scholarly journals Application of C4.5 Algorithm in Classification of Personality Types Based on KSPM Personality Theory

2021 ◽  
Vol 8 (3) ◽  
pp. 1077-1089
Author(s):  
Alfian Alfian
Keyword(s):  
Decision Tree ◽  
Personality Types ◽  
Personality Theory ◽  
C4.5 Algorithm

Dalam perkembangan teknologi saat ini, seluruh lini kehidupan tentunya telah terkomputerisasi. Begitu pula pada sebuah sistem penentuan jenis tipe kepribadian yang mampu membantu psikolog pada merampungkan tugasnya dengan mudah dan sempurna. Pada penelitian ini sistem yang akan dibangun memakai antar muka desktop berbasis online. Pada penelitian ini menggunakan algoritma decision tree, dimana metode ini merupakan metode yang cukup cocok dalam hal klasifikasi. Berdasarkan sebuah penelitian yang telah dilakukan oleh Florence Littauer bahwa kepribadian manusia telah diklasifikasikan menjadi empat jenis. Keempatnya termasuk dalam teori protopsikologis, teori ini dibagi menjadi empat jenis kepribadian mendasar yaitu koleris,sanguinis, phlegmatic, melankolis (KSPM). Banyak cara yang bisa dilakukan untuk menentukan kepribadian diri seseorang. Salah satunya ialah dengan menggunakan algoritma decision tree c4.5 untuk melakukan proses klasifikasi kepribadian yang dimiliki oleh seseorang. Algoritma decision tree c4.5 merupakan metode klasifikasi yang banyak digunakan untuk masalah klasifikasi. Algoritma decision tree c4.5 berfungsi untuk mengeksplorasi data dan memodelkan sekelompok data yang belum terklasifikasi.

scholarly journals DECISION TREE C4.5 ALGORITHM FOR TUITION AID GRANT PROGRAM CLASSIFICATION (CASE STUDY: DEPARTMENT OF INFORMATION SYSTEM, UNIVERSITAS TEKNOKRAT INDONESIA)

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Heni Sulistiani ◽  
Ahmad Ari Aldino
Keyword(s):  
Decision Tree ◽  
Classification Model ◽  
Grant Program ◽  
C4.5 Algorithm ◽  
Long Time ◽  
The Right ◽  
Short Time ◽  
Tuition Fee

In pandemic era, almost everyone struggles for their life. College students are such example. They have difficulty in paying tuition fee to continue their study. Based on this problematic situation, Universitas Teknokrat Indonesia grants the students who have good academic performance with tuition fee aid program. Many variables used for determining the grant made it hard to make a decision in a short time or even takes very long time. To make it easier for management to decide who is the right student to get grant, it needs classification model. The purpose of this study is the classification of grant recipients by using decision tree C4.5 algorithm. That can determine whether a potential student can be accepted as an awardee or not. Then, the results of the classification are validated with ten-fold cross validation with an accuracy, precision and recall with the score of 87 % for all part. It means the model perform quite well to be implemented into system.

scholarly journals Penerapan Metode Klasifikasi Decision Tree Pada Status Gizi Balita Di Kabupaten Simalungun

2020 ◽  
Vol 1 (2) ◽  
pp. 68-72
Author(s):  
Hananda Hafizan ◽  
Anggita Nadia Putri
Keyword(s):  
Decision Tree ◽  
Nutritional Status ◽  
Public Health Service ◽  
Children Under Five ◽  
Under Five ◽  
C4.5 Algorithm ◽  
Decision Tree Method ◽  
Nutritional Status Of Children ◽  
Tree Method

One of the health problems in Indonesia is the problem of nutritional status of children under five years. Cases of malnutrition are not only a family problem, but also a state problem. The nutritional status of children under five years can be assessed by measuring the human body known as "Anthropometry". To be able to carry out anthropometric examinations and measurements in order to find out the nutritional status of children under five, they can go to public health service places such as the Posyandu. We went to the KENANGA Posyandu located in Wonorejo, Kerasaan sub-district, Simalungun district. The purpose of this study will be to test the model for the classification of nutritional status of children under the WHO-2005 reference standard by utilizing data mining techniques using the Decision Tree method C4.5 Algorithm.

scholarly journals Discriminating Developing versus Nondeveloping Tropical Disturbances in the Western North Pacific through Decision Tree Analysis

2015 ◽  
Vol 30 (2) ◽  
pp. 446-454 ◽  
Author(s):  
Wei Zhang ◽  
Bing Fu ◽  
Melinda S. Peng ◽  
Tim Li
Keyword(s):  
Decision Tree ◽  
Air Temperature ◽  
North Pacific ◽  
Western North Pacific ◽  
Temperature Anomaly ◽  
Relative Vorticity ◽  
Precipitation Rate ◽  
C4.5 Algorithm ◽  
Air Temperature Anomaly

Abstract This study investigates the classification of developing and nondeveloping tropical disturbances in the western North Pacific (WNP) through the C4.5 algorithm. A decision tree is built based on this algorithm and can be used as a tool to predict future tropical cyclone (TC) genesis events. The results show that the maximum 800-hPa relative vorticity, SST, precipitation rate, divergence averaged between 1000- and 500-hPa levels, and 300-hPa air temperature anomaly are the five most important variables for separating the developing and nondeveloping tropical disturbances. This algorithm also unravels the thresholds of the five variables (i.e., 4.2 × 10−5 s−1 for maximum 800-hPa relative vorticity, 28.2°C for SST, 0.1 mm h−1 for precipitation rate, −0.7 × 10−6 s−1 for vertically averaged convergence, and 0.5°C for 300-hPa air temperature anomaly). Six rules are derived from the decision tree. The classification accuracy of this decision tree is 81.7% for the 2004–10 cases. The hindcast accuracy for the 2011–13 dataset is 84.6%.

scholarly journals Penerapan Algoritma C5.0 Untuk Prediksi Kelulusan Pembelajaran Mahasiswa Pada Matakuliah Arsitektur Sistem Komputer

2021 ◽  
Vol 5 (3) ◽  
pp. 1166
Author(s):  
Muchamad Sobri Sungkar ◽  
M Taufik Qurohman
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Extraction Process ◽  
Study Program ◽  
Data Set ◽  
C4.5 Algorithm ◽  
Previous Algorithm ◽  
Process Prediction ◽  
Computer System Architecture

Computer system architecture is one of the subjects that must be taken in the informatics engineering study program. In the study program the graduation of each student in the course is one of the important aspects that must be evaluated every semester. Graduation for each student / I in the course is an illustration that the learning process delivered is going well and also the material presented by the lecturer in charge of the course can be digested by students. Graduation of each student in the course can be predicted based on the habit pattern of the students. Data mining is an alternative process that can be done to find out habit patterns based on the data that has been collected. Data mining itself is an extraction process on a collection of data that produces valuable information for companies, agencies or organizations that can be used in the decision-making process. Prediction of graduation with data mining can be solved by classifying the data set. The C5.0 algorithm is an improvement algorithm from the C4.5 algorithm where the process is almost the same, only the C5.0 algorithm has advantages over the previous algorithm. The results of the C5.0 algorithm are in the form of a decision tree or a rule that is formed based on the entropy or gain value. The prediction process is carried out based on the classification of the C5.0 algorithm by using the attributes of Attendance Value, Assignment Value, UTS Value and UAS Value. The final result of the C5.0 algorithm classification process is a decision tree with rules in it. The performance of the C5.0 algorithm gets a high accuracy rate of 93.33%

scholarly journals An Extended C4.5 Classification Algorithm using Mathematical Series

2019 ◽  
Vol 7 (2) ◽  
pp. 54-59
Author(s):  
R. Raja Aswathi ◽  
◽  
K. Pazhani Kumar ◽  
B. Ramakrishnan
Keyword(s):  
Decision Tree ◽  
Categorical Data ◽  
Missing Values ◽  
Classification Algorithm ◽  
New Technique ◽  
Rule Based ◽  
C4.5 Algorithm ◽  
A New Technique

The algorithm C4.5 is an efficient decision tree based classification, which is derived from the ID3 approach. C4.5 is also a rule based classification algorithm. The main importance of the C4.5 algorithm is that it can deal with categorical data, over fitting of data and handling of missing values. The performance of C4.5 is superior to ID3 even with equal number of attributes. The EC4.5 (Exponential C4.5) is an extension of C4.5 algorithm which uses exponential of split value to predict the gain of attributes and handled the set back reported in C4.5. However the EC4.5 has some misclassification of data and to avoid this problem a new technique is introduced. This paper proposes a proficient technique TMC4.5 (Taylor-Madhava C4.5) to reduce the uncertainty in classification of data by integrating an exponential split value in EC4.5 and sin splitting value derived from the Madhava series. By using this technique an optimized gain value is obtained that reduces uncertainty. From the obtained result the TMC4.5 has far better results than the C4.5 and EC4.5 algorithms.

Penerapan Metode Klasifikasi Decision Tree dan Algoritma C4.5 dalam Memprediksi Kriteria Nasabah Kredit Mega Auto Finance

2020 ◽  
Vol 7 (2) ◽  
pp. 200
Author(s):  
Puji Santoso ◽  
Rudy Setiawan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Microsoft Excel ◽  
Customer Data ◽  
Data Mining Techniques ◽  
C4.5 Algorithm ◽  
Marketing Costs ◽  
Excel Format ◽  
Data Mining Application

One of the tasks in the field of marketing finance is to analyze customer data to find out which customers have the potential to do credit again. The method used to analyze customer data is by classifying all customers who have completed their credit installments into marketing targets, so this method causes high operational marketing costs. Therefore this research was conducted to help solve the above problems by designing a data mining application that serves to predict the criteria of credit customers with the potential to lend (credit) to Mega Auto Finance. The Mega Auto finance Fund Section located in Kotim Regency is a place chosen by researchers as a case study, assuming the Mega Auto finance Fund Section has experienced the same problems as described above. Data mining techniques that are applied to the application built is a classification while the classification method used is the Decision Tree (decision tree). While the algorithm used as a decision tree forming algorithm is the C4.5 Algorithm. The data processed in this study is the installment data of Mega Auto finance loan customers in July 2018 in Microsoft Excel format. The results of this study are an application that can facilitate the Mega Auto finance Funds Section in obtaining credit marketing targets in the future

scholarly journals Decision Tree C 4.5 Algorithm for Classification of Poor Family Scholarship Recipients

2021 ◽  
Vol 1125 (1) ◽  
pp. 012048
Author(s):  
Y Kustiyahningsih ◽  
B K Khotimah ◽  
D R Anamisa ◽  
M Yusuf ◽  
T Rahayu ◽  
...  
Keyword(s):  
Decision Tree ◽  
Poor Family ◽  
Scholarship Recipients

The application of AI in the aesthetic cultivation of Chinese education

2021 ◽  
pp. 1-5
Author(s):  
Yajuan Wang ◽  
Wen li ◽  
Ruifang Xu
Keyword(s):  
New Media ◽  
Decision Tree ◽  
Chinese Language ◽  
Chinese Students ◽  
Chinese Education ◽  
Coordinated Development ◽  
Language And Literature ◽  
C4.5 Algorithm ◽  
Literature Education ◽  
The Aesthetic

This article will briefly analyse the research background and research significance of the infiltration of aesthetic cultivation in Chinese language and literature education in the context of new media. And through the calculation based on decision tree and C4.5 algorithm, the paper tries to makes the construction of infiltration system of aesthetic cultivation in Chinese language and literature education in the context of new media more scientific and reasonable. This paper also analyses the main connotation and significance of aesthetic cultivation and puts forward the effective infiltration way of aesthetic cultivation in Chinese language and literature in the context of new media, aiming at promoting the comprehensive and coordinated development of Chinese students.

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