Available via license: CC BY-NC-ND 4.0
Content may be subject to copyright.
406
APPLICATION OF THE C5.0 ALGORITHM TO DETERMINE GOOD OR BAD ON 5S AUDIT RESULTS
Indra Aliyudin 1), Ari Purno Wahyu 2)
APPLICATION OF THE C5.0 ALGORITHM TO DETERMINE GOOD OR BAD ON 5S
AUDIT RESULTS
Oleh:
Indra Aliyudin 1)
Ari Purno Wahyu 2)
Universitas Widyatama, Bandung 1,2)
E-mail:
indra.aliyudin@widyatama.ac.id 1)
aripurnowahyu@gmail.com 2)
ABSTRACT
Artificial Intelligence is currently growing and is widely used in various aspects of life in
society. Likewise in today's corporate environment, we must be good at managing all activities
so that AI can help in lightening and streamlining decision making at work. In terms of
lightening this work, it is in the aspect of data management and data analysis. AI provides
many methods and ways to analyze data so that the data can be used as a reference for
employee self-assessment or even as a determinant of a company's business going forward.
This study discusses the C5.0 algorithm which is implemented or tested against the 5S (Short,
Set in Order, Shine, Standardize and Sustain) audit data set obtained from the company P.T.
Bekaert Indonesia. This study uses two types of methods from the C5.0 algorithm model as a
reference, namely the tree-based model and the rule-based model, besides that this study uses
the cross fold validation method which is expected to increase the level of accuracy of the
results of this study. This study was conducted aiming to find out whether the C5.0 algorithm
can be implemented on the 5S audit result data set and has high accuracy or not. With the data
collection method, analysis was carried out using RStudio software and the R programming
language, this study shows that determining the good and bad 5S in an area can be done with
the C5.0 algorithm with a tree-based model or a rule-based model and produces high
accuracy.
Keyword: Artificial Intelegence; C5.0 Algorithm, Classification, Data Mining, Short, Set In
Order, Shine, Standardize And Sustain
ABSTRAK
Kecerdasan Buatan saat ini semakin berkembang dan banyak digunakan dalam berbagai aspek
kehidupan di masyarakat. Begitu juga di lingkungan perusahaan saat ini, kita harus pandai
mengelola semua aktivitas agar AI dapat membantu dalam meringankan dan mengefektifkan
pengambilan keputusan di tempat kerja. Dalam hal meringankan pekerjaan ini adalah pada aspek
pengelolaan data dan analisis data. AI menyediakan banyak metode dan cara untuk menganalisis
data sehingga data tersebut dapat digunakan sebagai referensi penilaian diri karyawan atau
bahkan sebagai penentu bisnis perusahaan ke depan. Penelitian ini membahas algoritma C5.0
yang diimplementasikan atau diuji terhadap set data audit 5S (Short, Set in Order, Shine,
Standardize and Sustain) yang diperoleh dari perusahaan P.T. Bekasi Indonesia. Penelitian ini
menggunakan dua jenis metode dari model algoritma C5.0 sebagai acuan yaitu model tree-based
dan model rule-based, selain itu penelitian ini menggunakan metode cross fold validation yang
408
JURNAL DARMA AGUNG, Vol. 30, No. 3, (2022) Desember : 406 - 413
diharapkan dapat meningkatkan tingkat akurasi. dari hasil penelitian ini. Penelitian ini dilakukan
dengan tujuan untuk mengetahui apakah algoritma C5.0 dapat diimplementasikan pada dataset
hasil audit 5S dan memiliki akurasi yang tinggi atau tidak. Dengan metode pengumpulan data,
analisis dilakukan dengan menggunakan software RStudio dan bahasa pemrograman R,
penelitian ini menunjukkan bahwa menentukan baik buruknya 5S pada suatu area dapat
dilakukan dengan algoritma C5.0 dengan model atau rule berbasis pohon. model berbasis dan
menghasilkan akurasi yang tinggi.
Kata Kunci: Kecerdasan Buatan, Algoritma C5.0, Klasifikasi, Penambangan Data, Pendek,
Diatur Dalam Rangka, Bersinar, Standarisasi Dan Mempertahankan
1. INTRODUCTION
Technological developments continue to
increase along with the times, one of which
is Artificial Intelligence which is able to
assist in facilitating human work.
Artificial intelligence or AI is a
technology developed to be able to learn,
think and work like humans based on data
(Samek et al, 2017). This means that AI can
learn from existing data for further data
training as a learning process. Data Mining
are some examples of AI that can help
human work.
Data mining can be defined as the
process of searching for unknown or
unexpected data patterns. Data mining is one
of the stages in the whole process of
knowledge discovery in databases. In
general, there are several data mining
techniques, one of the data mining
techniques is classification. One of the
classification techniques is a decision tree.
These artificial intelligence techniques
can be utilized and implemented by various
parties, both individuals and companies.
Many companies have used this technique to
later be combined with other disciplines. In
addition, in large companies, technology
must also develop so that the company's
image becomes better because it can keep up
with the times. On the other hand, the use of
technology such as AI can help reduce the
burden on workers and of course can reduce
the company's cost because it can be cheaper
to use AI than having to pay employees.
Things that are commonly used in the
implementation of data mining are to
analyze CRM, customer segmentation and to
be used to detect fraud or fraud detection.
Along with the development of
technology, more and more research is being
carried out both for the benefit of
individuals and companies. There are a lot
of research currently available, call it
research on data mining, one of the things
that can be implemented in a daily activity
or formal activity in the company, for
example research to assess employee
409
APPLICATION OF THE C5.0 ALGORITHM TO DETERMINE GOOD OR BAD ON 5S AUDIT RESULTS
Indra Aliyudin 1), Ari Purno Wahyu 2)
performance, research to assist consumers in
choosing clothes in a shops and many other
examples. The two examples utilize data
mining with the decision tree method and
use the C4.5 algorithm as well as the C5.0
algorithm in their research. After comparing
between the two studies, it turns out that
there are shortcomings in the C4.5 algorithm
(Fajri, M. et al, 2022). Although the C5.0
algorithm is better than the C4.5 algorithm,
the accuracy of the research results can still
be improved with several additional
methods, one of which is adding the cross
fold validation method which can increase
the level of accuracy for the better
(Setyaning et al, 2020).
In this case the author is interested in
conducting research related to data mining
in assessing or determining an area in the
company environment whether it is good or
bad in the application of 5S (Sort, Set in
Order, Shine, Standardized and Sustain)
using the decision tree method with deeper
development than the previous method,
namely using a decision tree with a tree-
based and rule-based method with 10 cross
validation which is expected to use this
deeper method to produce better accuracy.
2. LITERATURE REVIEW
According to Bhatia (2019), data mining
is a collection of techniques for efficient
automated discovery of previously
unknown, valid, novel, useful and
understandable patterns in large databases.
The patterns must be actionable so they may
be used in an enterprise's decision making.
Likewise, according to Witten (2016) who
says that data mining is the process of
analyzing data from different sources and
summing it up into information or
knowledge or patterns that are important to
increase profits, reduce costs, or even both.
In this paper, we will implement one of
the functions of data mining, namely
classification, as Bhatia (2019) said,
classification is a classical method which is
used by machine learning researchers and
statisticians for predicting the outcome of
unknown samples. It is used for
categorization of objects (or things) into
given discrete number of classes.
Classification problems can be of two types,
either binary or multiclass. In binary
classification the target attribute can only
have two possible values. Which is in line
with this research which aims to determine
between two things from various existing
data.
From other research that has been done
by Kastawan (2018), the purpose of this
study is to get the results of employee
performance appraisals from several existing
410
JURNAL DARMA AGUNG, Vol. 30, No. 3, (2022) Desember : 406 - 413
attributes. He explained the performance of
the C5.0 algorithm can produce a fairly high
accuracy. This supports this research so that
it can be done.
Another research that has been done by
Setyaning (2020) which aims to analyze the
factors that affect the timely graduation of
informatics engineering students, she
conducted research with the C5.0 algorithm
with the addition of the k-fold cross
validation method. In her research resulted
in a fairly good accuracy.
In another study that discussed the
comparison of several classification methods
that had been carried out by Hadiwandra
(2019) the decision tree method was
mentioned to be the most robust method
among other methods, besides that it was
also stated that all methods had good
scalability and were able to increase
accuracy when given a large number of
records. bigger. Therefore, in this study, the
decision tree and rule-based classification
methods will be used because they are more
suitable for the data to be processed in this
study.
3. METHODS
Some of the methods used in this
research are as follows:
1. Problem Analysis
In determining whether an area is
good or bad, a method that can work
quickly and precisely is needed. This
research was conducted to be able to
produce a method that can help to
make decisions about an area
whether it is said to be good or bad.
2. Data Collection
In this study, data collection was
carried out by taking the results of
the 5R audit conducted at PT Bekaert
Indonesia, especially the
Maintenance Department.
3. Data Analysis
To determine whether an area is
good or bad, in this study an analysis
will be carried out using the C5.0
Algorithm. The following is a
description of the data that will be
used:
Table 1. Predictor Attribute
Description
Number
Predictor
Attributes
Remarks
1
Auditor
Name of the person
conducting the
audit
411
APPLICATION OF THE C5.0 ALGORITHM TO DETERMINE GOOD OR BAD ON 5S AUDIT RESULTS
Indra Aliyudin 1), Ari Purno Wahyu 2)
2
TotalR1
Total point value of
“Sort”
3
TotalR2
Total point value of
“Set in Order”
4
TotalR3
Total point value of
“Shine”
5
TotalR4
Total point value of
“Standardized”
6
TotalR5
Total point value of
“Sustain”
7
Comment
Comments from the
auditor on the
audited area
3.1. C5.0 Algorithm
The C5.0 algorithm is a decision
tree-based algorithm which is a
refinement of the ID3 and C4.5
algorithms formed by Ross Quinlan
in 1987. The C5.0 algorithm can
handle continuous and discrete
attributes. The selection of
attributes in this algorithm will be
processed using information gain.
The attribute with the highest Gain
value will be selected as the root
for the next node.
3.2. Confusion Matriks
Confusion matrix is a method for
evaluation that uses a matrix table.
The results of the evaluation using
the confusion matrix produce
accuracy values, as well as the error
rate. Accuracy states the amount of
data that is classified correctly after
the testing process is carried out,
while the error rate is used to
calculate identification errors. To
calculate the accuracy is as follows:
Where TP is true positive, namely
the amount of positive data that is correctly
classified by the system, TN is true negative,
namely the number of negative data that is
correctly classified by the system, FN false
negative is the amount of negative data but
is classified incorrectly by the system and
FP is false positive, i.e. the number of
positive data but classified incorrectly by the
system. The error rate can be calculated as
follows:
3.3. Cross Fold Validation
412
JURNAL DARMA AGUNG, Vol. 30, No. 3, (2022) Desember : 406 - 413
Cross validation or sometimes also
called rotation-estimation or out-of-
sample testing is one of a variety of
similar model validation techniques
to assess how statistical analysis
results will generalize to
independent data sets. Cross
validation is a re-sampling method
that uses different pieces of data to
test and train the model at different
iterations.
4. RESULTS AND DISCUSSION
The implementation of this research is
carried out using data mining software,
namely RStudio version 2021.09.2 Build
382. The dataset with the required attributes
and classes has been collected in the form of
files with comma delimited or .csv formats,
for programming using the R language
version 4.1.2.
In this study the author will test using
two methods of the C5.0 algorithm
classification, namely tree-based and rule-
based. But before that below are the core
steps of this test.
1. Step 1 : Process running required
library.
2. Step 2 : Process of cleaning, reading
data set and changing data type.
3. Step 3 : Process of randomizing the
dataset, creating a model
(determining predictors and
attributes) and performing cross fold
validation (including process of
separated data to training-data and
testing data).
4. Step 4 : Process all data in
tree-based and rule-based using C5.0
algorithm.
The first test is the application of the tree-
based c5.0 algorithm. The process has been
mentioned above, and below are the details
and the results.
1. Tree-based C5.0 algorithm training-
data processing and results.
Figure 1. Results of tree-based C5.0
algorithm training-data.
From the results of the training data
obtained the level of accuracy calculated by
the confusion matrix is 98.38%.
413
APPLICATION OF THE C5.0 ALGORITHM TO DETERMINE GOOD OR BAD ON 5S AUDIT RESULTS
Indra Aliyudin 1), Ari Purno Wahyu 2)
2. Tree-based C5.0 algorithm testing-
data processing and results.
Figure 2. Results of tree-based C5.0
algorithm testing-data.
The results of processing the testing-data
on the training-data above after being
calculated using the confusion matrix
method, it can be concluded that the
accuracy of the testing-data is 97.96%.
Next is testing the application of the rule-
based C5.0 algorithm, the steps are the same
as the steps above, only the method is
different.
1. Rule-based C5.0 algorithm training-
data processing and results.
Figure 3. Results of rule-based C5.0
algorithm training-data.
From the results of the training-data
obtained the level of accuracy calculated by
the confusion matrix is 98.10%.
2. Rule-based C5.0 algorithm testing-
data processing and results.
Figure 4. Results of rule-based C5.0
algorithm testing-data.
The results of the processing of the
testing-data on the training data above after
being calculated by the confussion matrix
can be concluded that the accuracy of the
testing-data is 98.21%.
5. CONCLUSION
From the results of the study, it can be
concluded that in analyzing the results of the
5S audit, the C5.0 algorithm can be used and
the resulting accuracy is 97.96% for tree-
based testing data, while the rules-based
algorithm has an accuracy of 98.21%. When
compared between the two models of the
C5.0 algorithm, the one with greater
accuracy is the rules-based model. Thus, it is
more advisable to use the C5.0 algorithm
with a rules-based model. After this research
is conducted, the authors hope that in the
future this research can assist in the design
414
JURNAL DARMA AGUNG, Vol. 30, No. 3, (2022) Desember : 406 - 413
and developement of applications that can
be used for 5S audits that can provide direct
results with high accuracy.
6. REFERENCES
Samek, W., Wiegand, T., & Müller, K. R.
(2017). Explainable artificial
intelligence: Understanding,
visualizing and interpreting deep
learning models. arXiv preprint
arXiv:1708.08296.
Bhatia, P. (2019). Introduction to Data
Mining. In Data Mining and Data
Warehousing: Principles and
Practical Techniques (pp. 17-27).
Cambridge: Cambridge University
Press.
doi:10.1017/9781108635592.
003
Frank, E., Pal, C. J., Witten, I. H., Hall, M.
A. (2016). Data Mining: Practical
Machine Learning Tools and
Techniques. Netherlands: Elsevier
Science.
Kastawan, P., Wiharta, D., & Sudarma, M.
(2018). Implementasi Algoritma
C5.0 pada Penilaian Kinerja
Pegawai Negeri Sipil. Majalah
Ilmiah Teknologi Elektro, 17(3),
371-376.
doi:10.24843/MITE.2018.v1
7i03.P11
Setyaning Nastiti, V. R., Azhar, Y., &
Pramudita, A. E. (2020). Penerapan
Algoritma C5.0 Pada Analisis
Faktor-Faktor Pengaruh Kelulusan
Tepat Waktu Mahasiswa Teknik
Informatika Universitas
Muhammadiyah Malang. Jurnal
Repositor, 1(2), 131-140.
https://doi.org/10.22219/repo
sitor.v1i2.545
T. Yudi Hadiwandra. (2019). Perbandingan
Kinerja Model Klasifikasi Decission
Tree , Bayesian Classifier,
Instance Base, Linear Function
Base, Rule Base pada 4 Dataset
Berbeda. Sains Dan
Teknologi Informasi, 5(1), 70–78.
https://doi.org/10.33372/stn.v5i1.452
Fajri, M., Utami, I. T. ., & Maruf, M. .
(2022). Comparison of C4.5 and
C5.0 Algorithm Classification
Tree Models for Analysis of Factors
Affecting Auction: Perbandingan
Model Pohon Klasifikasi Algoritma
C4.5 dan C5.0 untuk Analisis Faktor
yang Mempengaruhi Keberhasilan
Lelang. Indonesian Journal of
Statistics and Its Applications, 6(1),
13–22.
https://doi.org/10.29244/ijsa.
v6i1p13-22