Fuzzy Decision Tree to Predict Student Success in Their Studies

The number of students graduating on time is one of the important aspects in the assessment of accreditation of a university. But the problem is still a lot of students who exceed the target time of graduation. Therefore, the prediction of graduation on time can serve as an early warning for the university management to prepare strategies related to the prevention of cases of drop out. The purpose of this research is to build a model using fuzzy decision tree to form the classification rules are used to predict the success of a student's study using fuzzy inference system. Results of this study was generated model of the number of classification rules are 28 rules when the value θr is 98% and θn is 3%, with the level of accuracy is 95.85%. Accuracy of Fuzzy ID3 algorithm is higher than ID3 algorithms in predicting the timely graduation of students.

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SISTEM PAKAR DIAGNOSA PENYAKIT JANTUNG KORONER DENGAN METODE PROBABILISTIC FUZZY DECISION TREE

Jurnal Sains dan Informatika ◽

10.22216/jsi.v4i2.3258 ◽

2018 ◽

Vol 4 (2) ◽

pp. 106

Author(s):

Wizra Aulia

Keyword(s):

Blood Pressure ◽

Coronary Heart Disease ◽

Heart Disease ◽

Chest Pain ◽

Expert System ◽

Decision Tree ◽

Fuzzy Decision ◽

Fuzzy Decision Tree ◽

Id3 Algorithm ◽

Anticipatory Action

Di Indonesia, penyakit jantung koroner menempati posisi pertama sebagai penyakit yang paling banyak mengakibatkan kematian. Jika gejala penyakit jantung koroner dikenali sejak dini maka dapat dilakukan tindakan antisipasi. Diagnosa dilakukan berdasarkan 6 gejala penyakit jantung koroner yaitu sakit dada, tekanan darah tinggi, kolesterol, kadar gula darah, hasil EKG dan jumlah denjut jantung. Metode yang dipakai adalah Probabilistic Fuzzy Decision Tree (PFDT) dengan algoritma Probabilistic Fuzzy ID3. Hasil keakuratan sistem pakar diagnosa penyakit jantung koroner dengan metode PFDT mencapai 95%.In Indonesia, coronary heart disease the first position as the disease that most resulted in death. If symptoms of coronary heart disease are recognized early on, anticipatory action may be taken. Diagnosis is based on 6 symptoms of coronary heart disease chest pain, high blood pressure, cholesterol, blood sugar, ECG results and heartbeat. The method used is Probabilistic Fuzzy Decision Tree (PFDT) with Probabilistic Fuzzy ID3 algorithm. The result of accuracy of expert system of diagnosis of coronary heart disease by PFDT method reached 95%.

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Intelligent Techniques for Prediction of Engineering Colleges After XII

International Journal of Swarm Intelligence Research ◽

10.4018/ijsir.2020010102 ◽

2020 ◽

Vol 11 (1) ◽

pp. 24-43

Author(s):

Mukta Goyal ◽

Rajalakshmi Krishnamurthi ◽

Gokul Gupta ◽

Abhishek Sharma

Keyword(s):

Decision Tree ◽

Fuzzy Inference ◽

Fuzzy Decision ◽

Fuzzy Decision Tree ◽

Multiple Factors ◽

Intelligent Technique ◽

Extra Curricular Activities

Today, students are very confused while selecting colleges based on their ranking after XII standard exam. If students are willing to go for engineering, then they are interested to know the name of colleges on the basis of their merit. The particular college depends on several factors. More and more colleges are interested in mapping students' other features such as extra-curricular activities and financial background, so that they can provide better platforms to sharpen their skills. Thus, this paper proposes an intelligent technique to provide students a platform that will help them to match the colleges based on their academics and extra-curricular qualifications. A fuzzy inference and weighted fuzzy decision tree are used to calculate the score of each student based on the multiple factors of the student where results are shown to be promising.

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Application of Fuzzy Decision Tree Algorithm Based on Mobile Computing in Sports Fitness Member Management

Wireless Communications and Mobile Computing ◽

10.1155/2021/4632722 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Zhu Gu ◽

Chaohu He

Keyword(s):

Decision Tree ◽

Mobile Computing ◽

Classification Accuracy ◽

Decision Tree Algorithm ◽

Fuzzy Decision ◽

Fuzzy Decision Tree ◽

Data Set ◽

Tree Algorithm ◽

Id3 Algorithm ◽

Fitness Industry

After the reform and the opening, the economy of our country has developed rapidly, and the living conditions of the people have become better and better. As a result, they have a lot of time to pay attention to their health, which has promoted the rapid development of the sports and fitness industry in my country. In response to the increasing development of the sports and fitness sector of my country, the current state of the administration of members of the sports fitness industry does not keep pace with the development of the sports and fitness industry of my country. Based on this, this article uses a fuzzy decision tree algorithm to establish a decision tree based on the characteristics of customer data and loses existing customers. Analyzing the situation is of strategic significance for improving the competitiveness of the club. This article selects the 7 most commonly used data sets from the UCI data set as the initial experimental data for model training in three different formats and then uses the data of a specific club member to conduct experiments, using these data files as training samples to construct a vague analysis of the decision tree to overturn the customer to analyze the main factors of customer change. Experiments show that the fuzzy decision tree ID3 algorithm based on mobile computing has the highest accuracy in the Iris data set, reaching 97.8%, and the accuracy rate in the Wine data set is the smallest, only 65.2%. The mobile computing-based fuzzy decision tree ID3 algorithm proposed in this paper obtained the highest correct rate (86.32%). This shows that, compared to traditional analysis methods, the blurred decision tree obtained for churn client analysis has the advantages of high classification accuracy and is understandable so that ideal classification accuracy can be achieved when the tree is small.

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