Dengue is one of the endemic diseases in Indonesia. Dengue is being suffered by many people, regardless of their gender and age. Therefore, a research about dengue based on a dengue patients’ data is conducted. There was a lot of information written in that data regarding the corresponding patients and the dengue they had suffered, such as gender, age, how long the patients were hospitalized, the symptoms they experienced, and laboratory characteristics. The diagnosis of each of the corresponding patients based on the symptoms and laboratory characteristics were also written in that data. The diagnoses were classified into three different clinical degrees according to the severity level, which are DF as the mild level, DHF grade 1 as the intermediate level, and DHF grade 2 as the severe level. In this research, data of the patients on the third day of being hospitalized is analyzed, because on the third day, dengue is entering a critical phase. The objectives of this research are: i) to find laboratory characteristics that affect the clinical degree of dengue in the critical phase, and ii) to analyze how robust the impact of those laboratory characteristics on the clinical degree of dengue in the critical phase. In this research, Bivariate Analysis was applied as the method to find the solution of the analyzed problems. The results obtained from this research can give information for the physicians about laboratory characteristics that affect the clinical degree of dengue in the critical phase, and how robust the impact of those laboratory characteristics on the clinical degree of dengue in the critical phase. Those results also can help the physicians to find solutions or strategies in preventing and/or treating dengue. Furthermore, those results will be used in the development of Machine Learning predictor program which will be able to predict the clinical degree of dengue in the critical phase, if the laboratory characteristics are known.

1.
Andriyoko
,
B.
, et al 
MKB
,
44
(
4
),
253
260
(
2012
).
2.
Anggraeni
,
W.
, et al 
Procedia Computer Science
,
124
,
142
150
(
2017
).
3.
Candra
,
A.
Aspirator
,
2
(
2
),
110
119
(
2010
).
4.
Götz
,
T.
, et al 
Ecological Complexity
,
30
,
57
62
(
2016
).
5.
Gulati
,
S.
, dan
Maheshwari
,
A.
Trop Med Int Health
,
12
(
9
),
1087
1095
(
2007
).
6.
Hidayati
,
L.
,
Hadi
,
U. K.
, &
Soviana
,
S.
,
Acta Veterenaria Indonesiana
,
5
(
1
),
22
28
(
2017
).
7.
Lardo
,
S.
, et al,
Asian Pacific Journal of Tropical Medicine
,
9
(
2
),
134
140
(
2016
).
8.
Nurminha
,
Sugiarti
, M., &
Aulia
,
M. G.
,
Jurnal Analis Kesehatan
,
7
(
2
),
717
723
(
2018
).
9.
Rasyada
,
A.
,
Nasrul
,
E.
, &
Edward
,
Z.
,
Jurnal Kesehatan Andalas
,
3
(
3
),
343
347
, (
2014
).
10.
Rosdiana
,
Tjeng
,
W. S.
, &
Sudarso
,
S.
,
Sari Pediatri
,
19
(
1
),
41
45
(
2017
).
11.
Suryani
,
E. T.
,
Jurnal Berkala Epidemiologi
,
6
(
3
),
260
267
(
2018
).
This content is only available via PDF.