ANALISIS METODE DECISION TREE DALAM MEMPREDIKSI KELULUSAN MAHASISWA

2021 ◽  
Vol 13 (3) ◽  
pp. 131
Author(s):  
Wulandari Wulandari ◽  
Rika Rosnelly ◽  
Wanayumini Wanayumini
Keyword(s):  
Decision Tree ◽  
Input Data

<p>Perguruan tinggi juga menjadi tolak ukur keberhasilan mahasiswa itu sendiri.Bagi mahasiswa yang tidak dapat menyelesaikan studinya, prestasi yang tinggi juga menjadi penyebab kegagalan mahasiswa, salah satunya mahasiswa yang kurang aktif di lingkungan kampus. . Selain itu, yang sering menjadi penyebab adalah nilai rata-rata indeks prestasi kumulatif (IPK) yang rendah, selain itu kegagalan mahasiswa juga dapat disebabkan oleh moralitas dan disiplin mahasiswa yang kurang. Perlu dilakukan penelitian untuk memprediksi kelulusan mahasiswa, dengan menggunakan input data yaitu berupa data mahasiswa yang meliputi nilai tiap semester, peminatan, PKL, Skripsi 1 dan Skripsi 2.</p>

scholarly journals Globally Harmonized Carbon Storage Data

2019 ◽  
Author(s):  
Justin Andrew Johnson
Keyword(s):  
Decision Tree ◽  
Carbon Storage ◽  
Input Data ◽  
Visual Inspection ◽  
Above Ground Biomass ◽  
Tier 1 ◽  
Multiple Input ◽  
Spatial Configurations ◽  
Combination Of Methods ◽  
Tree Approach

AbstractThis paper presents methods and results that combine multiple carbon storage and above-ground biomass datasets using a simple decision tree approach. The resulting dataset combines the positive attributes of the multiple input datasets in order to have global, high-resolution extent while utilizing the best statistical methods where possible. Visual inspection shows very different spatial configurations of carbon storage between the results and the input data, suggesting that combination of methods can improve estimates. The summation of the decision tree result was 336.55 petagrams while the summation of a dataset based on the IPCC Tier 1 method was 502.38 petagrams (49.27% higher than the results from the decision tree).

scholarly journals Providing a Combination Classification (Honeybee Clooney and Decision Tree) Based on Developmental Learning

2015 ◽  
Vol 9 (13) ◽  
pp. 188
Author(s):  
Seyed Ahad Zolfagharifar ◽  
Faramarz Karamizadeh ◽  
Hamid Parvin
Keyword(s):  
Data Collection ◽  
Decision Tree ◽  
Input Data ◽  
Detection Rate ◽  
Ensemble Method ◽  
Matlab Software ◽  
Developmental Learning ◽  
Training Time ◽  
Software Note ◽  
Study Characteristics

<p class="zhengwen">The aim of this study is to provide a combination classification based on developmental learning in the proposed method using algorithms inspired by nature (honeybee Clooney) and decision tree, by using algorithm classifier consensus is proposed that this method, at first classifier once implemented and based on the detection rate of input data agreement in the final consensus which is an innovation in this research. To implement the proposed algorithms used MATLAB software. Note that, this is an increase compared to the classifiers ensemble, it have accuracy and fix. This shows that this method of making the ensemble by helping bee Clooney algorithm, when appropriate and effective which the number of data collection records is high or the number of study characteristics is high. In this study, we proposed algorithm on 8 samples tested. However, training time of this method compared with simple ensemble is a slower process but this method compared with simple ensemble method has higher accuracy, this shows, if we want a higher accuracy, we should be spent more time.</p>In general, if the accuracy of the process have a large importance for us, this method can be a good option to get the results that almost optimal.

Transmission Electron Diffraction Analysis by Computer

1970 ◽  
Vol 28 ◽  
pp. 40-41
Author(s):  
R.A. Ploc ◽  
G.H. Keech
Keyword(s):  
Electron Diffraction ◽  
Input Data ◽  
Reciprocal Lattice ◽  
Computer Programme ◽  
Transmission Electron Diffraction ◽  
Usual Procedure ◽  
Transmission Electron ◽  
Complex Shapes ◽  
Computer Input ◽  
Diffraction Effects

An unambiguous analysis of transmission electron diffraction effects requires two samplings of the reciprocal lattice (RL). However, extracting definitive information from the patterns is difficult even for a general orthorhombic case. The usual procedure has been to deduce the approximate variables controlling the formation of the patterns from qualitative observations. Our present purpose is to illustrate two applications of a computer programme written for the analysis of transmission, selected area diffraction (SAD) patterns; the studies of RL spot shapes and epitaxy.When a specimen contains fine structure the RL spots become complex shapes with extensions in one or more directions. If the number and directions of these extensions can be estimated from an SAD pattern the exact spot shape can be determined by a series of refinements of the computer input data.

Automated Development of Clinical Strategies Using Multistage Decision Analysis

1986 ◽  
Vol 25 (04) ◽  
pp. 207-214 ◽  
Author(s):  
P. Glasziou
Keyword(s):  
Decision Tree ◽  
Decision Analysis ◽  
Decision Trees ◽  
Optimal Strategy ◽  
Cholestatic Jaundice ◽  
Clinical Strategies ◽  
Simultaneous Study

SummaryThe development of investigative strategies by decision analysis has been achieved by explicitly drawing the decision tree, either by hand or on computer. This paper discusses the feasibility of automatically generating and analysing decision trees from a description of the investigations and the treatment problem. The investigation of cholestatic jaundice is used to illustrate the technique.Methods to decrease the number of calculations required are presented. It is shown that this method makes practical the simultaneous study of at least half a dozen investigations. However, some new problems arise due to the possible complexity of the resulting optimal strategy. If protocol errors and delays due to testing are considered, simpler strategies become desirable. Generation and assessment of these simpler strategies are discussed with examples.

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