scholarly journals ANALISIS REKOMENDASI PEMINATAN MENGGUNAKAN METODE DECISION TREE DENGAN ALGORITMA C4.5

Author(s):  
Tri Sutrisno ◽  
Stefanny Claudia

The application created are used to analyze which thesis preference subject suits students academic performance based on their academic grades. The application also provide online academic consultations features which students can use for their academic consultations. To find their thesis preference, the application use decision tree method with C4.5 algorithm. Testing prediction system using students data from 2012 to 2015 who have found their thesis preference. The value data used is 32 mandatory courses in the Faculty of Information Technology before thesis preference. The application can run , use and perform well in accordance with the design made. Testing is to compare the accuracy of the selected tree model build from training data and the thesis preference students have selected. The average accuracy percentage of this a 72,6227%.

2014 ◽  
Vol 6 (1) ◽  
pp. 9-14
Author(s):  
Stefanie Sirapanji ◽  
Seng Hansun

Beauty is a precious asset for everyone. Everyone wants to have a healthy face. Unfortunately, there are always those problems that pops out on its own. For example, acnes, freckles, wrinkles, dull, oily and dry skin. Therefore, nowadays, there are a lot of beauty clinics available to help those who wants to solve their beauty troubles. But, not everyone can enjoy the facilities of those beauty clinics, for example those in the suburbs. The uneven distribution of doctors and the expensive cost of treatments are some of the reasons. In this research, the system that could help the patients to find the solution of their beauty problems is built. The decision tree method is used to take decision based on the shown schematic. Based on the system’s experiment, the average accuracy level hits 100%. Index Terms–Acnes, Decision Tree, Dry Skin, Dull, Facial Problems, Freckles, Wrinkles, Oily Skin, Eexpert System.


Author(s):  
Hananda Hafizan ◽  
Anggita Nadia Putri

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.


2018 ◽  
Vol 10 (3) ◽  
pp. 106
Author(s):  
Mirza Suljic ◽  
Edin Osmanbegovic ◽  
Željko Dobrović

The subject of this paper is metamodeling and its application in the field of scientific research. The main goal is to explore the possibilities of integration of two methods: questionnaires and decision trees. The questionnaire method was established as one of the methods for data collecting, while the decision tree method represents an alternative way of presenting and analyzing decision making situations. These two methods are not completely independent, but on the contrary, there is a strong natural bond between them. Therefore, the result reveals a common meta-model that over common concepts and with the use of metamodeling connects the methods: questionnaires and decision trees. The obtained results can be used to create a CASE tool or create repository that can be suitable for exchange between different systems. The proposed meta-model is not necessarily the final product. It could be further developed by adding more entities that will keep some other data.


2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
D. Mudali ◽  
L. K. Teune ◽  
R. J. Renken ◽  
K. L. Leenders ◽  
J. B. T. M. Roerdink

Medical imaging techniques like fluorodeoxyglucose positron emission tomography (FDG-PET) have been used to aid in the differential diagnosis of neurodegenerative brain diseases. In this study, the objective is to classify FDG-PET brain scans of subjects with Parkinsonian syndromes (Parkinson’s disease, multiple system atrophy, and progressive supranuclear palsy) compared to healthy controls. The scaled subprofile model/principal component analysis (SSM/PCA) method was applied to FDG-PET brain image data to obtain covariance patterns and corresponding subject scores. The latter were used as features for supervised classification by the C4.5 decision tree method. Leave-one-out cross validation was applied to determine classifier performance. We carried out a comparison with other types of classifiers. The big advantage of decision tree classification is that the results are easy to understand by humans. A visual representation of decision trees strongly supports the interpretation process, which is very important in the context of medical diagnosis. Further improvements are suggested based on enlarging the number of the training data, enhancing the decision tree method by bagging, and adding additional features based on (f)MRI data.


2014 ◽  
Vol 926-930 ◽  
pp. 703-707
Author(s):  
Hu Yong

Aimed at the student the result problem, give student the result data scoops out the model. The decision tree method is a very valid classification method, in the data that scoop out. According to student the result data characteristics, adopted the C4.5 decision tree algorithm. C4.5 algorithm is the improvement algorithm of the decision trees core algorithm ID3, it construct in brief, the speed compare quickly, easy realization. Selection decision belongs to sex, scoop out the result enunciation, that algorithm can be right to get student the result data classification, and some worthy conclusion, provide the decision the analysis.


Author(s):  
Fana Wiza ◽  
Bayu Febriadi

School as one of the processes for implementing formal education is required to carry out the learning process optimally to produce quality students. Regarding the research process carried out to predict the graduation rate of SMA Nurul Falah students by using the decision tree method. The data used in this study are student data using the criteria for student names, majors, average report cards from semester one (I), two (II), three (III), four (IV), five (V), and the average value of the National Standard School Examination (USBN). The data is then managed using Rapidminer 5.3 software to make it easier to predict student graduation rates. The application of data mining is used to predict the graduation rate by using the decision tree method and C4.5 algorithm as a supporter as well as to find out information on the graduation rate of Nurul Falah High School students. This study aims to predict student graduation rates in order to get useful information and the school can make policies in the coming year.


2020 ◽  
Vol 4 (1) ◽  
pp. 64
Author(s):  
Md Zannatul Arif ◽  
Rahate Ahmed ◽  
Umma Habiba Sadia ◽  
Mst Shanta Islam Tultul ◽  
Rocky Chakma

The motive of the investigation is analyzing the categorization of fetal state code from the Cardiographic data set based on decision tree method. Cardiotocography is one of the important tools for monitoring heart rate, and this technique is widely used worldwide. Cardiotocography is applied for diagnosing pregnancy and checking fetal heart rate state condition until before delivery. This classification is necessary to predict fetal heart rate situation which is belonging. In this paper, we are using three input attributes of training data set quoted by LB, AC, and FM to categorize as normal, suspect or pathological where NSPF variable is used as a response variable. After drawing necessary analysis into three variables we get the 19 nodes of classification tree and also we have measured every single node according to statistic, criterion, weights, and values. The Cardiotocography Dataset applied in this study is received from UCI Machine Learning Repository. The dataset contains 2126 observation instances with 22 attributes. In this experiment, the highest accuracy is 98.7%. Overall, the experimental results proved the viability of Classification and Regression Trees and its potential for further predictions.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited. 


2014 ◽  
Vol 5 (2) ◽  
pp. 61-71 ◽  
Author(s):  
Ivan Horvat ◽  
Mirjana Pejić Bach ◽  
Marjana Merkač Skok

AbstractBackground: Fraud attempts create large losses for financing subjects in modern economies. At the same time, leasing agreements have become more and more popular as a means of financing objects such as machinery and vehicles, but are more vulnerable to fraud attempts. Objectives: The goal of the paper is to estimate the usability of the data mining approach in discovering fraud in leasing agreements. Methods/Approach: Real-world data from one Croatian leasing firm was used for creating tow models for fraud detection in leasing. The decision tree method was used for creating a classification model, and the CHAID algorithm was deployed. Results: The decision tree model has indicated that the object of the leasing agreement had the strongest impact on the probability of fraud. Conclusions: In order to enhance the probability of the developed model, it would be necessary to develop software that would enable automated, quick and transparent retrieval of data from the system, processing according to the rules and displaying the results in multiple categories.


2020 ◽  
Vol 4 (1) ◽  
pp. 63-69
Author(s):  
Romadhoni Romadhoni ◽  
Nurhasanah Nurhasanah

To make decisions in the production of fiberglass vessels in traditional shipyards in Bengkalis Regency, it is necessary to carry out economic calculations using the Expected Monetary Value (EMV) method related to the production of fiberglass ships and wooden ships using the decision tree method. This is done to determine the chances of the producers succeeding or not with the development of fiberglass ship products, then some forecasting will be conducted on various possibilities that will occur. These possibilities will be formulated with a decision tree model so that producers will be able to decide whether the fiberglass ship product can be continued or only continue by running the old product, which is producing wooden ships. The results of this modeling can later become a reference for producers to look for further solutions if it turns out that after modeling new product results, the fiberglass ship has a greater chance of failure compared to its chances of success. Based on the results of modeling using the decision tree model, it is expected that the opportunity for producers to succeed with their new products, namely fiberglass ships, will be obtained. Keywords: EMV, Fiberglass, Decision Tree, economic


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