This study aims to examine the accuracy of the time-invariant fuzzy method in forecasting the number of ship passengers at the port of Makassar. Forecasting helps a company establish the number of passengers to come, anticipate their increase, and provide policies for addressing the problem. The Time-Invariant Fuzzy, whose relationship does not depend on time t, was used by utilizing fuzzy sets as historical data. In general, this method has two essential aspects, including the use of historical or actual data and relations. The data used was obtained from the number of ship passengers at Makassar Port from January 2012 to December 2020. The results showed that the Mean Average Percentage Error (MAPE) value in the training and the testing data is 7.9% and 6.8%, respectively. This means that the method is very accurate in predicting ship passengers at Makassar Port.

Topics
Water transportation
1.
A.A.
 
Putra
.
J.M.
 
Yamin
.
B.
 
Riyanto
. and
A.T.
 
Mulyono
,
Int. Ref. J. Eng. Sci.
 
3
.
38
(
2014
).
Google Scholar
 
2.
S.
 
Dewa
.
M.S.
 
Pallu
.
M.I.
 
Ramli
. and
M.A.
 
Djabbar
,
Int. J. Eng. Res. Technol.
 
3
.
822
(
2014
).
Google Scholar
 
3.
M.Y.
 
Jinca
.
Indonesian Sea Transportation: Systems Analysis & Case Studies
 
Brilian Internasional
.
Surabaya
. (
2006
).
Google Scholar
 
4.
V.
 
Fitria
 and
S.
 
Anwar
,
E-Jurnal Ekon. Dan Bisnis Univ. Udayana
 
9
.
23
(
2020
).
https://doi.org/10.24843/EEB.2020.v09.i01.p02
Google Scholar
 
5.
V.A.
 
Fitria
 and
R.
 
Hartono
,
J. Ilm. Teknol. Inf. Asia
 
11
.
15
(
2017
).
Google Scholar
 
6.
M.
 
Bose
 and
K.
 
Mali
,
Int. J. Approx. Reason.
 
111
.
78
(
2019
).
https://doi.org/10.1016/j.ijar.2019.05.002
Google Scholar
 
7.
E.
 
Egrioglu
.
C.H.
 
Aladag
. and
U.
 
Yolcu
,
Expert Syst. Appl.
 
40
.
854
(
2013
).
https://doi.org/10.1016/j.eswa.2012.05.040
Google Scholar
 
8.
M.A.E.
 
Alqatqat
 and
M.T.
 
Feng
,
Am. J. Math. Stat.
 
10
.
79
(
2020
).
Google Scholar
 
9.
S.R.
 
Binaiya
.
M.N.
 
Hayati
. and
I.
 
Purnamasari
,
Eksponensial
 
10
.
175
(
2019
).
Google Scholar
 
10.
M.
 
Muhammad
,
Mat. J.
 
3
.
48
(
2016
).
Google Scholar
 
11.
H.J.
 
Sadaei
.
F.G.
 
Guimarães
.
C.
 
José da Silva
.
M.H.
 
Lee
. and
T.
 
Eslami
,
Int. J. Approx. Reason.
 
83
.
196
(
2017
).
https://doi.org/10.1016/j.ijar.2017.01.006
Google Scholar
 
12.
S.M.
 
Chen
.
X.Y.
 
Zou
. and
G.C.
 
Gunawan
,
Inf. Sci. (Ny)
.
500
.
127
(
2019
).
https://doi.org/10.1016/j.ins.2019.05.047
Google Scholar
 
13.
P.
 
Singh
 and
G.
 
Dhiman
,
J. Comput. Sci.
 
27
.
370
(
2018
).
https://doi.org/10.1016/j.jocs.2018.05.008
Google Scholar
 
This content is only available via PDF.
© 2023 Author(s).
2023
Author(s)
You do not currently have access to this content.