The prediction system that has been made is a system that can help provide forecasts of tourism ticket sales in the future. The system is web-based so that it can be easier to use. The importance of analysis and modeling in problems in the development of prediction systems, To provide references and instructions to developers in making the system. This research proposes the modeling stages in designing the system, including problem identification, data collection, literature study, analysis, design, development, and evaluation. It is done to collect information related to the problem with interview and observation techniques, then collect tourism data on all tourism in Bangkalan district and collect literature studies related to the least square method. The error test on the least fair method is carried out using the error test level method (error) using MAD and MAPE. Tests are carried out to find out the nominal error value, so that it can be used as a value recommendation in forecasting for the next period. The calculation result of MAD from this research is 402.16, while the calculation result from MAPE is 10.54. The result, the breakdown of system requirements at the analysis stage and the design system using a use case diagram. And finally, a web-based prediction system has been built based on the locations that have been passed.

1.
M. A.
Camilleri
, “
The Tourism Industry: An Overview BT - Travel Marketing, Tourism Economics and the Airline Product: An Introduction to Theory and Practice
,”
M. A.
Camilleri
, Ed.
Cham
:
Springer International Publishing
,
2018
, pp.
3
27
.
2.
F.
Higgins-Desbiolles
, “
Sustainable tourism: Sustaining tourism or something more?
,”
Tour. Manag. Perspect.
, vol.
25
, pp.
157
160
,
2018
, doi: .
3.
F.
Higgins-Desbiolles
,
S.
Carnicelli
,
C.
Krolikowski
,
G.
Wijesinghe
, and
K.
Boluk
, “
Degrowing tourism: rethinking tourism
,”
J. Sustain. Tour.
, vol.
27x
, no.
12
, pp.
1926
1944
, Dec.
2019
, doi: .
4.
R.
Scheyvens
and
R.
Biddulph
, “
Inclusive tourism development
,”
Tour. Geogr.
, vol.
20
, no.
4
, pp.
589
609
, Aug.
2018
, doi: .
5.
D.
Buhalis
, “
Technology in tourism-from information communication technologies to eTourism and smart tourism towards ambient intelligence tourism: a perspective article
,”
Tour. Rev.
, vol.
75
, no.
1
, pp.
267
272
, Jan.
2020
, doi: .
6.
A.
Jauhari
,
F. A.
Mufarroha
,
M.
Rofi’
,
M. F.
Nasrullah
,
Fitriyah
, and
K.
Nisa
, “
The Development of Smart Travel Guide Application in Madura Tourism
,” vol.
473
, no. Icss, pp.
771
776
,
2020
, doi: .
7.
S. K.
Viswanath
,
C.
Yuen
,
X.
Ku
, and
X.
Liu
, “
Smart Tourist - Passive Mobility Tracking Through Mobile Application BT - Internet of Things. IoT Infrastructures
,”
2015
, pp.
183
191
.
8.
M.
Afzaal
,
M.
Usman
, and
A.
Fong
, “
Tourism Mobile App With Aspect-Based Sentiment Classification Framework for Tourist Reviews
,”
IEEE Trans. Consum. Electron.
, vol.
65
, no.
2
, pp.
233
242
,
2019
, doi: .
9.
R.
Safitri
,
D. S.
Yusra
,
D.
Hermawan
,
E.
Ripmiatin
, and
W.
Pradani
, “
Mobile tourism application using augmented reality
,” in
2017 5th International Conference on Cyber and IT Service Management (CITSM)
,
2017
, pp.
1
6
, doi: .
10.
P. F.
Wilkinson
, “
Predictions, past and present: World and Caribbean tourism
,”
Futures
, vol.
41
, no.
6
, pp.
377
386
,
2009
, doi: .
11.
O. L. V
Costa
,
C. de Oliveira
Ribeiro
,
L. L.
Ho
,
E. E.
Rego
,
V.
Parente
, and
J.
Toro
, “
A robust least square approach for forecasting models: an application to Brazil's natural gas demand
,”
Energy Syst.
, vol.
11
, no.
4
, pp.
1111
1135
,
2020
, doi: .
12.
U.
Khair
,
H.
Fahmi
,
S.
Al Hakim
, and
R.
Rahim
, “
Forecasting Error Calculation with Mean Absolute Deviation and Mean Absolute Percentage Error
,”
J. Phys. Conf. Ser.
, vol.
930
, p.
12002
,
2017
, doi: .
13.
X.
Qiu
,
L.
Zhang
,
P. Nagaratnam
Suganthan
, and
G. A. J.
Amaratunga
, “
Oblique random forest ensemble via Least Square Estimation for time series forecasting
,”
Inf. Sci. (Ny).
, vol.
420
, pp.
249
262
,
2017
, doi: .
14.
B. D.
Liengaard
 et al., “
Prediction: Coveted, Yet Forsaken? Introducing a Cross-Validated Predictive Ability Test in Partial Least Squares Path Modeling
,”
Decis. Sci.
, vol.
52
, no.
2
, pp.
362
392
, Apr.
2021
, doi: .
15.
F.
Yang
,
M.
Li
,
A.
Huang
, and
J.
Li
, “
Forecasting time series with genetic programming based on least square method
,”
J. Syst. Sci. Complex.
, vol.
27
, no.
1
, pp.
117
129
,
2014
, doi: .
16.
N.
Dengen
,
Haviluddin
,
L.
Andriyani
,
M.
Wati
,
E.
Budiman
, and
F.
Alameka
, “
Medicine Stock Forecasting Using Least Square Method
,” in
2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT)
,
2018
, pp.
100
103
, doi: .
17.
C.
Sianturi
,
E.
Ardini
, and
N.
Sembiring
, “
SALES FORECASTING INFORMATION SYSTEM USING THE LEAST SQUARE METHOD IN WINDI MEBEL
,”
J. Inov. Penelit.
, vol.
1
, no.
2
SE-Articles, Jun.
2020
, doi: .
18.
P. G.
Roetzel
, “
Information overload in the information age: a review of the literature from business administration, business psychology, and related disciplines with a bibliometric approach and framework development
,”
Bus. Res.
, vol.
12
, no.
2
, pp.
479
522
,
2019
, doi: .
19.
F.
Zulfa
,
D. O.
Siahaan
,
R.
Fauzan
, and
E.
Triandini
, “
Inter-Structure and Intra-Structure Similarity of Use Case Diagram using Greedy Graph Edit Distance
,” in
2020 2nd International Conference on Cybernetics and Intelligent System (ICORIS)
,
2020
, pp.
1
6
, doi: .
20.
R.
Fauzan
,
D.
Siahaan
,
S.
Rochimah
, and
E.
Triandini
, “
Use Case Diagram Similarity Measurement: A New Approach
,” in
2019 12th International Conference on Information & Communication Technology and System (ICTS)
,
2019
, pp.
3
7
, doi: .
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