RABERTA : The Application of Low Fetal Weight Probability
Detector as a Solution for Recording andIntervention in the Golden Period

Zata Ismah, Citra Cahyati Nst

Faculty of Public Health, State Islamic University of North Sumatra, Indonesia

新生児の推定15〜20％は低体重（2500グラム以下）で生まれてくるという。そこで筆者らは、女性が妊娠中に、わが子が低体重で生まれてくる可能性を簡単に予測できるコンピュータ・プログラムRABERTAを開発した。

Abstract

Low Fetal Weight (LFW) or Small Gestational Age (SGA) can cause low birth weight and contribute to neonatal mortality and morbidity. This application innovation is named RABERTA which was created to facilitate intervention inmonitoring the condition of mothers who are included in the golden period by calculating the probability of SGA. This
innovation was created with the Java programming language through NetBeans IDE 6.8 with an expert system
based on a research model of BBJR risk prediction with a sample size of 752 samples of pregnant women by crosssectional in 25 health services purposively in Palembang City, Indonesia. LFW prediction model after confounding
and interaction control, there are 5 LFW significant variables, namely abortion history, hypertension, gestationaldistance <23 months, BMI before pregnancy is thin, and weight gain is not optimal. The quality of the prediction
model is powerful (AUC = 0.951) so it is accurate to be used as a prediction of LFW. The implementation of thewidespread use of RABERTA can be done by advocating for stake holders such as the Health Office and easily
applied to computers based on Windows 7, 8 and 10.
Keywords

low fetal weight, neonatal mortality, golden period, maternal health

Introduction

Low birth weight fetus is known as Small Gestational
Age (SGA) or Low Fetal Weight (LFW). The term SGAor small gestational age (KMK) is also often equatedwith fetal growth restriction or intrauterine growth
restriction (IUGR). SGA or LFW is a fetus with an estimated fetal weight or fetal abdominal circumference
when ultrasound examination is less than the 10th percentile, which means the fetus weighs less than 90% of
all fetuses in the same gestational age (POGI, 2016).
Low fetal weight or Small Gestational Age (SGA)
can have an impact on several conditions. SGA conditions can result in low birth weight (LBW). It is estimated that 15-20% of all births worldwide are LBW.
Improving human quality should start as early as possible since the fetus is still in the womb. So far, theintervention is still focused on babies who have beenborn so that the intervention on infant mortality can besaid to be too late. Prevention of infant mortality is carried out early on by preventing the occurrence of LBW
at the stage the baby is still in the womb (Ernawati,Kartono, & Puspitasri, 2011). Fetal period is included in
the period of the first 1000 days of life. This period isoften referred to as the golden period or window ofopportunities, during which a very fast growth anddevelopment process occurs and does not occur in otherage groups. Appropriate intervention during this period
will have an impact on the better quality of humanresources (Kemenkes RI, 2012).
The incidence of low fetal weight has an impact onseveral adverse health conditions. Therefore, recording,reporting and intervention as early as possible need to053

Zata Ismah, et al.

be done to avoid these losses. The current problem isthat there is no record of the prevalence of Intrauterine
Growth Restriction (IUGR) in Indonesia.
The government, the Health Service, and research
institutions only publish the incidence of Low Birth
Weight (LBW), without classifying gestational age andsupporting diagnoses. Not all low birth weight babies
are categorized as IUGR, because some prematurebabies even though their birth weight is <2500gr, theirgrowth is in accordance with their gestational age.
Meanwhile, IUGR is the inability of the fetus to achieve
normal growth, both in preterm, athermic, and postterm
conditions (Nuraini, 2017).

Methodology

This innovation stage starts from planning and several stages of research and application design stages.
a) User Research
The research stage is carried out in 4 stages: 1) analysis of risk factors that most influence fetal weight; 2)
confounding analysis and interaction test; 3) making a
fetal weight prediction model; 4) the last, calculation of
the probability of fetal weight.
1) Risk factor analysis
1. Examination of 752 pregnant women
2.	The variable measured is a variable that based on
previous research has a lot to do with the incidence of LBW. The variables are maternal age,mother’s education, mother’s occupation, maternalheight, maternal BMI before pregnancy, maternal
weight gain, parity, history of abortion, pregnancy interval, hypertension, and smoking status
3.	From these variables, a partial statistical test wasperformed. From the statistical test results at 5%
alpha, 7 variables become risk factors, namely:
BMI before pregnancy; Age; Weight gain; parity;Abortion history; Pregnancy Distance; andhypertension2) Confounding analysis and interaction test
1.	After obtaining 7 variables, the next analysis isthe confounding test. From the confounding054analysis that has been done, it is obtained 2 confounding variables in this study. Confounding
variables affect changes by >10% in exp B othervariables. The variables are age and parity. Thenext stage is to test the interaction on eachvariable
2.	The significance value for all interaction variables is P-value > 0.05. This means that there is nointeraction between these variables, so all interaction variables are excluded from the model.
Thus, it is concluded from this research that there
is no interaction variable.
3) Making a Fetal Weight Prediction Model
From the results of the confounding and interaction
tests and from the results of multivariate analysis
using logistic regression, there were 5 variables that
affected the incidence of low fetal weight. The variables are:1.	
The most dominant risk factors for low fetal
weight were the history of abortion with a P-valueof 0.005; 95% CI of = 2.00-52.56.
2.	The other risk factors are hypertension with aP-value of 0.012; 95% CI of = 1.65–57.99. Next,
gestational interval <23 months with a P-value of0.001; 95% CI of = 2.63–33.86.
3.	The variable BMI before pregnancy being underweight was a risk factor as well with a P-value of0.026; 95% CI of = 1.25–30.14.
4.	The last risk factor is an increase in body weightP-value 0.016; 95% CI of = 1.32 - 16.4.
b) Application program design
The stages of designing the RABERTA application
start from making a programming language with Java,
then end with application testing

Resultsa. App View
In Indonesian, RABERTA stands for ramalan optimal
berat janinku (optimal prediction of my fetal weight).
This innovation answers questions from health workersand mothers regarding optimal or not the weight of the6th AFC Best Papers (2022)

fetus they are carrying easily and accurately. The desktop version of RABERTA is a calculator that is optimized for use with Windows 7, 8, and 10. The scenario
for how to use it is as follows :1. Install the application into the Computer
2. Once installed, open the application
3. Enter data such as name and age

Filled with mother's name
Filled with mother's age
To be ﬁlled with the status of the mother's BMI

Filled with maternal weight gain
Filled with history of maternal abortionFill in the distance of previous pregnancies
Filled with hypertension status

Figure 1. Application Formb. Fetal Weight Probability Calculation
After obtaining a fit logistic regression model equation, the probability of the binary logistic regression
model equation is as follows:
WithP(x) = 1/ (1 + e-y)y = Fit Modele = Natural number; 2.718
Description:a. Weight gain: optimal = 0; not optimal =1
b. Hypertension: no hypertension = 0; hypertension = 1
c. Pregnancy interval: >23 months = 0; < 23 months =1
d. BMI before pregnancy: normal = 0; skinny =1
e. Abortion history : no abortion = 0; ever had an abortion=1
The value of the quality of this equation can be seenfrom the results of the Receiver Operating Curve (ROC).

4. Enter data on risk factors, namely BMI before pregnancy, weight gain, history of abortion, pregnancy
interval and hypertension according to the respondent’s condition.
5. Click “Results”
6. The results will appear in the form of a pop up menu
as shown in the image below.

Filled with mother's name
Filled with mother's age
To be ﬁlled with the status of the mother's BMI

Table 1. ROC Calibration ValueAreaStd. Error

0.951

0.018

95% Confidence IntervalMinMak
0.917
0.986

Based on the results of the Area Under Curve (AUC)
assessment, it is known that the AUC value is 0.951 or
95% with a range of 92% - 99%. Statistical interpretation as an equation discrimination parameter can beseen in the following table:
Table 2. Interpretation of ROC Calibration Values

Filled with maternal weight
Filled with history ofFill in the distance of previous pregnancies
Filled with hypertension

Figure 2. Application Results Page

AUC Value>50%-60%>60%-70%>70%-80%>80%-90%>90%-100%

Interpretation
Very weak
WeakMediumStrong
Very strong

potential low fetal weight38.06%

Based on the discrimination parameter, the AUC
value is 95%, which means that statistically the qualityof the equation model for the incidence of low fetal
weight in this study is very strong (> 90%-100%).

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Zata Ismah, et al.

c. Application testing
The trial of this RABERTA application used 164
samples. The sample formula uses the minimum sample
size formula for validity testing. The calculation formulais as follows

With α = 99%; β = 99%; r = 0,4; drop out 20%
Thus n = 164 samples
Based on the test results obtained :100 %Validity ==100%= 83,53%

Discussion

Fetal period is included in the first 1000 days of life
which is commonly referred to as the golden period.
This period is a very important time because at this timea very fast growth and development process occurs andis not experienced by other age groups. For now, datacollection related to BBJR has not been carried out. Sofar, the recording and reporting carried out by the government, the Health Service and research institutions isonly limited to data collection of LBW. It is important tonote that interventions for LBW problems can also prevent the occurrence of LBW. With the problem of BBJR
resolved, neonatal health problems can be resolved
earlier.
Therefore, the right intervention during this period ofpregnancy will have a good impact on the quality ofhuman resources in the future. Interventions are especially carried out for pregnant women who have a risk ofstunted fetal growth, in this case referred to as low fetal
weight (LBW). This is done to prevent the occurrence of
health problems for babies, both when they are still in
the womb and when they are born, such as Small
Gestational Age (SGA), LBW and others. This intervention can be carried out if pregnant women have a high056

percentage of the probability of having LBW. Therefore,a breakthrough is needed to detect this percentage ofopportunity.
This breakthrough can be realized by creating a software that is able to calculate the percentage probabilityof a mother having a fetus with low birth weight. This
device will facilitate the data collection process for
pregnant women who have a high risk. Thus, based on
the results of the data collection, interventions can be
carried out quickly and accurately.
This RABERTA application has received a goodvalidity score. In the future, RABERTA will continue
with web-based and android-based versions. If both versions have been implemented, this version will bedirectly connected to the surveillance system of thehealth office. If it is connected, the fetal weight surveillance system is expected to be optimal and capture
many underweight fetuses. Thus, many fetuses will beintervened, and reduce the number of LBW (low birthweight babies).
The advantages of this desktop version application:
1. The main display application is very simple; 2. Easyto use; 3. Light application size; 4. RAM used is light; 5.
High validity. Another advantage can be distributed to
public health centers and private clinics as a solution if
the internet network is problematic or public health centers or private clinics do not have internet access. Spreadthe application by inserting it into a CD-R to be installedon each PC in the Public Health Center and delivery
clinic. Public Health Center and Delivery Clinics canuse this application every time the patient visits pregnancy (K1 to K4).
The disadvantages of this application are : 1. If theapplication is closed, history data will be lost; 2. Absenceof data backup; 3. There is no undo and redo menu.

Conclusion

RABERTA (ramalan optimal berat janinku, or optimal prediction of my fetal weight) is created as a solution for recording and intervention in the fetal goldenperiod. The application can be used easily, quickly andaccurately. Suggestions that can be given to the Health
Office can use this application and disseminate this6th AFC Best Papers (2022)

application to the Health Service and the community to
be used to detect the low fetal weight probability. For
further research, the development of applications thatcan be used on Android, the development of more nutritional sensitivity and obesity calculation formulas, thedevelopment of application history storage, the development of application data backup and the development of
undo and redo menus.

Suggestions

This RABERTA application requires collaboration
with various parties in order to maintain its sustainability. Including the following :1. Commitment of the Department of Health. TheHealth Office needs commitment and supervision sothat this application can continue to be used by publichealth centers and private clinics.
2. Private Clinic. Cooperation of the willingness of private clinics is required to install this RABERTA
application.
3. BKKBN of North Sumatra province. In this case, therole of the BKKBN is needed to socialize theRABERTA Application in every event. BKKBN canalso offer help installing this application to the public
4. Cooperation with various universities to empowerstudents as agents to maintain the sustainability ofthe RABERTA application. In this case, the university is also a center for research studies to evaluate
the effectiveness of RABERTA.
5. Cooperation from investors is needed for financial
assistance. If it has received support from investors,
then this application will expand cooperation to theIT research center, so that this RABERTA can continue its development to the WEB Based and Android
Based versions. Thus, the benefits of the RABERTA
Application will be felt more and more throughout
the archipelago

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