177 

 

 

COMPARISON OF BREAST CANCER CLASSIFICATION USING DECISION 
TREE ID3 AND K-NEAREST NEIGHBORS ALGORITHM TO PREDICT THE 

BEST PERFORMANCE OF ALGORITHM 
 

Zyhan Faradilla Daldiri 1*) , Desti Fitriati 2 
 

Informatics Engineering 1,2  
Universitas Pancasila 1,2 

Jakarta, Indonesia 
zyhandilla1308@gmail.com 1*) , desti.fitriati@univpancasila.ac.id 2 

 
(*) Corresponding Author 

 
Abstract 

One of the leading causes of death is cancer. The most common cancer in women is breast cancer. Breast 
cancer (Carcinoma mammae) is a malignant neoplasm originating from the parenchyma. Breast cancer 
ranks first in terms of the highest number of cancers in Indonesia and is among the first contributors to 
cancer deaths. Globocan data in 2020 shows that the number of new breast cancer cases reached 68,858 
(16.6%) of the total 396,914 new cancer cases in Indonesia. Meanwhile, deaths reached more than 22 
thousand cases (Romkom, 2022). This death rate is increasing due to insufficient information about breast 
cancer’s early symptoms and dangers. Of this lack of information, a system is needed that can provide 
information about breast cancer, such as early diagnosis. Several parameters and classification data mining 
techniques can predict which patients will develop breast cancer and which do not. In this study, a 
comparison of the classification of breast cancer using the Decision Tree ID3 algorithm and the K-Nearest 
Neighbors algorithm will be carried out. Attribute data consists of Menopause, Tumor-Size, Node-Caps, 
Deg-Malig, Breast-Squad, and Irradiant. The main objective of this study is to improve classification 
performance in breast cancer diagnosis by applying feature selection to several classification algorithms. 
The Decision Tree ID3 algorithm has an accuracy rate of 93.333%, and the K-Nearest Neighbors algorithm 
has an accuracy rate of 76.6667%. 
 
Keywords: Breast cancer; Woman; Classification; Comparison; Decision Tree Algorithm; K-Nearest 
Neighbors Algorithm 
 

Abstrak 
Salah satu penyebab kematian yang utama adalah kanker. Kanker yang paling umum pada wanita adalah 
kanker payudara. Kanker payudara (Carcinoma mammae) didefinisikan sebagai suatu penyakit neoplasma 
ganas yang berasal dari parenchyma. Kanker payudara menempati urutan pertama terkait jumlah kanker 
terbanyak di Indonesia serta menjadi salah satu penyumbang kematian pertama akibat kanker.  Data 
Globocan tahun 2020, jumlah kasus baru kanker payudara mencapai 68.858 kasus (16,6%) dari total 396.914 
kasus baru kanker di Indonesia. Sementara itu, untuk jumlah kematiannya mencapai lebih dari 22 ribu jiwa 
kasus(Romkom,2022). Angka kematian ini meningkat karena kurangnya informasi tentang gejala awal dan 
bahaya dari kanker payudara itu sendiri. Dari kurangnya informasi tersebut, maka dibutuhkan sebuah sistem 
yang dapat memberikan informasi tentang penyakit kanker payudara seperti diagnosa secara dini. Teknik 
data mining klasifikasi dapat digunakan untuk memprediksi pasien mana yang terkena kanker payudara dan 
tidak dengan beberapa parameter yang ada. Dalam penelitian ini akan dilakukan perbandingan klasifikasi 
penyakit kanker payudara dengan menggunakan algoritma Decision Tree ID3 dan algoritma K-Nearest 
Neighbors. Atribut data yang digunakan terdiri dari Menopause, Tumor-Size, Node-Caps, Deg-Malig, Breast, 
Breast-Squad dan Irradiant. Tujuan utama penelitian ini adalah untuk meningkatkan peforma klasifikasi 
pada diagnosis kanker payudara dengan menerapkan seleksi fitur pada beberapa algoritma klasifikasi. 
Algoritma Decision Tree ID3 memiliki tingkat akurasi yaitu 93,333% dan algoritma K-Nearest Neighbors 
memilki tingkat akurasi yaitu 76,6667%. 
 
Kata kunci : Kanker Payudara; Wanita; Klasifikasi; Perbandingan;  Algoritma Decision Tree; Algoritma K-
Nearest Neighbors 
 



 

 
178 

 

 

INTRODUCTION 
 

Information technology continues to 
progress from time to time. Information technology 
has provided various helpful information and data 
for people’s lives. Technology also plays a role in 
advancing various institutions, one of which is 
health agencies. The data generated in the health 
sector can be in the form of data about diseases that 
are considered deadly such as cancer, which of 
course this data can be used to dig deeper into 
information related to cancer itself, both for the 
treatment or prevention of patients who have not or 
have experienced cancer (Ramadhan & Kurniawati, 
2020). 

Information from the data can be used to 
find new patterns of information by processing or 
extracting information. This new pattern can be 
used to classify cancer patients based on recurrence 
or non-recurrence of the disease. This knowledge 
can help the medical side handle patients to 
minimize the number of cancer patients who 
experience recurrence of the disease. 

Currently, breast cancer is a type of cancer 
that is very frightening for women around the 
world, and this also applies in Indonesia. Cancer 
prevention can be done early by being aware of 
cancer at its initial appearance so that it can have a 
high cure rate (Hasanah, 2018). Therefore, it is 
necessary to carry out prevention efforts to increase 
public awareness in recognizing the symptoms and 
risks, especially breast cancer, in determining 
appropriate preventive measures and early 
detection. 

Breast cancer is a malignant tumor formed 
from breast cells that grow and develop 
uncontrollably so that it can spread to organs near 
the breast or to other parts of the body (Buana 
Briliant, 2020). Breast cancer is still a disease with a 
high mortality rate in women. Based on data from 
the WHO (World Health Organization), in 2020, the 
number of breast cancer cases will reach 68,858 
(16.6%) of 396,914 new cancer cases in Indonesia. 
Meanwhile, deaths reached more than 22 thousand 
cases (Kementrian Kesehatan Indonesia, 2022). 

Judging the significant mortality rate in 
Indonesia makes us aware of the importance of 
knowing the symptoms of breast cancer to prevent 
increased deaths caused by breast cancer. The 
research data is used to test the accuracy of breast 
cancer diagnosis using public data from the UCI 
Machine Learning Repository breast cancer dataset. 
The dataset is tested by processing the data using 
data mining. 

Data mining is a series of processes to find 
values in data sets that are not known manually 
(Prakarsya & Prambayun, 2020). So that the breast 

cancer dataset that has extensive data can be 
analyzed in making decisions using data mining. 
One of the data mining methods in the data 
exploration process is the data classification 
technique. 

With this data classification technique, it is 
possible to classify the breast cancer dataset using 
the ID3 Decision Tree algorithm and the K-Nearest 
Neighbors algorithm with the results of the accuracy 
of the classification of each of these algorithms. This 
study will be compared for the best performance 
evaluation in breast cancer detection. Thus, the 
results of this comparison can be used as a reference 
for diagnosing breast cancer at an early stage. 
Research objectives and methods used 
1. To find out which algorithm performance 

produces higher accuracy. 
2. To improve classification performance in breast 

cancer diagnosis. 
 Benefits of research 
1. Assist in developing further research related to 

algorithm comparison problems. 
2. It can be used as a reference in research on 

breast cancer. 
Research contribution 

This study can provide knowledge about 
the results of research that has been carried out in 
obtaining information about comparing the K-
Neirest Neighbors Algorithm and the ID3 Decision 
Tree Algorithm in classifying breast cancer. 
 

RESEARCH METHODS 
 

Types of research 
The data obtained in this study were 

sourced from the UCI Machine Learning Repository 
website, entitled breast cancer dataset. The data 
taken is data randomly seen from the classification 
results obtained. 

 
Research Time and Place 

The study was conducted for two weeks, 
the first week at the end of May 2022 and the second 
week at the beginning of June 2022. The study was 
carried out at home by analyzing and calculating the 
data obtained. 

 
Data Mining 

Data mining is looking for information 
models that can be useful in database storage. Data 
mining is one of a series of knowledge search 
processes in a database known as Knowledge 
Discovery In Database (KDD). KDD relates to 
integration techniques and scientific discoveries for 
interpretation and visualization so that they are 
easy to understand (Zulfa et al., 2020). Some 
techniques frequently mentioned in the Data Mining 



 

 
179 

 

 

literature include clustering, classification, 
association rule mining, and neural networks. 

 
Classification 

Classification is a data processing 
technique that determines data into predetermined 
groups or classes (Bahri & Lubis, 2020). This 
method helps produce functions or models that 
explain classes in data used to predict classes of 
objects that have not been labeled (Arie Wijaya et 
al., 2021). The classification method refers to 
forming data groups by applying algorithms in the 
classification process. 

 
Decision Tree 

Decision Tree ID3 is a tree modeling 
technique that can implement a series of decisions. 
The ID3 Decision Tree has tree nodes that represent 
the attributes that have been tested, and each 
branch is a division of the test results, and leaf nodes 
represent certain class groups (Setio et al., 2020). 
The process in the ID3 Decision Tree is to change the 
form of a data table into a tree model (Rionaldi, 
2022). In the calculation phase using the Decision 
Tree ID3 algorithm, there are several stages, 
namely: 
1. Specifies attribute data with data values in the 

dataset. 
2. Looking for the same data. 
3. Searching for data is false. 
4. Searching for data is accurate. 
5. Calculate entropy with the following formula 

(1): 
 

Entropy (S) = −
𝑁𝑚𝑗

𝑁
∑𝑖 𝑃𝑚𝑗

(𝑖)
 𝑙𝑜𝑔2 𝑃𝑚𝑗

(𝑖)
  ...................... (1) 

 
6. Add up the entropy results. 
7. Make a decision tree according to the results 

obtained. 
 

K-Nearest Neighbors 
The K-Nearest Neighbor (KNN) algorithm is 

a case search approach that calculates the closeness 
between new and old cases based on matching the 
weights of several existing features (Setiawan et al., 
2020). K in K-NN is the number of neighbors that 
will be taken to determine decisions (Mustafa & 
Wayan Simpen, 2019). The K-NN algorithm in this 
study has several steps, which are as follows: 
1. Converting qualitative data into quantitative 

data. 
2. Calculate using the Euclidean formula on the 

data. The Euclidean formula is as follows: 
 

𝑑(𝑝, 𝑞) =  √∑ (𝑞𝑖 − 𝑝𝑖)2𝑛𝑖=1    ........................................ (2) 

 

3. Determine the closest Distance from the results 
of the Euclidean formula calculation (2). 

4. Determine the K to be taken. 
5. Determine the existing prediction results by the 

taken K. 
 

Procedure 
 In this study, a comparison was made 
between the Decision Tree ID3 algorithm and the K-
Nearest Neighbors algorithm to get the best 
classification results. The stages used in this 
research are: 
1. Dataset processing. 
2. Calculations using the Decision Tree ID3 

algorithm. 
3. Calculations using the K-NN algorithm. 
4. Perform data comparisons using the two 

algorithms. 
5. Calculate the accuracy (3) of the two algorithms 

with the following formula: 
 

 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
𝑇𝑃+𝑇𝑁

𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁
𝑥100% ............................... (3) 

 
 
Data, Instruments, and Data Collection 
Techniques 

This study uses primary data and as many 
as 30 test data. The technique of collecting criteria 
and alternative data to be used in system testing 
was obtained by taking a dataset from the UCI 
Machine Learning Repository site, namely 
https://archive.ics.uci.edu/ml/datasets/breast+ca
ncer. For information on breast cancer data, this 
study used six attributes, namely Menopause, 
Tumor-Size, Node-Caps, Deg-Malig, Breast, Breast-
Squad, and Irradiant. 

 
Data analysis technique 

The data in this study consisted of 
quantitative and qualitative data. For qualitative 
data, researchers convert them into numbers 
(quantitative data) to make it easier to calculate 
both manual and system calculations. 
 
 

RESULTS AND DISCUSSION 
 
Based on the research stages, the first step is 
processing breast cancer datasets. The attributes of 
the dataset can be seen in Table 1. 
 
 
 

Table 1. Attributes of the Breast Cancer 
Dataset 

Attribute 
Menopause 



 

 
180 

 

 

Attribute 
Tumor Size 
Node caps 
Deg Malig 

breast 
Breast Squad 

 

From these attributes, 30 data samples 
were taken randomly to be tested. The sample data 
can be seen in Table 2. 
 
 
 
 

 
Table 2. Breast Cancer Sample Data 

Menopause Tumor 
size 

inv-
nodes 

node-
caps 

deg-
malig 

breast breast squad irradiat 

ge40 15-19 0-2 no 2 right left_up no 
ge40 25-29 0-2 no 3 left right_up no 
ge40 30-34 0-2 no 1 right right_up no 
ge40 30-34 0-2 no 2 left left_low yes 
ge40 25-29 0-2 no 3 left right_low yes 
ge40 30-34 0-2 no 3 right left_up yes 
ge40 15-19 0-2 no 3 right left_up yes 
ge40 30-34 0-2 no 1 right Central no 
ge40 15-19 0-2 no 1 left right_low no 
ge40 20-24 0-2 no 1 left left_low no 
ge40 15-19 0-2 no 3 right left_up yes 
ge40 15-19 0-2 no 2 left left_up yes 
ge40 30-34 0-2 yes 2 right right_up yes 
lt40 30-34 0-2 no 1 left left_low no 
lt40 15-19 0-2 no 2 left left_low no 
lt40 30-34 0-2 no 3 right left_up no 
lt40 15-19 0-2 no 3 right left_up no 
premeno 30-34 0-2 no 2 right right_up yes 
premeno 20-24 0-2 no 3 right left_low yes 
premeno 35-39 0-2 yes 3 right left_up yes 
premeno 35-39 0-2 yes 3 right left_low yes 
premeno 25-29 0-2 no 1 right left_low yes 
premeno 25-29 0-2 no 2 left left_up yes 
premeno 20-24 0-2 no 2 left right_low no 
premeno 30-34 0-2 no 3 right left_up yes 
premeno 20-24 0-2 no 1 left right_low no 
premeno 15-19 0-2 no 1 left left_low no 
premeno 20-24 0-2 no 2 left Central no 
premeno 35-39 0-2 no 2 right right_up no 
premeno 20-24 0-2 no 3 left left_up yes 

After taking the test data, the next step is 
calculating the data classification using the Decision 
Tree ID3 algorithm. The first stage in Decision Tree 
ID3, which determines data attributes with values in 
the test data, can be seen in Table 3. 
 

Table 3. Value Data Based on Attributes 
Attribute (m) Value Data (j) 
Menopause Ge40; Lt40; Premeno 
Tumor Size 15-19; 20-24; 25-29 

30-34; 35-39 
Node Caps Yes; No 
Deg Malig 1; 2; 3 
breast Left; Right 
Breast Squad Left Low; Left Up; Central 

Right Low; Right Up 

 
After determining the value of the test data 

according to the attributes, calculations can be made 
to determine the same data, determine false data, 
determine accurate data, calculate entropy, and add 
up the results of the entropy values in each of the 
data attributes with N as the amount of data being 
tested. The results can be seen in Table 4. 
 
 
 
 
 
 
 

Table 4. Entropy Calculation Results 
m j Nmj/N FALSE TRUE entropy 

 

Menopause ge40 13/30 6/13 7/13 0,4314818959 0,848017758 



 

 
181 

 

 

m j Nmj/N FALSE TRUE entropy 
 

lt40 4/30 4/4 0/4 0 
premeno 13/30 5/13 8/13 0,416535862 

Tumor Size 15-19 8/30 5/8 3/8 0,2545157341 

0,9354977189 
20-24 6/30 4/6 2/6 0,1836591668 
25-29 4/30 1/4 3/4 0,1081704166 
30-34 9/30 4/9 5/9 0,297322818 
35-39 3/30 1/3 2/3 0,09182958341 

Node-caps yes 3/30 0/3 3/3 0 
0,8919684539 

no 27/30 15/27 12/27 0,8919684539 
Deg-malig 1 8/30 7/8 1/8 0,1449505182 

0,8027951013 2 10/30 5/10 5/10 0,3333333333 
3 12/30 3/12 9/12 0,3245112498 

Breast left 14/30 9/14 5/14 0,438800114 
0,9478315823 

right 16/30 6/16 10/16 0,5090314682 
Breast-
squad 

left low 8/30 4/8 4/8 0,2666666667 

0,8466241924 
left up 11/30 3/11 8/11 0,3099620101 
Central 2/30 2/2 0/2 0 
right low 4/30 3/4 1/4 0,1081704166 
right up 5/30 3/5 2/5 0,1618250991 

 
From the results of the entropy calculation, 

tree data from deg-malig can be taken because the 
results obtained have the smallest value. Then 

proceed with calculating the entropy again based on 
the data from the deg-malig with value = 1, which 
can be seen in Table 5.

 
Table 5. Data Based on Deg Malig Attribute With Value=1 

Menopause Tumor size inv-nodes node-caps deg-malig breast breast squad irradiat 
ge40 30-34 0-2 no 1 right right_up no 
ge40 30-34 0-2 no 1 right Central no 
ge40 15-19 0-2 no 1 left right_low no 
ge40 20-24 0-2 no 1 left left_low no 
lt40 30-34 0-2 no 1 left left_low no 
premeno 25-29 0-2 no 1 right left_low yes 
premeno 20-24 0-2 no 1 left right_low no 
premeno 15-19 0-2 no 1 left left_low no 

 
From this data, entropy calculations can be 

made based on the values in the deg-malig attribute 
data. The results of the calculation of the deg-malig 
attribute data with value = 1 can be seen in Table 6. 
 

Table 6. Entropy Calculation Results Based on Deg Malig Data With Value=1 
m j Nmj/N FALSE TRUE entropy 

 

Menopause ge40 4/8 4/4 0/4 0 
0,3443609378 lt40 1/8 1/1 0/1 0 

premeno 3/8 2/3 1/3 0,3443609378 
Tumor Size 15-19 2/8 2/2 0/2 0 

0 
20-24 2/8 2/2 0/2 0 
25-29 1/8 0/1 1/1 0 
30-34 3/8 3/3 0/3 0 

Breast left 5/8 5/5 0/5 0 
0,3443609378 

right 3/8 2/3 1/3 0,3443609378 
Breast-squad left low 4/8 3/4 1/4 0,4056390622 

0,4056390622 
central 1/8 1/1 0/1 0 
right low 2/8 2/2 0/2 0 
right up 1/8 1/1 0/1 0 

 
Repeated patterns can be used to calculate 

the entropy of the deg-malig attribute data with 
value=2 and value=3. After these calculations, the 
tree results will be obtained as in Figure 1. 
 



 

 
182 

 

 

 
Figure 1. Classification of ID3 Decision Tree Results 
 

Furthermore, testing the existing data with 
the KNN algorithm by performing the steps on the 
mentioned methodology by changing the qualitative 
data into quantitative ones. The data is as in Table 7. 

 
 
 
 

 

Table 7. Changes in Value Data 
Attribute Value 

(Qualitative) 
Value 
(Quantitative) 

Menopause Ge40 
Lt40 
Premeno 

1 
2 
3 

Tumor Size 15-19 
20-24 
25-29 
30-34 
35-39 

1 
2 
3 
4 
5 

Node Caps Yes 
No 

1 
2 

breast Left 
Right 

1 
2 

Breast Squad Left Low 
Left Up 
Central 
Right Low 
Right Up 

1 
2 
3 
4 
5 

 
After changing the value of the data, the test 

dataset will be like in Table 8.

 
 Table 8. Data Changes Based on Quantitative Value Data 

Menopause Tumor size inv-nodes node-caps deg-malig breast breast 
squad 

1 1 0-2 2 2 2 2 
1 3 0-2 2 3 1 5 
1 4 0-2 2 1 2 5 
1 4 0-2 2 2 1 1 
1 3 0-2 2 3 1 4 
1 4 0-2 2 3 2 2 
1 1 0-2 2 3 2 2 
1 4 0-2 2 1 2 3 
1 1 0-2 2 1 1 4 
1 2 0-2 2 1 1 1 
1 1 0-2 2 3 2 2 
1 1 0-2 2 2 1 2 
1 4 0-2 1 2 2 5 
2 4 0-2 2 1 1 1 
2 1 0-2 2 2 1 1 
2 4 0-2 2 3 2 2 
2 1 0-2 2 3 2 2 
3 4 0-2 2 2 2 5 
3 2 0-2 2 3 2 1 
3 5 0-2 1 3 2 2 
3 5 0-2 1 3 2 1 
3 3 0-2 2 1 2 1 
3 3 0-2 2 2 1 2 
3 2 0-2 2 2 1 4 
3 4 0-2 2 3 2 2 
3 2 0-2 2 1 1 4 
3 1 0-2 2 1 1 1 
3 2 0-2 2 2 1 3 
3 5 0-2 2 2 2 5 
3 2 0-2 2 3 1 2 

 
 
 
 

The change in the data value can be 
calculated using the Euclidean formula, determining 
the closest Distance in ascending order and the 



 

 
183 

 

 

result of the specified K. The results of these 
calculations are as follows.

Table 9. KNN Calculation Results With 1st Data Trial 
Menopause Tumor 

size 
inv-

nodes 
node-
caps 

deg-
malig 

breast breast 
squad 

irradiat Euclidean Nearest 
Distance 

(k) 

k=5 Classify 

1 1 0-2 2 2 2 2 N 0 1 Y N 
1 3 0-2 2 3 1 5 N 15 24 N 

 

1 4 0-2 2 1 2 5 N 19 25 N 
 

1 4 0-2 2 2 1 1 Y 11 18 N 
 

1 3 0-2 2 3 1 4 Y 10 14 N 
 

1 4 0-2 2 3 2 2 Y 10 14 N 
 

1 1 0-2 2 3 2 2 Y 1 2 Y Y 
1 4 0-2 2 1 2 3 N 11 18 N 

 

1 1 0-2 2 1 1 4 N 6 8 N 
 

1 2 0-2 2 1 1 1 N 4 7 N 
 

1 1 0-2 2 3 2 2 Y 1 2 Y Y 
1 1 0-2 2 2 1 2 Y 1 2 Y Y 
1 4 0-2 1 2 2 5 Y 19 25 N 

 

2 4 0-2 2 1 1 1 N 13 22 N 
 

2 1 0-2 2 2 1 1 N 3 6 N 
 

2 4 0-2 2 3 2 2 N 11 18 N 
 

2 1 0-2 2 3 2 2 N 2 5 Y N 
3 4 0-2 2 2 2 5 Y 22 27 N 

 

3 2 0-2 2 3 2 1 Y 7 9 N 
 

3 5 0-2 1 3 2 2 Y 22 27 N 
 

3 5 0-2 1 3 2 1 Y 23 29 N 
 

3 3 0-2 2 1 2 1 Y 10 14 N 
 

3 3 0-2 2 2 1 2 Y 9 13 N 
 

3 2 0-2 2 2 1 4 N 10 14 N 
 

3 4 0-2 2 3 2 2 Y 14 23 N 
 

3 2 0-2 2 1 1 4 N 11 18 N 
 

3 1 0-2 2 1 1 1 N 7 9 N 
 

3 2 0-2 2 2 1 3 N 7 9 N 
 

3 5 0-2 2 2 2 5 N 29 30 N 
 

3 2 0-2 2 3 1 2 Y 7 9 N 
 

 
Judging from Table 9, the KNN calculation 

is based on the test data on the first data to find out 
the prediction results obtained in the KNN 
classification. In this study, 30 existing data will be 
tested using the KNN algorithm so that the 

calculation is repeated with test data on the 2nd to 
30th data. After calculating the KNN classification 
data on the 30 data, the KNN classification can be 
generated in Table 10. 
 

 
Table 10. KNN Calculation Classification Results 

No Menopause Tumor 
size 

inv-
nodes 

node-
caps 

deg-
malig 

breast breast 
squad 

irradiat Result 

1 ge40 15-19 0-2 N 2 right left_up N Y 
2 ge40 25-29 0-2 N 3 left right_up N Y 
3 ge40 30-34 0-2 N 1 right right_up N N 
4 ge40 30-34 0-2 N 2 left left_low Y N 
5 ge40 25-29 0-2 N 3 left right_low Y Y 
6 ge40 30-34 0-2 N 3 right left_up Y Y 
7 ge40 15-19 0-2 N 3 right left_up Y Y 
8 ge40 30-34 0-2 N 1 right Central N N 
9 ge40 15-19 0-2 N 1 left right_low N N 

10 ge40 20-24 0-2 N 1 left left_low N N 
11 ge40 15-19 0-2 N 3 right left_up Y Y 
12 ge40 15-19 0-2 N 2 left left_up Y Y 
13 ge40 30-34 0-2 Y 2 right right_up Y Y 
14 lt40 30-34 0-2 N 1 left left_low N Y 



 

 
184 

 

 

No Menopause Tumor 
size 

inv-
nodes 

node-
caps 

deg-
malig 

breast breast 
squad 

irradiat Result 

15 lt40 15-19 0-2 N 2 left left_low N N 
16 lt40 30-34 0-2 N 3 right left_up N Y 
17 lt40 15-19 0-2 N 3 right left_up N Y 
18 premeno 30-34 0-2 N 2 right right_up Y Y 
19 premeno 20-24 0-2 N 3 right left_low Y Y 
20 premeno 35-39 0-2 Y 3 right left_up Y Y 
21 premeno 35-39 0-2 Y 3 right left_low Y Y 
22 premeno 25-29 0-2 no 1 right left_low Y Y 
23 premeno 25-29 0-2 no 2 left left_up Y Y 
24 premeno 20-24 0-2 no 2 left right_low N N 
25 premeno 30-34 0-2 no 3 right left_up Y Y 
26 premeno 20-24 0-2 no 1 left right_low N N 
27 premeno 15-19 0-2 no 1 left left_low N Y 
28 premeno 20-24 0-2 no 2 left Central N N 
29 premeno 35-39 0-2 no 2 right right_up N N 
30 premeno 20-24 0-2 no 3 left left_up Y Y 

 
This study compared test data using the 

Decision Tree ID3 algorithm and the KNN algorithm 
as the classification method. The results of the 
predictions using the Decision Tree ID3 algorithm 

and the KNN algorithm are equated with the results 
of the existing test data so that the data is obtained, 
like in Table 11. 
 

 
Table 11. Data Comparison 

No Menopause Tumor 
size 

inv-
nodes 

node-
caps 

deg-
malig 

breast breast 
squad 

irradiat ID3 KNN 

1 ge40 15-19 0-2 no 2 right left_up no no yes 
2 ge40 25-29 0-2 no 3 left right_up no yes yes 
3 ge40 30-34 0-2 no 1 right right_up no no no 
4 ge40 30-34 0-2 no 2 left left_low yes yes no 
5 ge40 25-29 0-2 no 3 left right_low yes yes yes 
6 ge40 30-34 0-2 no 3 right left_up yes yes yes 
7 ge40 15-19 0-2 no 3 right left_up yes yes yes 
8 ge40 30-34 0-2 no 1 right Central no no no 
9 ge40 15-19 0-2 no 1 left right_low no no no 

10 ge40 20-24 0-2 no 1 left left_low no no no 
11 ge40 15-19 0-2 no 3 right left_up yes yes yes 
12 ge40 15-19 0-2 no 2 left left_up yes no yes 
13 ge40 30-34 0-2 yes 2 right right_up yes yes yes 
14 lt40 30-34 0-2 no 1 left left_low no no yes 
15 lt40 15-19 0-2 no 2 left left_low no no no 
16 lt40 30-34 0-2 no 3 right left_up no no yes 
17 lt40 15-19 0-2 no 3 right left_up no no yes 
18 premeno 30-34 0-2 no 2 right right_up yes yes yes 
19 premeno 20-24 0-2 no 3 right left_low yes yes yes 
20 premeno 35-39 0-2 yes 3 right left_up yes yes yes 
21 premeno 35-39 0-2 yes 3 right left_low yes yes yes 
22 premeno 25-29 0-2 no 1 right left_low yes yes yes 
23 premeno 25-29 0-2 no 2 left left_up yes yes yes 
24 premeno 20-24 0-2 no 2 left right_low no no no 
25 premeno 30-34 0-2 no 3 right left_up yes yes yes 
26 premeno 20-24 0-2 no 1 left right_low no no no 
27 premeno 15-19 0-2 no 1 left left_low no no yes 
28 premeno 20-24 0-2 no 2 left Central no no no 
29 premeno 35-39 0-2 no 2 right right_up no no no 
30 premeno 20-24 0-2 no 3 left left_up yes yes yes 

The accuracy between the algorithms is 
calculated from the comparison data to determine 
which algorithm is more accurate. The accuracy test 
is carried out to measure how much accuracy is 

obtained from the tests carried out. Accuracy can be 
calculated using the formula (3) above. By 
calculating the accuracy, the accuracy of the 



 

 
185 

 

 

Decision Tree ID3 algorithm is 93.333%, while the 
accuracy of the KNN algorithm is 76.6667%. 
 

CONCLUSIONS AND SUGGESTIONS 
 
Conclusions 

From this research, it can be concluded that 
the classification calculation using the Decision Tree 
ID3 Algorithm has more accurate results than the 
classification calculation using the KNN Algorithm 
on the breast cancer dataset. These results can be 
seen through the accuracy value obtained by 
calculating the classification using the Decision Tree 
ID3 algorithm, which results in 93.333% having a 
more excellent accuracy value than the KNN 
algorithm, which results in 76.6667%. So it can be 
said that using Decision Tree ID3 classification 
calculations on breast cancer estimates has accurate 
results on the incidence of breast cancer so that it 
can be used as a reference in predicting and 
preventing breast cancer early.  
 
Suggestions 

In further research, the classification 
algorithm with Decision Tree ID3 is expected to be 
used in the health sector and other fields by re-
comparing other classification algorithms to find 
the best accuracy results. 
 

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