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IMPLEMENTATION OF DATA MINING TO DETERMINE THE
ASSOCIATION BETWEEN BODY CATEGORY FACTORS BASED ON BODY
MASS INDEX
Desti Fitriati1, Bima Putra Amiga2
Program Studi Teknik Informatika1,2
Universitas Pancasila1,2
desti.fitriati@univpancasila.ac.id1, bimaamg@gmail.com2
Abstrak
Perkembangan arus globalisasi yang semakin meningkat dalam bidang ilmu pengetahuan dan teknologi
serta peningkatan pendapatan telah memberikan dampak pada berkurangnya aktivitas fisik masyarakat
yang mengakibatkan penyimpangan pola makan dan aktivitas fisik yang membuat seseorang tidak
memperhatikan bentuk tubuhnya. Metode perhitungan Indeks Massa Tubuh ini dapat digunakan untuk
menentukan bentuk tubuh seseorang. Terdapat beberapa faktor yang dapat mempengaruhi nilai Indeks
Massa Tubuh diantaranya adalah faktor individu, pola konsumsi, dan kurangnya aktivitas fisik yang
mengarah pada pola hidup sedentaris (sedentary lifestyle). Faktor-faktor tersebut dijadikan menjadi 69
itemset yang akan dijadikan dasar pertanyaan dalam kuesioner untuk mengumpulkan dataset yang
nantinya akan diolah menggunakan algoritma FP-Growth dan dicari aturan asosiasi yang memiliki nilai
support x confidence tertinggi. Dari 490 data hasil perhitungan dikategorikan menjadi 10 masing-masing
adalah Laki-Laki dengan Indeks Massa Tubuh (IMT) Sangat Kurus dengan nilai support x confidence
tertinggi sebesar 39,56%, Laki-Laki dengan IMT Kurus sebesar 55,90%, Laki-Laki dengan IMT Normal
sebesar 70%, Laki-Laki dengan IMT Gemuk sebesar 49,23%, Laki-Laki dengan IMT Obesitas sebesar 41,34%,
Perempuan dengan IMT Sangat Kurus sebesar 41,37%, Perempuan dengan IMT Kurus sebesar 37,21%,
Perempuan dengan IMT Normal sebesar 68,83%, Perempuan dengan IMT Gemuk sebesar 41,65%, dan
Perempuan dengan IMT Obesitas sebesar 42,91%.
Kata kunci: Algoritma FP-Growth, Data Mining, Asosiasi, Indeks Massa Tubuh, IMT
Abstract
The development of the increasing flow of globalization in the field of science and technology as well as
increased income has resulted in reduced physical activity of the community which results in diverging
diet and physical activity which makes a person not pay attention to his body shape. This method of
calculating the Body Mass Index can be used to determine a person's body shape. Several factors can affect
the value of the Body Mass Index, including individual factors, consumption patterns, and lack of physical
activity which leads to a sedentary lifestyle. These factors are made into 69 itemsets which will be used as
the basis for questions in the questionnaire to collect a dataset that will later be processed using the FP
Growth algorithm and looking for association rules that have the highest support x confidence value. From
the 490 calculation data, the results are categorized into 10, each of which is Men with a Very Thin BMI
with the highest support x confidence value of 39.56%, Men with a Thin BMI of 55.90%, Men with a
Normal BMI of 70%, men with a fat BMI of 49.23%, men with an obese BMI of 41.34%, women with a very
thin BMI of 41.37%, women with a thin BMI of 37.21%, Normal BMI is 68.83%, women with obese BMI
are 41.65%, and women with obese BMI are 42.91%.
Keywords: FP-Growth Algorithm, Data Mining, Associations, Body Mass Index, IMT
PENDAHULUAN
The development of the increasing flow of
globalization in the field of science and technology
as well as increasing incomes have had an impact
on changes in lifestyle, behavior, and situations in
the community (Ayuni, Suharso, & Sukidin, 2019).
These changes have an impact on reduced physical
activity (Suryadinata & Sukarno, 2019) a society
which results in divergent eating patterns and
physical activities that make a person not pay
attention to his body shape (Rahmayanti, 2016)
Some people feel their body is fat when in
fact they are not fat or vice versa (Ariati, Gumala, &
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mailto:desti.fitriati@univpancasila.ac.id1
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Nursanyoto, 2017). This perception arises because
a person does not know how to categorize his body
shape (Nahdiyah, 2015) in the right way. One way
to find out a person's body shape is by calculating
the Body Mass Index method.
This method of calculating the Body Mass
Index can be used to determine a person's
nutritional status (Kusuma & Pinandita, 2011),
(Syahputra & Muhathir, 2018). The results of the
calculation of the Body Mass Index can be
categorized into very thin, skinny, normal,
overweight, and obese body shapes. (Putra &
Solichathi Rizqi, 2018). Among the Body Mass
Index categories, obesity is a problem (Dewi &
Mahmudiono, 2013) where people who are
categorized by body shape have excess nutrition
(Dewi & Mahmudiono, 2013). In public life, the
term obesity is usually called overweight. This
obesity phenomenon is the result of an unbalanced
excessive intake of nutrients (Mahdali, 2019) by
expending energy and can cause health problems
(Hasanah & Febrianti, 2012). A high Body Mass
Index (BMI) value is indicated by a bodyweight
above the average due to the accumulation of
excess fat in the body over a long period. (Jannah &
Utami, 2018)
In 2016, WHO stated that the prevalence
of obesity in the world has more than doubled
since 1980. In 2014, more than 1.9 billion adults
aged 18 years and older were overweight, and 600
million among them are obese. Some of the factors
associated with high Body Mass Index include high
dietary habits and lack of physical activity which
leads to a sedentary lifestyle such as watching
television and playing computer/video games.
This study aims to examine the relationship
between the factors that influence body shape
based on a person's BMI which includes individual
factors, physical activity, eating habits, sedentary
behavior, and stress factors at work using the FP-
Growth algorithm data mining method. Also, this
study provides guidelines and guidelines to
determine what factors influence body shape based
on BMI.
RESEARCH METHODS
Types of research
This research is a type of experimental
quantitative research, where the conclusion of this
study is based on the results of the calculation trial
which is selected the best.
Time and Place of Research
The data collection time in this study was
October 2019.
Research Target / Subject
In this study using non-physical data by
distributing questionnaires that have been
compiled based on the results of a literature study.
Then the questionnaire was distributed to general
people using Google Form. The completed
questionnaires will be collected in a file with the
XLS extension. Subjects were taken randomly to
get various types of body categories.
Procedure
The FP-Growth algorithm uses the concept
of building a tree, which is commonly called the FP-
Tree, in searching for Frequent itemsets instead of
generating candidates as is done in the Apriori
Algorithm. By using this concept, the FP-Growth
algorithm is faster than the Apriori algorithm
(Anggraeni, Iha, Erawati, & Khairunnas, 2019). The
following are the steps for the FP-Growth
algorithm.
1. Determine the minimum support and
minimum confidence.
2. Create an Fp-Tree based on the sorted itemset.
Fp-Tree is formed by a root labeled Null, a
group of trees whose members are certain
items. Each node in the Fp-Tree contains three
important information, namely the item label,
informing the type of item the node represents,
support count, representing the number of
transaction paths that go through the node,
and a pointer (Muliono, 2017).
3. Create a Conditional Pattern Base. Conditional
Pattern Base is a sub-database that contains a
prefix path and a pattern suffix. Generating the
Conditional Pattern Base is obtained through
the previously built Fp-Tree. The Conditional
Pattern Base is obtained from the FP-tree.
4. Create a Conditional Fptree. At this stage, the
support count of each item in each Conditional
Pattern Base is added up, then each item that
has a support count greater than the minimum
support count will be generated with a
conditional FP-tree. The conditional Fp.tree
was obtained from the established fp.tree
(Lestari, 2015).
5. Create a Frequent Pattern. The Frequent
itemset Search Stage If the Conditional Fp-Tree
is a single path, then the Frequent itemset is
obtained by doing a combination of items for
each conditional FP-tree. If it is not a single
track, a Frequent Pattern will be generated.
Frequent Patterns are obtained from the
Conditional Fp-Tree
6. Create association rules. The results of the
Association Rules are obtained from the
conditional Frequent pattern which is entered
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into the support, confidence, lift ratio formula.
Lift ratio is an important parameter besides
support and confidence in the association rule.
The lift ratio measures how important the rule
is based on the value of support and
confidence. The lift ratio is a value that
indicates the validity of the transaction process
and provides information on whether product
A has a relationship with product B. If the
calculation results are looking for lift ratio> 1,
then we can determine a valid rule. The results
of the Association Rules are obtained from the
conditional Frequent pattern. And to find out
the validation of the results of the association
rules, the Support, Confidence, and Lift ratio
values can be calculated.
Data, Instruments, and Data Collection
Techniques
The data used in this study are primary, that is,
the data is taken and processed by the researcher.
The data collection technique used was a randomly
distributed questionnaire form, where the
instrument used was the Google Form.
Data analysis technique
The results of data processing using the FP
Growth algorithm by setting a minimum support
value which is determined based on the upper
quartile calculation of the itemset frequency of
each gender and BMI, and minimum confidence of
80%. Where the minimum 80% confidence
indicates the certainty that the data will appear in
the item set.
RESULTS AND DISCUSSION
The results of the research are presented
in graphical, tabular, or descriptive form. Analysis
and interpretation of these results are required
before the discussion.
Data processing
The data that will be used to predict this is
obtained based on the results of the questionnaire
that the respondents have filled in. The results of
the questionnaire can be divided into 10 types of
respondent characteristics based on gender and
BMI category. The following is the distribution in
Table 1 below.
Table 1. Data Grouping by Gender and BMI
No. Gender BMI
1 Laki- Laki Sangat Kurus
2 Laki- Laki Kurus
3 Laki- Laki Normal
No. Gender BMI
4 Laki- Laki Gemuk
5 Laki- Laki Obesitas
6 Perempuan Sangat Kurus
7 Perempuan Kurus
8 Perempuan Normal
9 Perempuan Gemuk
For the itemset used, there were 69
itemsets, among others, as shown in Table 2 below.
Table 2. List of Itemset Categories
No. Code Itemset
1 A1 Remaja
2 A2 Dewasa
3 B1 Jawa dan Bali
4 B2 Sumatra
5 B3 Kalimantan
6 B4 Papua
7 B5 Sulawesi
8 B6 Maluku
9 B7 Nusa Tenggara
10 B8 Pendatang
11 C1 Tidak Berpendidikan
12 C2 Pendidikan Terakhir SD atau yang
Sederajat
13 C3 Pendidikan Terakhir SMP atau yang
Sederajat
14 C4 Pendidikan Terakhir SMA atau yang
Sederajat
15 C5 Pendidikan Terakhir D3
16 C6 Pendidikan Terakhir D4
17 C7 Pendidikan Terakhir S1
18 C8 Pendidikan Terakhir S2
19 C9 Pendidikan Terakhir S3
20 D1 Perokok Aktif
21 D2 Perokok Pasif
22 D3 Mantan Perokok
23 D4 Tidak Rokok
24 E1 Waktu Tidur Ideal
25 E2 Waktu Tidur Tidak Ideal
26 F1 Makan Pagi Sering
27 F2 Makan Pagi Sebagian Besar Waktu
28 F3 Makan Pagi Jarang
29 F4 Makan Pagi Tidak Pernah
30 G1 Makan Siang Sering
31 G2 Makan Siang Sebagian Besar Waktu
32 G3 Makan Siang Jarang
33 G4 Makan Siang Tidak Pernah
34 H1 Makan Malam Sering
35 H2 Makan Malam Sebagian Besar Waktu
36 H3 Makan Malam Jarang
37 H4 Makan Malam Tidak Pernah
38 I1 Konsumsi Susu Sering
39 I2 Konsumsi Susu Sebagian Besar
Waktu
40 I3 Konsumsi Susu Jarang
41 I4 Konsumsi Susu Tidak Pernah
42 J1 Konsumsi Buah Sering
43 J2 Konsumsi Buah Sebagian Besar
Waktu
44 J3 Konsumsi Buah Jarang
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No. Code Itemset
45 J4 Konsumsi Buah Tidak Pernah
46 K1 Konsumsi Fast Food Sering
47 K2 Konsumsi Fast Food Sebagian Besar
Waktu
48 K3 Konsumsi Fast Food Jarang
49 K4 Konsumsi Fas Food Tidak Pernah
50 L1 Konsumsi Makanan Kecil Sering
51 L2 Konsumsi Makanan Kecil Sebagian
Besar Waktu
52 L3 Konsumsi Makanan Kecil Jarang
53 L4 Konsumsi Makanan Kecil Tidak
Pernah
54 M1 Konsumsi Minuman Berasa Sering
55 M2 Konsumsi Minuman Berasa Sebagian
Besar Waktu
56 M3 Konsumsi Minuman Berasa Jarang
57 M4 Konsumsi Minuman Berasa Tidak
Pernah
58 N1 Konsumsi Soft Drink Sering
59 N2 Konsumsi Soft Drink Sebagian Besar
Waktu
60 N3 Konsumsi Soft Drink Jarang
61 N4 Konsumsi Soft Drink Tidak Pernah
62 O1 Sedentary Behavior Rendah
63 O2 Sedentary Behavior Tinggi
64 P1 Aktivitas Fisik Ringan
65 P2 Aktivitas Fisik Kuat
66 P3 Aktivitas Fisik Sedang
67 Q1 Stress Ringan
68 Q2 Stress Sedang
69 Q3 Stress Berat
System Design
The architecture of this system contains
four kinds of data which can be seen in Figure 1,
namely Login, category management, management
dataset, and calculation of FP Growth. This login
process is only done by the admin. Admin is
required to log in before entering the system. The
admin in this system is only one condition and
cannot be added so that it does not require user
management.
There are two data management systems
for this system, namely the management data
category and the management dataset. Categorical
data that contains factors that affect the value of
the Body Mass Index. While the dataset contains
data from the distribution of questionnaires filled
out by respondents.
Finally, the calculation of FP Growth,
which contains the process of calculating FP-
Growth from the tb_relation data, which contains
data from the results of the respondent's
questionnaire and is related to tb_data and
tb_category. The calculation of FP-Growth is
carried out to see the results of the association
between the factors that affect the Body Mass
Index value.
Figure 1. Software Architecture
The following is a snippet of the FP-Growth
program:
<?php
class Fpg extends CI_Controller {
public function __construct()
{
parent::__construct();
if(!$this->session
>userdata('login'))
redirect('user/login');
$this->load->model('data_model');
$this->load
>model('kategori_model');
$this->load->helper('fpgrowth');
$this->load->model('relasi_model');
}
public function index() {
$data['title'] = 'Perhitungan FP-
Growth';
$this->form_validation->set_rules(
'min_support', 'Minimal Support', 'required'
);
$this->form_validation->set_rules(
'min_confidence', 'Minimal Confidence',
'required' );
if ($this->form_validation->run()=== FALSE)
{
load_view('fpg', $data);
}
else
{
$data['data'] = $this->relasi_model >
get_relasi_filter($this->input->post('jk'),
$this->input->post('imt'));
$data['kategori'] = $this->kategori_model-
>tampil();
$this->load->view('header', $data);
$this->load->view('fpg');
$this->load->view('fpg_hasil');
$this->load->view('footer');
}
}
}
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Implementation Program
1. FP-Growth Calculation page
Figure 2. FP-Growth Calculation Page Interface
Figure 2 is the initial entry before calculating the
FP Growth according to the selected category,
where the entries include setting Gender, BMI
Category, the minimum value of support, and
minimum confidence. In this study, we will look
for the associated value of the factors that affect
the value of BMI based on gender and BMI
category.
2. The interface of FP-Growth for Ordered
Itemset Stage
Figure 3 Detailed Interface Implementation
After making the initial settings, then the item
combination process is carried out as seen in
Figure 3 above.
3. FP-Growth Calculation Interface for
Conditional FP-Tree Stage
Figure 4 FP-Growth Calculation Interface for
Conditional FP-Tree Stage
After obtaining an ordered itemset, the next step
is to calculate the conditional pattern base and
conditional FP-Tree to get the rules as shown in
Figure 4.
4. FP-Growth Calculation Interface Frequency
Pattern Stage
Figure 5 Formation Interface for FP-Growth Stage
Frequency Pattern
After getting the rules, then this algorithm starts
making Frequency Patterns. Where at this stage
you will see several itemsets that are always close
together and the number of occurrences is
calculated simultaneously. The frequency pattern
stage can be seen in Figure 5 above.
5. FP-Growth Calculation Interface Association
Rules Stage
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Figure 6 The interface of FP-Growth Calculation of
Association Rules Stage
The last step to take is to find association rules that
match the predetermined minimum support and
minimum confidence. The results of the final
association are taken for itemset that has a lift ratio
value of more than 1. Figure 6 shows the results of
the rules taken, where the itemset that is blocked
or colored orange are itemsets that do not meet the
requirements.
Results Analysis
Based on the test results by setting a minimum
confidence value of 80%, the following results
were obtained:
1. The results of system testing for the male sex
group with a very thin BMI category with a
minimum support value of 16, the highest
support x confidence results are 39.56% in the
association rule "If you eat dinner often then
ideal sleep time".
2. the results of the system test for the male sex
group with the thin BMI category with a
minimum support value of 20, the highest
support x confidence results are 55.90% in the
association rules "If the last education is a high
school or equivalent, then the adolescent age"
3. the results of system testing for the male sex
group with the Normal BMI category with a
minimum support value of 20, the results
obtained support x confidence of 70% in the
association rules "If the Age is Adolescent then
the Last Education is High School or the
Equivalent"
4. the results of the system test for the male sex
group with the Fat BMI category with a
minimum support value of 22, the highest
support x confidence results are 49.23% on the
association rule "If lunch is frequent, then
adult age"
5. the results of system testing for the male sex
group with the category of BMI Obesity with a
minimum support value of 23, the highest
support x confidence results are 41.34% in the
association rules "If you are a teenager then
the last education is high school or equivalent"
6. The results of system testing for the female sex
group in the Very Thin BMI category with a
minimum support value of 17, the highest
support x confidence results are 41.37% in the
association rule "If Sedentary Behavior is High,
then Stress is Moderate"
7. The results of system testing for the female sex
group with the Normal BMI category with a
minimum support value of 19, the highest
support x confidence results are 37.21% in the
association rule "If you consume milk most of
the time then moderate stress"
8. the results of the system testing for the female
sex group with the thin BMI category with a
minimum support value of 20, the highest
support x confidence results are 68.83% in the
association rules "If the last education is a high
school or equivalent, then the teenage age"
9. the results of system testing for the female sex
group with the Fat BMI category with a
minimum support value of 22, the highest
support x confidence results are 41.65% in the
association rules "If you are an adult then don't
smoke"
10. The results of system testing in the female sex
group with the BMI category of Obesity with a
minimum support value of 24 obtained the
highest support x confidence results of 42.91%
in the association rule "If it is ideal sleep time
then the adult age"
CONCLUSIONS AND SUGGESTIONS
Conclusion
From the 490 calculation data, the results
are categorized into 10, each of which is Men with
a Very Thin BMI with the highest support x
confidence value of 39.56%, Men with a Thin BMI
of 55.90%, Men with a Normal BMI of 70%, men
with a fat BMI of 49.23%, men with an obese BMI
of 41.34%, women with a very thin BMI of 41.37%,
women with a thin BMI of 37.21%, Normal BMI is
68.83%, women with obese BMI are 41.65%, and
women with obese BMI are 42.91%.
Suggestion
It is necessary to distribute questionnaires
more widely to add more data to obtain the results
of the association rules which have a higher
support x confidence value in each grouping based
on the Gender and Body Mass Index Category.
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