Decision Making Model for Choosing Normal Maternity or Cesarean

Section with Machine Learning Approach

Rimin, Ermi Girsang, and Sri Lestari R. Nasution

Faculty of Medical, Universitas Prima Indonesia, Indonesia

Keywords: Choice of delivery, Normal delivery, cesarean section.

Abstract: Globally, the number of cesarean section has almost doubled, namely 12% in 2000 to 21% in 2015. While

more than 50 developing countries have cesarean birth rates> 27%. Normal childbirth actually has many

advantages over cesarean section, but data in various hospitals shows an increase in cesarean section rates.

The purpose of this study was to identify the relationship of factors that influence mothers with the decision

to choose normal delivery or cesarean section. An evaluation of 3,121 respondents with 118 samples was

conducted. Statistical evaluation using univariate and bivariate analysis with chi-square test, and multivariate

analysis with multiple logistic regression at 95% confidence level ( = 0.05) was performed. Whereas the

model of the relationship of the main factors in decision making in the selection of maternity scenarios was

built using machine learning approach. Statistical evaluations indicate that there are only three variables (i.e.,

culture, lifestyle, and perception, p ≤ 0.009) that have a relationship with the decision of the mother to choose

normal delivery or cesarean section. The factor with the greatest relation is perception (Exp (B) / OR was

3.305).

1 INTRODUCTION

Delivery of cesarean section is becoming a trend now

not only for mothers who have medical indications,

but also those who do not experience medical

indications (reasons for wanting to adjust the desired

date and day). Another reason is that labor is fast,

does not experience pain such as giving birth

normally and can maintain a slim body appearance

(Kasdu, 2015).

A systematic review published studies from the

first year of records through August 2018 in PubMed,

Scopus, and Web of Science showed that the reasons

of maternal request for cesarean section were fear of

childbirth, fear of labor pain, anxiety for fetal

injury/death, pelvic floor and vaginal trauma, urinary

incontinence, doctors suggestion, time of birth,

experience of prior bad delivery, previous infertility,

anxiety for gynecologic examination, anxiety for loss

of control, anxiety for lack of support from the staff,

fear of fecal, avoid long labor, emotional aspecs, body

weight of the infant at birth and abnormal prenatal

examination. The results of studies on demographic

reasons of maternal request for cesarean section were

advanced maternal age, occupation, parity, maternal

obesity, education, family status, decreasing level of

religiosity, number of living children, household

income and age at marriage (Jenabi E et al, 2019)

Excessive and unnecessary use of cesarean

section is growing as a major problem for women’s

health for which both developed as well as

developing countries. There is no evidence to show

any benefits either to mother or to infant when the

procedure is not medically indicated (Panda et al.,

2017).

Globally, infants born with cesarean section

increased by 12% in 2000 to 21% in 2015 (Marleen,

2018). The high rate of cesarean delivery makes

WHO set the average standard for cesarean delivery

in a country around 5-15% per 1000 births in the

world (Suryati, 2012). WHO also reviewed 110,000

births from nine countries in Asia during 2007-2008,

showing that 27% of births were performed cesarean

section. A similar survey conducted in Latin America

found that 35% of pregnant women were born

through cesarean section (Sihombing, 2017).

Around 23 million cesarean deliveries were

performed globally in 2012. The international health

care community previously considered the 10% and

15% levels to be ideal for cesarean section (Hamilton,

2018). More than 45 countries globally have cesarean

birth rates <7.5%, while more than 50 developing

Rimin, ., Girsang, E. and R. Nasution, S.

Decision Making Model for Choosing Normal Maternity or Cesarean Section with Machine Learning Approach.

DOI: 10.5220/0010289200880095

In Proceedings of the International Conference on Health Informatics, Medical, Biological Engineering, and Pharmaceutical (HIMBEP 2020), pages 88-95

ISBN: 978-989-758-500-5

countries have cesarean birth rates> 27% (Molina et

al., 2015).

In 2017 in the United States, around 32% of

women in the hospital were delivered by cesarean

section (Hamilton, 2018). Primary cesarean delivery

and repetition means that the mother giving birth at

the first delivery in a cesarean section will do the

same thing in the next delivery (Menacker, Declercq

& Macdorman, 2016).

The incidence of cesarean section birth at the

request of the mother and her contribution to the

increase in cesarean section rate is not known with

certainty, but it is estimated that around 2.5% of all

deliveries in the United States are cesarean section at

the request of the mother (Meredith, Birsner, & Flint,

2019). In a retrospective cohort study of 66,266

mothers giving birth in China, there were 24.7% of

deliveries with cesarean section at the request of the

mother (Liu, Landon, Cheng, & Chen, 2015).

It was found that the USA (84,5%) and Australia

(77,3%) had the highest proportion of obstetricians

who would perform a cesarean section on maternal

request in uncomplicated pregnancies, while Spain

(15%) and Canada (23%) had lowest proportion

(Loke et al., 2019). Behind a maternal request for a

planned cesarean section are various rationales and

life experiences needing carefully targeted attention

and health care (Eide et al, 2019). Women who

requested a cesarean section had higher antepartum

depression and anxiety levels (Olieman et al., 2017).

The study of cesarean section rate among doctors

in S.V. Medical College, Tirupati indicates that the

rapid socio-economic changes and the outlook

towards medical intervention by the women, families

and society are increasingly responsible for the

current high incidence of cesarean section. For

doctors point of view it is a defensive medicine to

have a better outcome (Radha et al., 2015).

The number of cesarean section in Indonesia also

continues to increase both in government hospitals

and in private hospitals. According to the Indonesian

Demographic and Health Survey Data (SDKI) in

1991-2007 there was an increase in cesarean section

births, which was between 1.3% -6.8% (Ministry of

Health Republic of Indonesia, 2018a). The results of

the 2018 Riskesdas show that cesarean delivery rates

were 9.8% of the total 49,603 births during 2014-

2017, with the highest proportion in DKI Jakarta

(19.9%) and the lowest in Southeast Sulawesi (3.3%)

(Ministry of Health) RI, 2018b). The standard of

caesarean delivery according to the Ministry of

Health is 40% (Ayuningtyas, Oktarina, Misnaniarti,

& Sutrisnawati, 2018).

One of the effects of cesarean section is pain and

results in changes in tissue continuity due to

abdominal surgery. According to Hillan in

(Anggorowati & Sudiharjani, 2011) that 68% of post-

sectio caesarean mothers have difficulty caring for

infants, difficulty moving up and down from bed and

arranging a comfortable position during

breastfeeding due to pain, thereby delaying

breastfeeding from the beginning of the baby

(Batubara, 2008). Cesarean section is associated with

an increased risk of uterine rupture, abnormal

placentation, ectopic pregnancy, stillbirth, and

preterm birth. (Sandall, 2018). After cesarean section

there can be infection, bleeding, formation of blood

clots in the legs, pelvic organs or lungs, injury to the

intestine and / or bladder and reactions to drugs or

anesthesia (Kasdu, 2015). The emergence of various

postoperative complications after cesarean does not

dampen the interest of the mother to give birth in a

cesarean section, although there are no medical

indications (Menacker et al., 2006).

The decision to choose a health service facility in

a hospital are influenced by several factors /

components (Figure 1). According to Kotler and

Armstrong, the factors that influence the decision to

choose service facilities are social factors, cultural

factors, individual factors and psychological factors.

Social factors consist of reference groups, family, role

and status. Cultural factors include culture, sub-

culture, social class. Individual factors include age,

occupation, economic conditions, lifestyle,

personality and self-concept. Psychological factors

include motivation, perception, knowledge, beliefs

and attitudes (Kotler & Armstrong, 2014; Turnip et

al, 2020; Wijaya et al, 2019).

Based on interviews conducted in the initial

survey of 10 mothers who gave birth, namely 5

normal births, and 5 cesarean delivery mothers,

normal delivery mothers said that they wanted to feel

a true mother can give birth to a normal baby, afraid

of having cesarean delivery, costs cheaper, during

pregnancy there are no significant problems or

disorders of pregnancy so they want to give birth

normally.

Mothers who choose cesarean delivery say fear of

pain or fear of pain experienced by many mothers

who give birth normally, want to give birth according

to a beautiful date, want to give birth on a special day

like according to their wedding date, in addition he

also has a less pleasant experience on normal delivery

before. There are also mothers whose reason because

of a husband's request to give birth by cesarean

section so that the shape of the vagina does not change

after delivery, and the body shape of the mother can

Decision Making Model for Choosing Normal Maternity or Cesarean Section with Machine Learning Approach

be slim like before pregnancy. The medical doctor has

actually explained to the mother who will give birth

about the procedure and the negative effects of

cesarean section delivery and the existence of a code

of ethics about cesarean delivery if done without

medical indications, but many mothers who still want

to do it.

Figure 1: Theoretical Framework for Decision Making

(Kotler & Armsrtong, 2014)

In this study, the decision making model for

choosing normal maternity or cesarean section with

fuzzy method is performed. The relationship between

the factors that influenced the mother in deciding on

a normal delivery or cesarean section in relation to the

characteristics of the respondents was evaluated.

2 METHOD

This research was a quantitative analytic study with

cross sectional research design. The study was

conducted at the Stella Maris Hospital in Medan in

November 2019. The number of deliveries (normal

and cesarean section) from January 2019 - October

2019 was 3,121 deliveries (1,064 normal deliveries,

2,057 cesarean section divided into 403 cesarean

section deliveries without medical indication and

1,654 cesarean section cesarean with medical

indications). The research sample obtained by 118

respondents. Sampling technique by accidental

sampling.

The conceptual framework of this study consists

of independent and dependent variables. The

independent variable consists of cultural factors

consisting of Culture (values in the family); Social

factors consist of reference groups, family support;

Private (individual) factors consist of age, profession,

lifestyle; Psychological factors consist of motivation

and perception. The dependent variable is the

decision to choose normal delivery or cesarean

section. The full research conceptual framework can

be seen in Figure 2.

Figure 2: Research Scheme

The sample is a portion taken from the whole

object studied and is considered to represent the entire

population (Notoatmodjo, 2015). The sample of this

study was a portion of the population whose size was

taken using the Lemeshow formula as follows:

 









12/1

1)1(

PoPa

PaPaZPoPoZ











Where n is the sample size, Z



is the standard

deviation of the alpha for  = 0.05 à Z= 1.96, Z



is the standard deviation of beta for  = 0.10 à Z =

1,282, Po is the proportion of the prevalence of

cesarean section without medical indication, Pa is the

estimated maternal cesarean section without medical

indication, Pa-Po is the proportion difference of 0.10.

Validity test is used to measure the validity of an

instrument used (able to express what is measured).

This test was conducted at Sarah Medan General

Hospital as many as 30 women. The test conducted

is to determine the correlation between the questions

with the total construct score or variable. A

construct is declared valid if there is a positive and

significant correlation in the Pearson Product

Moment test. The correlation value must be greater

than 0.361 or the Corrected Indicator-Total

Correlation value in SPSS output greater than 0.361

(Ghozali, 2015). Univariate data analysis, bivariate

using chi-square and multivariate tests using

multiple logistic regression tests with a confidence

level of 95% ( = 0.05).

Fuzzy Logic (Fuzzy Logic) or commonly also

called Samar Logic is an appropriate way to map an

input space into an output space based on the concept

HIMBEP 2020 - International Conference on Health Informatics, Medical, Biological Engineering, and Pharmaceutical

of fuzzy sets. Among the inputs and outputs are black

boxes. Inside the black box there is an unknown

process, it can be approached with a linear system

approach, econometrics, interpolation, expert

systems or fuzzy logic. Fuzzy logic as the main

component of soft computing builders, has proven to

have excellent performance to solve problems that

contain uncertainty (Kusumandari et al, 2018; Turnip

et al, 2018). Its implementation is broad, both in the

fields of engineering, psychology, social, and also in

the economic field. This research is used to make

decisions for Choosing Normal Maternity or

Caesarean Section.

The Crisp set is defined by the items in that set. If

a is a member of A, then the value associated with a

is 1. However, if a is not a member of A, then the

value associated with a is 0. Notation A = {x P (x)}

indicates that A contains item x with P (x) is correct.

If XA is a characteristic function of A and property P,

then it can be said that P (x) is true, if and only if XA

(x) = 1. To design a fuzzy system the following steps

need to be carried out: (i) Define the functional and

operational characteristics of the model. (ii) In this

section, it is necessary to consider what

characteristics the existing system has, and then

formulate the characteristics of operations to be used

in the fuzzy model. (iii) Decomposing the model

variables into fuzzy sets. From the variables that have

been formulated, fuzzy sets are formed without

ignoring the domain. (iv) Making fuzzy rules. The

rules on fuzzy show how a system operates. How to

write rules in general are: If (X1 is A1). ... (Xa is An)

Then Y is B where (.) is an operator (OR or AND), X

is scalar and A is a linguistic variable. The block

diagram of fuzzy interference system is shown in

Figure 3.

Figure 3: Block diagram of fuzzy interference system.

3 RESULTS AND DISCUSSIONS

Characteristics of respondents involved in data

collection are the latest education and the number of

births. The observations showed that the majority of

respondents had an undergraduate education of 49

respondents (41.5%), the minority had a midle high

school education of 1 respondent (0.8%). Based on

the number of births, the majority of respondents gave

birth to their first child as many as 59 respondents

(50.0%), the minority gave birth to a fifth child of 1

respondent (0.8%).

Based on the results of bivariate analysis with the

Chi-Square test obtained variables that have a

significant relationship with the selection of normal

maternity or cesarean section with a value <0.05

namely culture (p = 0.005), reference group (p =

0.008), family support (p = 0.003), lifestyle (p =

0.021), motivation (p = 0.003) and perception (p =

0.002). Whereas the unrelated variable because it has

a value of p> 0.05 is age (p = 0.253), and occupation

(p = 0.701).

Table 1: Relationship of each Independent and Dependent

Variable (Normal and CS is C-Section).

Variables

Childbirth

Metho

value

NCS

ff F

Age (years):

20-35

> 35

104

0,253

Profession:

Working

Not Workin

0,701

Culture:

Not Follow

0,005

Reference group:

Not Follow

0,008

Family support:

Support

Not Su

ort

0,003

Lifestyle:

Lifestyle

Not Lifest

0,021

Motivation:

High

Low

0,003

Perception:

Good

Not Goo

0,002

Furthermore, multivariate analysis with multiple

logistic regression tests showed that out of 8 research

variables, there were 6 variable logistic regression

candidates, p <0.25, namely culture, reference group,

family support, lifestyle, motivation and perception.

While the variables that were not included in the

model candidate because it has a value of p> 0.25

were age and occupation. The results of multiple

Decision Making Model for Choosing Normal Maternity or Cesarean Section with Machine Learning Approach

logistic regression tests (Table 2) show that of the 6

variables tested about 3 variables related to the

selection of normal maternity or cesarean section

namely culture (values in the family), lifestyle and

perception.

The most dominant variable related to the

selection of normal childbirth or cesarean section is

the perception variable with the value Exp (B) / OR =

3.305 meaning that mothers with good perceptions,

have the opportunity to choose normal childbirth by

3.3 times higher than mothers with poor perception.

Table 2: Multiple Logistic Regression Test Results.

Variable B Sig. Exp(B)

95%CI for

Exp(B)

Culture

Lifestyle

Perception

Constan

1,130

1,260

1,195

-1,427

0,009

0,008

0,007

3,096

1,284

3,305

1,333-7,193

1,110-6,732

1,359-8,037

3.1 Culture Factors

The results of this study indicate that there was a

relationship between culture and the mother’s

decision to choose a normal delivery or cesareaen

section. Mothers who follow the culture in the family

which always give birth with normal delivery, have

the opportunity to choose normal delivery by 3 times

higher than mothers who do not follow the culture.

Culture is the result of human creativity form one

generation to the next generation which greatly

determines the behaviour of the members in their

life as a society. Culture is complex which includes

science, artistic beliefs, morals, customs, habits and

norms that occurs in the society. According to

Kotler, values in the family is included in the culture

(Kotler, 2014).

The culture in the family influence a person to

make a decision. Many groups can influence a

person’s behaviour especially in the family. A

person’s reference group consists of all groups that

have direct (face-to-face) or indirect influence on a

person’s attitude or behaviour, especially in the

famiy. Groups of direct influence on a person are

called membership groups. This is a group in which a

person participates and interacts (Tjiptono, 2014).

The results of this study indicate that culture is

related to the mother’s decision to choose normal or

caesarean section. According to researchers’

assumptions, this is because the habits that occur in

the family will be carried out by other family

members. Likewise in the case of childbirth, other

family members will follow the culture in the family

when they are about to give birth. Usually families

who have normal childbirth habits, in choosing a

birth, the respondent will also choose to have a

normal birth. Likewise, families who have the habit

of giving birth by caesarean section will also choose

to give birth by caesarean section. This is also due to

the type of delivery chosen after getting information

from family or closest relatives, as well as

suggestions from the family or relatives who suggest

choosing childbirth that is usually done by the family.

Culture in the family or relatives has a major

influence on the decision of the mother to choose type

of delivery, whether nomal or caesarean delivery,

especially without medical indications.

3.2 Lifestyle Factors

The results of this study indicate that there was a

relationship between lifestyle and the mother's

decision to choose a normal delivery or cesarean

section. Mothers who do not follow a lifestyle, have

the opportunity to choose normal childbirth by 1.2

times higher than mothers who follow a lifestyle.

Reasons that oftenly given by patients to deliver labor

with cesarean section were not resistant to pain, worry

about damage to the birth canal which is also a sexual

organ and a factor of trust in dates and numbers.

The doctor's reason for fulfilling patient requests

is generally to respect and pay attention to the

patient's autonomy rights and the Medical Practice

Law No. 29 of 2004 Article 52 paragraph d. (fear of

being blamed if something bad happens to the mother

and baby). Reasons that are not allowed are financial

considerations (GoI, 2004).

To prevent the misuse of the cesarean section at

the request of the patient or family by an Obstetrician

and Gynecology specialist, the following signs

should be made: The patient must submit a request to

the doctor for cesarean section. Next the doctor must

explain that at that time vaginal delivery is still

possible and cesarean section is no better / safer than

vaginal delivery. The signs as above must be stated

clearly and clearly in the cesarean section request

sheet at the request of the patient / family, which is

incorporated in the informed consent sheet and

permission for action (POGI, 2017).

Lifestyle is a lifestyle of someone in the world

expressed in one's activities, interests and opinions

(Kotler & Amstrong, 2014). Delivery of cesarean

section has become a way of life, especially for young

people. There was a trend of an increase in cesarean

section in a number of hospitals, whereas the clinical

risk for mothers giving birth was greater than the risk

of vaginal delivery. But the perception of the

community, especially young people, about cesarean

section delivery is a fast and painless way of birth as

HIMBEP 2020 - International Conference on Health Informatics, Medical, Biological Engineering, and Pharmaceutical

experienced in normal childbirth (Simanjuntak,

2012).

The results of this study indicate that lifestyle was

related to the mother's decision to choose normal

delivery or cesarean section. According to the

researchers' assumptions, lifestyle is a personal factor

(individual) of the mother herself, especially those

who choose cesarean section without medical

indications that assume cesarean section is a modern

way to deal with the pain during childbirth. As for

mothers who choose normal childbirth, assume that

with normal childbirth, she will become a whole

mother. As it is the case now, lifestyle returns to

nature which means that everything in natural is more

interesting and has great benefits for itself.

3.3 Perception Factors

The results of this study indicate that there was a

relationship of perception with the decision of

mothers to choose normal delivery or cesarean

section. Mothers who have a good perception have a

chance to choose normal delivery by 3.3 times higher

than mothers with poor perception. Perception is the

process that people go through in choosing,

organizing and interpreting information to form a

meaningful picture of the world. Someone which

motivated is ready to act. How the person acts is

influenced by their perception of the situation (Kotler

& Armstrong, 2014).

The results of this study indicate that perception

was related to the mother's decision to choose normal

delivery or cesarean section. According to the

researchers' assumptions, mothers who had the

perception that childbirth was painful decided to

choose cesarean section while mothers who had the

perception that giving birth normally were a pleasure.

Especially if he does not have a medical indication for

cesarean delivery then he prefers to give birth

normally. Normal delivery process is longer and

painful, but now there are many therapeutic

techniques that can be used to reduce pain during

normal labor such as hypnobirthing, Entonox gas,

ILA (Intrathecal Labor Analgesia) and others. Giving

birth normally through a long process involves a

mother's hard work and results in physical fatigue.

However, many benefits are obtained by giving birth

normally, such as being able to leave the hospital

more quickly, avoiding the risks caused by surgery,

the mother can directly interact with the baby, not too

worried about the next pregnancy or childbirth and so

forth. Cheap and painless normal delivery perceptions

need to be explained to mothers since pregnancy,

especially for young mothers so that more mothers

give birth normally and take classes in hospitals such

as pregnancy exercise, hypnobirthing, etc.

3.4 Machine Learning Approach

In the implementation phase, three processes were

carried out, namely application testing,

documentation, and analysis of test results.

Application testing aims to see the results of

applications that have been made whether running

well or not. Tests conducted were black box testing

(Turnip et al, 2018). This test was run to observe

whether the program has successfully received input,

processed, and produced the appropriate output

without looking at the application source code.

In analyzing the problem, the first thing to do was

to determine what parameters were used as input to

the system. Second, observations made by the author

indicate that what will be used as input reference was

the calculation obtained from the evaluation variables

in the form of culture, reference groups, family

support, life style, motivation, and preception. Third,

how the input values of these variables can be

identified by ANFIS to produce an output so that the

problem in predicting the decision for choosing

normal maternity or cesarean section is resolved.

Decision making prediction application is an

application that can predict maternity choices only by

entering input variables as a result of measurement

(Figure 4). Prediction of maternity choices can be

done by taking the unique value of the maternity

variable which will be converted into a certain value

which is the identity of each decision and through a

process of matching or matching training data. The

database used was training data from maternity

choices.

In the process of evaluating maternity choices

preceded by taking the value of variables that have

been determined by the Region of Interest.

Furthermore, the value will be converted into a new

value that is searched for the average value and will

be used as input parameters in ANFIS.

The training of 80 training data uses six input

membership function linear trimf output curves with

40 epochs (Figure 5), and a hybrid optimization

method. After the training process was completed, the

ANFIS Architecture is formed as Figure 6.

Membership function and parameters formed from

the training process are shown in Figure 7.

Decision Making Model for Choosing Normal Maternity or Cesarean Section with Machine Learning Approach

Figure 4: Trainig data and Training Error

Figure 5: Membership function and FIS output.

In testing the prediction choice of maternity

applications using the ANFIS method. The application

can identify 111 correctly from a total of 118 input

data. The overall success percentage was 94.24%.

From 80 training data trained, all images were correctly

identified. This shows the percentage of success

identifying data that has been trained by 100%.

Meanwhile, from 38 data that have not been trained or

have not yet gone through the ANFIS training process

there are 36 results that are correctly identified so that

the percentage of success in identifying the data that

has not been trained is 94.24%.

Figure 6. Adaptive neural network architecture.

Figure 7: The applied rules for modeling.

4 CONCLUSIONS

There is a cultural relationship (values in the family),

lifestyle and perception with the mother's decision to

choose normal delivery or cesarean section. There is

no relationship between the reference group, family

support, age, occupation and motivation with the

mother's decision to choose a normal delivery or

cesarean section. The most dominant variable related

to the mother's decision to choose a normal delivery

or cesarean section in this study is the perception

variable. Mothers who have good perception, have

the opportunity to choose normal delivery by 3.3

times higher than mothers whose perceptions are not

good.

Based on the results of the study concluded that to

predict maternity choice decisions can be done using

the Adaptive Neuro Fuzzy Inference System method

as a matching tool with the values of variables that

have been trained. From the results of testing of input

data, this application has an overall success rate of

94.24%. With this application, maternity choice

decisions can easily be predicted based on variable

values that are set without having to retake data.

REFERENCES

Anggorowati, & Sudiharjani, N. (2011). Mobilisasi Dini

dan Penyembuhan Luka Operasi pada Ibu Post Sectio

Caesarea (SC) di Ruang Dahlia Rumah Sakit Umum

Daerah Kota Salatiga. Departemen Keperawatan

Maternitas Dan Anak PS Ilmu Keperawatan FK Undip.

Ayuningtyas, D., Oktarina, R., Misnaniarti & Sutrisnawati,

N. N. D. (2018). Etika Kesehatan pada Persalinan

Melalui Sectio Caesarea Tanpa Indikasi Medis. Jurnal

MKMI, 14(1), 9–16.

Batubara, S. (2008). Hubungan Pengetahuan, Nyeri Sectio

Caesarea dan Bentuk Puting dengan Pemberian Air

Susu Ibu Pertama Kali pada Ibu Post Partum. Jurnal

Keperawatan Sudirman (The Soedirman Journal of

Nursing, 3(2), 24–34.

Eide KT., Morken, NH., & Baerie K. (2019). Maternal

Reasons for Requesting Planned Cesarean Section in

HIMBEP 2020 - International Conference on Health Informatics, Medical, Biological Engineering, and Pharmaceutical

Norway: A Qualitative Study. BMC Pregnancy and

Chilbirth, 19:102.

Ghozali, I (2015). Aplikasi Analisis Multivariat dengan

Program SPSS (Cetakan 7). Semarang: Universitas

Diponegoro Press.

Hamilton, B. E. (2018). Births: Provisional Data for 2017.

NVSS. Vital Statistics Rapid Release.

Jenabi E et al. (2019). Reasons for Elective Cesarean

Section on Maternal RequestL A Systematic Review. J

Matern Fetal Neonatal Med, 1-6.

Kasdu, D. (2015). Operasi Caesar: Masalah dan Solusinya

(Cetakan 3). Jakarta: Puspa Swara.

Kemenkes RI. (2018a). Data Survei Demografi Kesehatan

Indonesia (SDKI) Tahun 2017. Jakarta: Kementerian

Kesehatan Republik Indonesia.

Kemenkes RI. (2018b). Riset Kesehatan Dasar (Riskesdas)

Tahun 2018. Jakarta: Kementerian Kesehatan Republik

Indonesia.

Kotler, P. (2014). Manajemen Pemasaran di Indonesia :

Analisis, Perencanaan, Implementasi dan Pengendalian

(Cetakan 2). Jakarta: Salemba Empat.

Kotler, P., & Armstrong, G. (2014). Prinsip-prinsip

Pemasaran (Edisi 13). Jakarta: Erlangga.

Kusumandari, D., Risqyawan, M., Yazir, M., Turnip, M.,

Darma, A. and Turnip, A., 2018. Application of

convolutional neural network classifier for wireless

arrhythmia detection, Journal of Physics: Conference

Series, Volume 1080 (2018) 012048 doi:

10.1088/1742-6596/1080/1/012048.

Liu, X., Landon, M. B., Cheng, W., & Chen, Y. (2015).

Cesarean Delivery on Maternal Request in China: What

are the Risks and Benefits? Am J Obstet Gynecol,

212(817), el-9.

Loke AY., Davies L., Mak YW. (2019). Is It the Decision

of Women to Choose a Cesarean Section as the Mode

of Birth? A review of Literature on the Views of

Stakeholders. BMC Pregnancy and Childbirth, 19:286.

Marleen, T. (2018). The Lancet: Cesarean Section Use has

Almost Doubled Globally Since 2000.

http://www.thelancet.com/journals/

lancet/article/PIIS0 140- 6736.

Menacker, F., Declercq, E., & Macdorman, M. F. (2006).

Cesarean Delivery: Background, Trends and

Epidemiology. Seminars in Perinatology.

https://doi.org/10.1053/ j.semperi.2006.07.002

Meredith, L., Birsner, M. D., & Flint, T. P. M. D. (2019).

Cesarean Delivery on Maternal Request. ACOG

Committee Opinion No. 761. American College of

Obstetricians and Gynecologists. Obstet Gynecol,

133e73-7.

Molina, G., Weiser, T. G., Lipsitz, S. R., Esquivel, M. M.,

Uribe-Leitz, T., Azad, T., & Shah, N. (2015).

Relationship Between Cesarean Delivery Rate and

Maternal and Neonatal Mortality. JAMA - Journal of

the American Medical Association, 314(21), 2263–

2270.

Notoatmojo, S. (2015). Metodologi Penelitian Kesehatan

(Cetakan 2). Jakarta: Rineka Cipta.

Olieman RM., Siemonsma F., Bartens MA., Niegel SG.,

Scheele F., & Honig A. (2017). The Effect of an

Elective Cesarean Section on Maternal Request on

Peripartum Anxiety and Depression in Women with

Chilbirth Fear: A Systemic Review. BMC Pregnancy

and Childbirth, 17:195.

Panda SR., Jain M., Jain S (2017). Rising Trend in

Caesarean Section Rate: A Community Heath Hazard.

Biomed J Sci & Tech Res. Vol 1 – Issue 5, 1476-1478.

Pemerintah RI. (2004). Undang-Undang Praktek

Kedokteran No. 29 tahun 2004. Jakarta: Lembaran

Negara Republik Indonesia Nomor 116.

POGI. (2017). Panduan Etik dan Profesionalisme Obstetri

dan Ginekologi di Indonesia. SK PB POGI No.

003/SK/PB.POGI/2017. Perkumpulan Obstetri dan

Ginekologi Indonesia.

Radha K., Devi GP., Manjula RV., Chandrasekharan PA

(2015). Study On Rising Trends Of Caesarean Section

(C-Section): A Bio-Sociological Effect. Journal of

Dental and Medical Sciences. Vol 14, Issue 8 Ver II,

10-13.

RSIA Stella Maris. (2019a). Data Persalinan Normal dan

Seksio Sesarea Tahun 2017 dan 2018. Medan.

RSIA Stella Maris. (2019b). Data Persalinan Normal dan

Seksio Sesarea Tahun 2019 (Januari 2019 - Oktober

2019). Medan.

Sandall et al (2018). Short-term and Long-term Effects of

Caesarean Section on the Health of Women and

Children. Lancet 392(10155): 1349-1357.

Sihombing, N. (2017). Determinan Persalinan Sectio

Caesarea di Indonesia (Analisis Lanjut Data Riskesdas

2013). Jurnal Kesehatan Reproduksi, 8(1), 63–75.

Simanjuntak, R. (2012). Persalinan Seksio Sesarea

(Cetakan 2). Jakarta: Sagung Seto.

Suryati, T., 2012. (Analisis Lanjut Data Riskesdas 2010)

Persentase Operasi Caesaria di Indonesia Melebihi

Standard Maksimal, Apakah Sesuai Indikasi Medis?

Buletin Penelitian Sistem Kesehatan, 15(4), 331–338.

Tjiptono, F. (2014). Manajemen Jasa (Cetakan 1).

Yogyakarta: Andi Offset.

Turnip, A., Andrian, Turnip, M., Dharma, A., Paninsari, D.,

Nababan, T., Ginting, C.N., 2020. An application of

modified filter algorithm fetal electrocardiogram

signals with various subjects, International Journal of

Artificial Intelligence, vol. 18, no., 2020.

Turnip, A., Ilham Rizqywan, M., Kusumandari, D., et al.,

2018. Classification of ECG signal with Support Vector

Machine Method for Arrhythmia Detection, Journal of

Physics: Conference Series, Vol. 970 (2018) 012012

doi: 10.1088/1742-6596/970/1/012012.

Turnip, A., Kusumandari, D., Pamungkas, D., 2018. Drug

Abuse Identification based EEG-P300 Amplitude and

Latency with Fuzzy Logic Calssifier, IEEE International

Conference on Applied Engineering, (ICAE), 3-4 Oct.

2018, DOI: 10.1109/INCAE.2018.8579378.

Wijaya, C., Andrian, M., Harahap, M., Turnip, A., 2019.

Abnormalities State Detection from P-Wave, QRS

Complex, and T-Wave in Noisy ECG, Journal of

Physics: Conference Series, Volume 1230, (2019)

012015. doi:10.1088/1742-6596/1230/1/012015.

Decision Making Model for Choosing Normal Maternity or Cesarean Section with Machine Learning Approach