scholarly journals PREDIKSI CALON MAHASISWA BARU MENGUNAKAN METODE KLASIFIKASI DECISION TREE

2015 ◽  
Vol 7 (1) ◽  
pp. 48
Author(s):  
Mambang Mambang ◽  
Finki Dona Marleny
Keyword(s):  
Decision Tree ◽  
Information Gain ◽  
Internal Node ◽  
Terminal Node ◽  
Gain Ratio ◽  
Root Node ◽  
Information Gain Ratio

<p>Sebelum penyelengaraan pendidikan tenaga kesehatan memulai tahun ajaran baru, maka langkah awal akan dilaksanakan seleksi penerimaan mahasiswa baru yang berasal dari lulusan pendidikan menengah umum maupun kejuruan yang sederajat. Seleksi penerimaan mahasiswa baru ini bertujuan untuk menyaring calon mahasiswa dari berbagai latar belakang yang di sesuaikan dengan standar yang telah di tentukan oleh lembaga. Dalam penelitian ini bagaimana akurasi algoritma C4.5 untuk memprediksi kelulusan calon mahasiswa baru. Model decision tree merupakan metode prediksi klasifikasi untuk membuat sebuah tree yang terdiri dari root node, internal node dan terminal node. Berdasarkan hasil eksperimen dan evaluasi yang dilakukan maka dapat disimpulkan bahwa Algoritma C4.5 dengan Uncertainty didapatkan Akurasi 80,39%, Precision 94,44%, Recall 75,00% sedangkan dengan Algoritma C4.5 dengan Information Gain Ratio Akurasi 88,24%, Precision 98,28%, Recall 83,82%. </p>

An Improved SPRINT Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1685-1690 ◽  
Author(s):  
Zhi Kang Luo ◽  
Huai Ying Sun ◽  
De Wang
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Tree ◽  
Learning Communities ◽  
Information Gain ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Gain Ratio ◽  
Information Gain Ratio ◽  
Improved Algorithm

This paper presents an improved SPRINT algorithm. The original SPRINT algorithm is a scalable and parallelizable decision tree algorithm, which is a popular algorithm in data mining and machine learning communities. To improve the algorithm's efficiency, we propose an improved algorithm. Firstly, we select the splitting attributes and obtain the best splitting attribute from them by computing the information gain ratio of each attribute. After that, we calculate the best splitting point of the best splitting attribute. Since it avoids a lot of calculations of other attributes, the improved algorithm can effectively reduce the computation.

Application Research on Traffic Modal Choice Based on Decision Tree Algorithm

2011 ◽  
Vol 97-98 ◽  
pp. 843-848
Author(s):  
Zheng Hong Peng ◽  
Xin Luan
Keyword(s):  
Decision Tree ◽  
Information Gain ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Tree Model ◽  
Modal Choice ◽  
Sample Data ◽  
Urbanization In China ◽  
Decision Tree Method ◽  
Information Gain Ratio

With the rapid development of urbanization in china, the contradiction between transport, environment and population growth is becoming more and more pronounced, which offers higher demands for transport planning. This article mainly describes the application of decision tree learning algorithm in traffic modal choice. First preprocess the sample data, then calculate and analyze the information gain ratio, and finally we will build a decision tree model. The results show that the rules obtained by decision tree method have some practical value in the analysis of traffic modal choice.

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