, et al. (2016) 

conducted research related to Kenya's GDP 

forecasting for the next five years using 

ARIMA. The results of the estimates in the 

sample indicate that the relative values and 

predictions are in the range of 5% and the fore-

casting effect of this model is relatively efficient 

in making Kenya's annual GDP model. In all 

countries, data forecasting is very helpful for 

policymakers in determining the direction of 

their policies (Dovern, 2013). Dovern analyzed 

the frequency of estimated revisions and the 

factors that influenced them. The results show 

that each month on average 40% -50% of the 

forecasting results revise its estimates. Revision 

depends on a number of factors, such as pre-

dictions, business cycles, or strategic interac-

tions between forecast. GDP forecasting analy-

sis is also carried out in various studies in Indo-

nesia. Satria, et al. (2015) forecast GDP and in-

flation in Indonesia. The projection results for 

the next five years show that 64% of models can 

explain variables well. 

Forecasting Gross Regional Domestic 

Product (GRDP) of the Province of Bangka Be-

litung Islands by using the Trend is expected to 

provide an overview of economic growth and 

economic performance in the Province of 

Bangka Belitung Islands. Forecasting results can 

also be used as a basis for determining the 

amount of the budget and controlling costs. 

Forecasting of GDP of Bangka Belitung Prov-

ince for 5 years from the first quarter of 2018 to 

the fourth quarter of 2022 was carried out to 

support the RPJMD of Bangka Belitung Prov-



 

144   Jurnal Ekonomi & Studi Pembangunan Vol. 19, No. 2, Oktober 2018: 142-151 

ince and to look at strategic issues and govern-

ment policies focusing on economic growth. If 

forecasting is not done on GRDP for the future, 

the government will have difficulty predicting 

the possibilities in the future and not being right 

on target in determining or making policies. 

This study aims to determine the value or 

forecasting of Bangka Belitung Province's Gross 

Regional Domestic Product (GRDP) data for 

preventing actions related to the type of policy 

that will be made by the decision maker (deci-

sion making). Research with the trend method 

predicts the GRDP of the Province of Bangka 

Belitung for the next 5 years, namely the first 

quarter of 2018 to the fourth quarter of 2022. 

The results show an increasing trend. This is not 

in line with research from Utama, et al. (2014). 

Forecasting the Gross Regional Domestic Pro-

duct (PDRB) of Bali Province with the Box-Jen-

kins method which shows fluctuating results. 

This is because quarterly data is quite influ-

enced by seasonal factors, such as tourist visits, 

weather and also the realization and develop-

ment budget of the government. 

The trend is a movement (tendency) up or 

down in the long run, which is obtained from 

the average change over time. The average 

change can increase and can reduce. If the aver-

age change increases, it is called a positive trend 

or trend that has an upward tendency. Con-

versely, if the average change is reduced, it is 

called a negative trend or a trend that has a 

downward trend. Trend illustrates the progress 

of time series data over a long period of time 

and has an ascending or descending tendency. 

This motion reflects the nature of continuity or a 

continuous state from time to time over a period 

of time. Because of the nature of this continuity, 

the trend is considered a stable movement so 

that in interpreting it can use a mathematical 

model. The trend can be either a straight line 

(regression/linear trend) or not straight (regres-

sion/non-linear trend) (Dajan, 1986). The ad-

vantage of the trend is that the forecast results 

are close to the actual value and are very good 

for long-term data while the weakness of this 

method is not suitable for short-term data use 

(Dajan, 1986). 

RESEARCH METHOD 

Data 

The data used in this study are time se-

ries. Time series is a series of time will be 

known whether the events, events, symptoms, 

and variables observed are developing follow-

ing regular patterns of development (Hanke, et 

al., 2003). The data used is the GRDP data of the 

Bangka Belitung Islands Province. This data is 

sourced from the Central Bureau of Statistics of 

the Province of Bangka Belitung Islands with a 

time period from the first quarter of 2010 to the 

fourth quarter of 2017. This type of data is sec-

ondary data because the data used is not ob-

tained directly by the author.  

Data Analysis  

Data analysis is done using the trend 

method. Trend analysis is a long-term upward 

or downward trend from the average change 

over time. The data used is GRDP data in the 

quarter. The software used to process data is 

SPSS 20. Software According to Makridakis, et 

al. (1999) the selection of trend forecasting mo-

dels can be done by comparing the coefficient of 

determination (R2). The coefficient of determi-

nation (R2) measures how far the ability of the 

model in explaining the variation of the de-

pendent variable and measuring the accuracy of 

a regression line applied to a group of research 

data. In general, the value of R2 can be defined 

as follows: 

R2 = 
  ∑       

 

  ∑       
 
 

Where: 

𝑡  = time / period 

Y_t  = time series value in the 𝑡 period 

Ŷ_t  = forecast value in the 𝑡 period 

Ȳ  = average time series value. The greater 

the R2 value, the better the model / exact model 

obtained, which is about 0.7 to 1. 

The equation formula in the trend method is as 

follows: 

Y  = a + bx + e 



 

Gross Regional Domestic Product Forecasts… (Nurmalita Oktaviana, Nurisqi Amalia)   145 

Where: 

Y  = GRDP 

a  = constant 

bx  = PDRBt-1 

e  = error 

RESULTS AND DISCUSSION 

GRDP of Bangka Belitung Province on the 

basis of constant prices in 2010 per quarter 

during the first quarter of 2010 to the fourth 

quarter of 2017 experienced an increase as pre-

sented in Table 1. This shows that the data 

GRDP Bangka Belitung on a quarterly basis 

during 2010 - 2017 experienced positive growth. 

 

The data above is GRDP data that starts 

from 2010 in the first quarter and ends in 2017 

quarter IV. In this data, it is known that the 

GRDP of Bangka Belitung Province in the pe-

riod of 2010 in the first quarter to 2017 in the 

fourth quarter has increased, can be seen from 

the highest GRDP in the fourth quarter of 2017 

with a total of 12,672,009.37. 

Output Results of GRDP data of Bangka Beli-

tung Province using SPSS 20 software in figure 

2 and figure 2. 

Table 1. Bangka Belitung Province's Gross Regional Domestic Product 
Based on Constant Prices in 2010, from Quarter I of 2010 to Quarter IV of 2017 

No Year, (quarterly) 
GRDP  

(million rupiah) 
Growth (%) 

1 2010.1 8,592,658.23 
 

2 2010.2 8,808,349.94 0.025101861 
3 2010.3 9,018,196.28 0.02382357 
4 2010.4 9,142,699.72 0.013805803 
5 2011.1 9,134,044.11 -0.000946724 
6 2011.2 9,478,236.39 0.037682354 
7 2011.3 9,716,970.73 0.025187633 
8 2011.4 9,684,739.07 -0.003317048 
9 2012.1 9,689,450.06 0.000486434 
10 2012.2 9,962,560.97 0.02818642 
11 2012.3 10,144,330.25 0.018245236 
12 2012.4 10,308,564.86 0.016189793 
13 2013.1 10,309,220.80 6.36306E-05 
14 2013.2 10,508,920.08 0.019370938 
15 2013.3 10,628,591.25 0.01138758 
16 2013.4 10,744,124.96 0.010870087 
17 2014.1 10,756,992.97 0.001197679 
18 2014.2 11,017,029.10 0.024173682 
19 2014.3 11,136,268.84 0.010823221 
20 2014.4 11,249,148.61 0.010136229 
21 2015.1 11,197,460.36 -0.004594859 
22 2015.2 11,455,280.50 0.023024876 
23 2015.3 11,580,681.87 0.010947036 
24 2015.4 11,728,881.25 0.01279712 
25 2016.1 11,582,514.86 -0.012479143 
26 2016.2 11,895,095.64 0.026987298 
27 2016.3 12,063,444.17 0.014152768 
28 2016.4 12,309,765.99 0.020418864 
29 2017.1 12,326,039.26 0.00132198 
30 2017.2 12,511,643.00 0.015057857 
31 2017.3 12,498,059.28 -0.001085686 
32 2017.4 12,672,009.37 0.013918168 

Source: Author estimation (2018), data are obtained from BPS Bangka Belitung Province. 



 

146   Jurnal Ekonomi & Studi Pembangunan Vol. 19, No. 2, Oktober 2018: 142-151 

Table 2. Summary Output 

Model 
Summary 

R R Square Adjusted R Square 
Std. The error of the Esti-
mate 

1 .997a .994 .994 96221.64555 

a. Predictors: (Constant). Quarterly 

Source: Author estimation (2018) 

Table 3. Coefficients Regression 

Coefficientsa 
Model Unstandardized Coefficients Standardized 

Coefficients 
T Sig. 

B Std. Error Beta 

1 
(Constant) 8648738.545 34832.819  248.293 .000 
PDRBt-1 127068.824 1842.259 .997 68.974 .000 

a. Dependent Variable: PDRB 

Source: Author estimation (2018) 

Based on table 2 above it is known that 

the R-value of 0.997 which is closed to 1 means 

that between the independent variable, in this 

case, the GRDP of the previous year has a close 

relationship with the dependent variable 

GRDP.  The value of R-Square is 0.994 which 

means the variation of GRDP can be explained 

by the time period of the transaction of 0.994 or 

99.4% and the remaining 0.6% is explained by 

other variables outside the model. 

Linear Trend Method will be used to fore-

cast GRDP data in Bangka Belitung Province for 

the next five years quarterly using SPSS 20. 

Software Based on the regression coefficient 

table above it can be seen that the significant 

value <alpha means that the independent vari-

able (PDRBt-1) affects the dependent variable 

(GRDP). 

Table 4 shows the forecasting data of the 

Gross Regional Domestic Product of the Bangka 

Belitung Islands Province in 2018 in the first 

quarter to 2022 in the fourth quarter along with 

growth in percent for the first quarter to the 

fourth quarter of each year.  

Table 4. Results of Forecasting the Gross Regional Domestic Product of Bangka Belitung 
Province According to Constant Prices in 2010, from Quarter I of 2018 to Quarter IV of 2022 

No Year, Quarterly PDRB (million rupiahs) Growth (%) 

1 2018.1 12,842,009.74 
 

2 2018.2 12,969,078.56 0.009894777 
3 2018.3 13,096,147.39 0.009797830 
4 2018.4 13,223,216.21 0.009702763 
5 2019.1 13,350,285.03 0.009609524 
6 2019.2 13,477,353.86 0.009518061 
7 2019.3 13,604,422.68 0.009428321 
8 2019.4 13,731,491.50 0.009340258 
9 2020.1 13,858,560.33 0.009253826 
10 2020.2 13,985,629.15 0.009168977 
11 2020.3 14,112,697.98 0.009085671 
12 2020.4 14,239,766.80 0.009003864 
13 2021.1 14,366,835.63 0.008923519 
14 2021.2 14,493,904.45 0.008844593 
15 2021.3 14,620,973.27 0.008767052 
16 2021.4 14,748,042.10 0.008690860 
17 2022.1 14,875,110.92 0.008615979 
18 2022.2 15,002,179.75 0.008542379 
19 2022.3 15,129,248.57 0.008470024 
20 2022.4 15,256,317.39 0.008398885 

Source: Author estimation 



 

Gross Regional Domestic Product Forecasts… (Nurmalita Oktaviana, Nurisqi Amalia)   147 

Figure 1. Province PDRB Forecasting Results of Bangka Belitung  
Year 2018 Quarter I - 2011 Quarter IV 

Source: Author estimation (2018)

Based on the table, it is known that the results 

of the forecasting of the Gross Regional Do-

mestic Product of the Bangka Belitung Islands 

Province from 2018 quarter I up to 2022 quarter 

IV using the trend method experienced an in-

crease seen from the table of GRDP. 

The forecasting results of the Gross Re-

gional Domestic Product of the Bangka Belitung 

Islands Province are used as an illustration to 

see the possibilities that occur in the future. 

With the prediction about the possibilities that 

occur in the coming period, the government has 

a reference for making policies. 

Figure 1 shows the results of forecasting 

the Gross Regional Domestic Product (GRDP) 

of the Bangka Belitung Islands Province in 2018 

the first quarter to 2022 the fourth quarter 

shows an increasing trend. Bangka Belitung 

Province is one of the provinces in Indonesia 

which has mining natural wealth (Sujitno, 

2007). Statistical data from the United States 

Bureau of Mines (USBM) records that Malaysia 

has tin mining reserve a number 1 (one)   and 

followed by Indonesia which has tin mining 

reserves around 800,000 tons. This has the po-

tential to increase foreign exchange for the na-

tional economy in Indonesia. 

The mining sector is very influential on 

GRDP growth in Bangka Belitung Province. 

The sector is able to contribute around 13% to 

the provincial economy with its main commo-

dity being tin mining. The economic growth of 

the Bangka Belitung Islands Province continues 

to experience a slowdown during the period 

2011 - 2014. The economic growth of the Bangka 

Belitung Islands Province is slowing down due 

to the decline in the mining sector. Tin mining 

is the main product of natural resources in 

Bangka Belitung Province. The decline in the 

contribution from the mining sector was due to 

lower demand from the tin metal industry. In 

addition to declining demand, the selling price 

also decreased. 

With the increasingly intense issue of en-

vironmental damage as a result of mining ac-

tivities, mining permits are increasingly tight-

ened. Bangka Belitung Islands Province has a 

damaged and very critical land condition com-

pared to other provinces, reaching 

Rp1,053,253.19 hectares or 62% of the land area 

of the Bangka Belitung Islands Province 

(WALHI Nasional - WALHI Bangka Belitung 

Islands). The diminishing supply of tin com-

modities requires the alternative sector to sup-

port the economy of the Bangka Belitung Is-

lands Province. To reduce the dependence of 

the Bangka Belitung Province on the mining 

sector, the government is trying to improve the 

tourism sector to increase economic growth be-

cause tourism potential in the Province of 

Bangka Belitung Islands is very large. The gov-

ernment's efforts have been stated in the 

RPJMD of the Bangka Belitung Islands Province 

in 2017-2022 and also become one of the gov-

ernment's priority programs. There are 11 pri-

ority programs that will be implemented in the 

Regional Development Program 2017-2022. 

There are 3 (three) main priorities that are of 

particular concern, namely the development of 

pepper, tourism and mining management that 

pay attention to the environment. 



 

148   Jurnal Ekonomi & Studi Pembangunan Vol. 19, No. 2, Oktober 2018: 142-151 

In the Province of Bangka Belitung Is-

lands tourism development is one of the activi-

ties that in recent years have been carried out 

more seriously than in previous years. Tourism 

has even been designated as one of the leading 

sectors in the face of the post-tin Bangka Beli-

tung Islands. After more than three centuries 

tin was mined in Bangka Belitung and became 

the backbone of the regional economy then tin 

mining is estimated to be entering a "sunset in-

dustry" so that other sectors are needed as an 

alternative, namely tourism and marine fisher-

ies. To encourage the acceleration of tourism 

development, the provincial government of the 

Bangka Belitung Islands launched the Tourism 

Visit Year program to the 2010 Bangka Belitung 

Islands called the Visit Bangka Belitung Archi-

pelago 2010, abbreviated as Visit Babel Archi 

(VBA) 2010. The program, which began with 

the launching of the Minister of Culture and 

Tourism in Belitung at the end of 2009, contains 

a number of agenda activities in Bangka Beli-

tung which are expected to attract tourists to 

visit. Likewise in 2011, an international national 

scale event was held, namely Sail Wakatobi Be-

litong (SWB) in 2011, where participants who 

participated in this activity were yachter fans 

from various countries. Tourism development 

in a country will encourage and accelerate eco-

nomic growth. In the framework of regional 

development, the tourism sector plays a deci-

sive role and can be a catalyst to gradually im-

prove the development of other sectors. The 

success of the development of the tourism sec-

tor means that it will increase its role in re-

gional revenues, where tourism is the main 

component (Pleanggra and Yusuf, 2003). The 

tourism sector is one sector that has a contribu-

tion in the employment absorption. The arrival 

of tourism to an area will open opportunities 

for the community to open a business so that 

the opportunity will provide an opportunity for 

the local community to work so that the com-

munity will earn income from the work (Pro-

jogo, 1976). Tourism is one of several major in-

dustries in the world, with a contribution to the 

total value of the world's Gross Domestic Pro-

duct (GDP) reaching 9% and the number of 

workers absorbed in this industry reaching 200 

million people (UNWTO. Sustainable Tourism 

for Development Guidebook Madrid, Spain: 

World). Notes from the World Travel Orga-

nization (WTO) indicate that within the next 

few decades, tourism as an industry will de-

velop at a rate that exceeds other types of in-

dustries. In 2012, the number of tourist trips to 

various world tourist destinations reached 1,035 

billion; a growth of 5.6% per year. A number of 

research conducted in various world tourism 

destinations, generally indicates that tourism as 

an industry will provide positive benefits to the 

regional economy and the potential for negative 

benefits on the local socio-cultural and envi-

ronmental dimensions (Sebele, 2010; Aref, 

2011). Tourism can open employment opportu-

nities, improve community welfare, create 

business opportunities and entrepreneurs in 

basic industries and support tourism. On the 

different side, tourism has a tendency to exploit 

natural resources and the cultural potential of 

local communities (Untong et al., 2010). The 

Province of Bangka Belitung Islands has an 

amazing natural beauty of the beach, besides 

that, it also has a variety of tourist attractions 

such as water tourism. Nature tourism, cultural 

tourism, and special interest tours. This diver-

sity can provide an alternative choice of travel 

that is more varied for tourists. The visit of local 

tourists and foreign tourists to Bangka Belitung 

has quite favorable prospects because it greatly 

helps the improvement of the community's 

economy. Efforts to develop the tourism sector 

in the Bangka Belitung Islands are progressing 

as seen from the increasing number of tourism 

facilities, transportation, communication and 

the increasing number of tourists. However, 

faster acceleration is needed to spur the prepa-

ration of the tourism sector in the Bangka Beli-

tung Islands, because mines, especially tin and 

minerals will surely run out.  

 



 

Gross Regional Domestic Product Forecasts… (Nurmalita Oktaviana, Nurisqi Amalia)   149 

Table 5. Data of Foreign Tourist Visits and Archipelago Tourists to Tourism Objects 
 in Bangka Belitung Islands Province in 2011-2014. 

Year Target Realization 

 Domestic 
Tourists 
 

International 
Tourists 

Total Tourists % International 
tourists 

% Number % 

2006 75,131 2,324 77,455 67,935 90.42 447 19.23 68,328 88.29 
2007 77,686 2,403 80,089 62,271 80.16 148 6.16 62,419 77.94 
2008 80,192 4,221 84,413 78,700 98.14 380 9.00 79,080 93.68 
2009 84,523 4,449 88,972 93,816 111.00 622 13.98 94,438 106.14 
2010 89,087 4,689 93,776 136.022 152.68 701 14.95 136,723 145.80 

Source: Author estimation, data obtained from BPS of the Bangka Belitung Province and the RIPPDA of 

Bangka Belitung Island Province.  

In other words, the tourism sector as a leading 

sector in the region must be prepared more 

quickly before the end of the role of tin mining. 

Based on Table 5 can be seen the number 

of tourist visits in the Bangka Belitung Islands. 

In 2007 there was a decrease in the number of 

tourist visits, but for the following years, an 

increase occurred. If referring to the tourist tar-

gets listed in the Regional Tourism Develop-

ment Master Plan (RIPPDA) document, the tar-

get number of domestic tourist visits can be 

said to have been exceeded, but the target 

number of foreign tourist arrivals is still not 

reached. The target of tourists in RIPPDA who 

came to Bangka Belitung Islands in 2010 was 

93,776 people divided into domestic tourists as 

many as 89,087 people and foreign tourists as 

many as 4,689 people. Of these targets, the 

achievements in 2010 were for domestic tourists 

136,022 people or 152.6% of the target while 

number of foreign tourist only 701 people or 

only 14.95% of the target in RIPPDA. If tourist 

visits in the Bangka Belitung Islands Province 

continue to increase, it does not rule out the 

possibility that the tourism sector can replace 

the contribution to the GRDP of the Bangka 

Belitung Islands Province. 

Figure 2 shows the results of the figure 

where the blue line is the observation data of 

the Gross Regional Domestic Product (GRDP) 

of Bangka Belitung Province in the period of 

2010 I quarter to 2017 IV quarter, and the green 

line is the result of GRDP forecasting 2018 - 

2022.  

 
Figure 2. Figure of GRDP Forecasting Results 

in Bangka Belitung Province 
Source: Data Processed 

It is known that green lines (forecasting lines) 

follow the rising pattern of the blue lines or 

observation data. 

Based on the data above, then the re-

searchers do the next step, namely the data pro-

cessing process with the help of SPSS 20 soft-

ware. Before making a forecast, it must be 

known in advance the type of data pattern 

used. The results of the plot of data processed 

with SPSS 20 software are shown in Figure 3. 

Figure 3. Linear Trend Charts and Gross 

GRDP Forecasting  

in Bangka Belitung Province 

Source: Data Processed 



 

150   Jurnal Ekonomi & Studi Pembangunan Vol. 19, No. 2, Oktober 2018: 142-151 

In figure 3 above, it can be seen that the 

GRDP data of Bangka Belitung Province has an 

upward trend pattern which means that it has 

experienced an increase in GRDP in 2010 in the 

first quarter to 2022 quarter IV. Observation 

data from PDRB in the first quarter of 2010 to 

the fourth quarter of 2017 shows an upward 

pattern which is depicted with a red line. For 

the blue line is the result of GRDP forecasting in 

2018 in the first quarter up to the fourth quarter 

of 2022. Both lines have an uptrend pattern 

which means that the forecasting data follows 

the pattern of observation data, which is in-

creasing every quarter of the year. 

CONCLUSION 

Forecast results using a trend have a type 

of data that has an up-trend pattern, meaning 

that the GRDP of the Bangka Belitung Islands 

Province for the next five years, namely from 

2018 in the first quarter and ending in the fore-

cast period of the fourth quarter of 2022, has 

increased. The increase in GRDP of the Bangka 

Belitung Islands Province for the next five years 

is supported by government policies that prior-

itize the tourism sector. Consequently, by pri-

oritizing the tourism sector, this will increase 

economic growth and can reduce GRDP de-

pendency on mining sector, especially tin that 

has been continuously decreased. 

REFERENCES 

Abel, A.B., and Bernake, B.S. (2001). Macroeco-
nomics. New York: Addison Wesley 
Longman. Inc. 

Aref, F. (2011). The Effects of Tourism on 
Quality of Life: A Case Study of Shiraz. 
Iran. Life Science Journal, 8(2), 26-30. 

Badan Perencanaan Pembangunan Daerah. 
(2018). Rencana Pembangunan Jangka 
Menengah Daerah (RPJMD) Provinsi 
Bangka Belitung Tahun 2017-2022. (Online) 
Belitung. Available from 
http://bappeda.babelprov.go.id (Ac-
cessed: 29th March, 2018). 

Badan Pusat Statistik Provinsi Bangka Belitung. 
(2018). PDRB Triwulanan Provinsi 
Kepulauan Bangka   Belitung Atas Dasar 
Harga Konstan Menurut Lapangan Usaha 
(Million Rupiahs) 2010-2017. (Online) Ja-
karta. Available from 
https://babel.bps.go.id/ (Accessed: 29th, 
March 2018). 

Bangka Belitung Provinsi. (2018). Pertumbuhan 
Ekonomi Tantangan Terbesar Provinsi 
Bangka    Belitung. (Online) Belitung. 
Available from http://babelprov.go.id 
(Accessed: 29th, March 2018). 

Bappenas. (2018). Sistem Informasi dan Mana-
jemen Data Dasar Regional. Analisis Pem-
bangunan Wilayah Provinsi Bangka Beli-
tung. (Online) Jakarta. Available from 
http://simreg.bappenas.go.id. (Accessed: 
29th, March 2018). 

Dajan, A. (1986). Pengantar Metode Statistik. 
Jilid I. Jakarta: LP3ES. 

Dias, F., Pinheiro, M., and Rua, A. (2015). Fore-
casting Portuguese GDP with factor 
models: Pre- and post-crisis evidence. 
Economic Modelling 44, 266-272. 

Dovern, J. (2013). When are GDP forecasts     
updated? Evidence from a large interna-
tional panel. Economics Letters 120, 521-
524. 

Gregory, P.R., and Stuart, R.C. (1992). Compar-
ative Economic’System. Fourth Edition. 
New Jersey: Houghton Mifflin Com-
pany. 

Hanke, John, et.al. (2003). Peramalan Bisnis. 
Jakarta: Pearson Education Asia Ptc. Ltd 
dan PT. Pren Hallindo. 

Jansen, W.J., Jin, X., and de Winter., J.M. (2016). 
Forecasting and nowcasting real GDP: 
Comparing statistical models and sub-
jective forecasts. International Journal of 
Forecasting 32, 411-436. 

Jiang, Y., Guo, Y., and Zhang, Y. (2017). Fore-
casting China’s GDP growth using      
dynamic factors and mixed-frequency 
data. Economic Modelling 66, 132-138. 

Makridakis, S., Andersen, A., Carbone, R.,     
Fildes, R., Hibon, M., Lewandowski, R., 
Newton, J., Parzen, E. and Winkler, R.. 
(1982) The accuracy of extrapolation 
(time series) methods: Results of a fore-

http://bappeda.babelprov.go.id/
https://babel.bps.go.id/
http://babelprov.go.id/
http://simreg.bappenas.go.id/


 

Gross Regional Domestic Product Forecasts… (Nurmalita Oktaviana, Nurisqi Amalia)   151 

casting competition. Journal of the Royal 
Statistical Society. Series D (The Statisti-
cian), 34(2), 261-262. 

Maity, B., and Chatterjee, B. (2012). Forecasting 
GDP growth rates of India: An empiri-
cal study. International Journal of Eco-
nomics and Management Sciences, 1(9), 52-
58. 

Modis, T. (2013). Long-term GDP forecasts and 
the prospects for growth. Technological 
Forecasting & Social Change 80, 1557-
1562. 

Pleanggra, F. and Yusuf, E.A. (2012). Analisis 
Pengaruh Jumlah Obyek Wisata, 
Jumlah Wisatawan dan Pendapatan 
Perkapita Terhadap Pendapatan 
Retribusi Obyek Pariwisata 35 
Kabupaten/ Kota di Jawa Tengah 
Doctoral dissertation. Fakultas 
Ekonomika dan Bisnis. Universitas 
Diponegoro. 

Projogo, M.J. (1976): Pengantar Pariwisata 
Indonesia; Jakarta: Direktorat Jenderal 
Pariwisata. 

Rahardja, P., and Manurung, M. (2001). Teori 
Ekonomi Makro: Suatu Pengantar.      
Jakarta: LP FE Universitas Indonesia. 

Sebele, L.S. (2010). Community-Based Tourism 
Ventures. Benefits and Challenges: 
Khama Rhino Sanctuary Trust. Central 
District. Botswana. Tourism Manage-
ment, 31, 136-146. 

Satria, I., Yasin, H., and Suparti. (2015). 
Proyeksi Data Produk Domestik Bruto 
(PDB) dan Foreign Direct Investment 
(FDI) Menggunakan Vector Autoregres-
sive (VAR). Jurnal Gaussian, 4(4), 895-
905. 

Sujitno, S. (2007). Dampak Kehadiran Timah 
Indonesia Sepanjang Sejarah Pada 
Aspek Politik Ekonomi Sosial Budaya. 
Jakarta: Cempaka Publishing. 

Utama, M.S. and Wirawan, I.G.P.N. (2014). 
Model Box-Jenkins Dalam Rangka    
Peramalan Produk Domestik Regional 
Bruto Provinsi Bali. Universitas Uda-
yana. Jurnal Buletin Studi Ekonomi, 19(1), 
92-104. 

Untong, A., Kaosa-ard, M., Ramos, V., Sangka-
korn, K., and Rey-Maquieria, J. (2010). 

Factors Influencing Local Resident   
Support for Tourism Development: A 
Structural Equation Model. The APTA 
Conference 2010. Macau. 

UNWTO. (2013). Sustainable Tourism for      
Development Guidebook. Madrid. 
Spain: World Tourism Organization. 

Wabomba, M.S., Mutwiri, M.P., and Frederick, 
F. (2016). Modeling and Forecasting 
Kenyan GDP Using Autoregressive    
Integrated Moving Average (ARIMA). 
Science Journal of Applied Mathematics and 
Statistics, 4(2), 64-73. 

Xu. Q., Zhuo, X., Jiang, C., Liu, X. and Liu, Y. 
(2018). Group penalized unrestricted 
mixed data sampling model with appli-
cation to forecasting US GDP growth. 
Economic Modelling 75, 221-236.