scholarly journals Komparasi Model Prediksi Daftar Ulang Calon Mahasiswa Baru Menggunakan Metode Decision Tree Dan Adaboost

2021 ◽  
Vol 10 (1) ◽  
pp. 18-24
Author(s):  
Muhammad Naufal Rabbani ◽  
Ahmad Yusuf ◽  
Dwi Rolliawati
Keyword(s):  
Decision Tree ◽  
Selection Process ◽  
Performance Comparison ◽  
Tree Model ◽  
Chi Square ◽  
Islamic School ◽  
New School ◽  
Adaboost Algorithm ◽  
New Students ◽  
Enrollment Process

Every year, all the colleges hold new student enrollment. It is needed to start a new school academic year. Unfortunately, the number of students who resigned is considerably high to reach 837 students and caused 324 empty seats. The college’s stakeholders can minimize the resignation number if the selection phase of new students is done accurately.  Making a  machine learning-based model can be the answer. The model will help predict which candidates who potentially complete the enrollment process. By knowing it in the first place will help the management in the selection process. This prediction is based on historical data. Data is processed and used to train the model using the Adaboost algorithm. The performance comparison between Adaboost and Decision Tree model is performed to find the best model. To achieve the maximum performance of the model, feature selection is performed using chi-square calculation. The results of this research show that the performance of Decision Tree is lower than the performance of the Adaboost algorithm. The Adaboost model has f-measure score of 90.9%, precision 83.7%, and recall 99.5%. The process of analyzing the data distribution of prospective new students was also conducted. The results were obtained if prospective students who tended to finish the enrollment process had the following characteristics:  graduated from an Islamic school, 19-21 years old, parents' income was IDR 1,000,000 to IDR. 5,000,000, and through the SBMPTN program.

Students’ Management to Increase the Character and Achievement in MAN 2 Banjarnegara

2018 ◽  
Vol 2 ◽  
Author(s):  
Sidik Wibowo Akhmad
Keyword(s):  
Academic Achievement ◽  
Extracurricular Activities ◽  
Selection Process ◽  
Skill Test ◽  
Spare Time ◽  
New Students ◽  
Strong Motivation ◽  
Character Building ◽  
The Impact ◽  
Enrollment Process

The purpose of this study was to describe the students’ management in increasing the character and achievement in MAN 2 Banjarnegara including: (1) the enrollment process of new students, (2) guiding students through discipline, noble character building, academic and non-academic achievement, and (3) the impact of character building and the achievement for students MAN 2 Banjarnegara. This research implemented descriptive qualitative approach. The data collection techniques were in-depth interview, observation, and documentation study. The validity of the data used three criteria; namely credibility, dependability, and conformability. The findings of this study were: The first, the enrollment process of the new students was made a breakthrough during the registration of academic and non-academic achievement of scholarships, the selection process was conducted through the value of official learning reports, certificate of championship/achievement, academic potential test and non-academic, and also the skill test. For the students who passed the selection process were supposed to sign the achievement contract during the learning process at MAN 2 Banjarnegara. The second, the character building was done by the concept of habituation and activities program that were integrated in curricular and extracurricular activities. The third, students who joined the academic and non-academic achievement programs at MAN 2 Banjarnegara had strong motivation, spirit of competition to achieve higher achievement and more focus on self-development and they could anticipate the usage of spare time for positive things/activities.

scholarly journals Seleksi Peserta Didik Baru Menggunakan Metode AHP Dan SAW

2020 ◽  
Vol 4 (1) ◽  
pp. 18
Author(s):  
Yuniarti Lestari ◽  
Sunardi S ◽  
Abdul Fadlil
Keyword(s):  
Selection Process ◽  
New School ◽  
School Year ◽  
Participant Selection ◽  
Selection Decisions ◽  
Starting Point ◽  
New Students ◽  
Alternative Ranking ◽  
The Right ◽  
School Selection

The admission activity of new students is an administrative process that is sure to occur every new school year and always repeats every year as a starting point for the search for quality resources in accordance with the criteria of each school. Selection is done manually such as using a spreadsheet or number processor still raises several problems including the length of the selection process. At the time of the participant selection process, it involved many criteria that were assessed (multi criteria). Efforts to assist the school in selecting participants from the results considered to be acceptable results require a decision support system for selecting new students. The method used to support students' selection decisions is AHP and SAW. AHP method is used to determine the weight of predetermined criteria, while the SAW method is used for alternative ranking. The purpose of alternative ranking is who has the right to be accepted as a new student based on predetermined criteria.

Supplier Evaluation Model in Freight Activity Microsimulation Estimator

2018 ◽  
Vol 2672 (9) ◽  
pp. 70-80
Author(s):  
Seyed Mehdi Mahmoudifard ◽  
Ramin Shabanpour ◽  
Nima Golshani ◽  
Kiana Mohammadian ◽  
Abolfazl Mohammadian
Keyword(s):  
Decision Tree ◽  
Supplier Selection ◽  
Selection Process ◽  
Evaluation Model ◽  
Evaluation Process ◽  
Probit Model ◽  
Decision Tree Model ◽  
Supplier Evaluation ◽  
Tree Model ◽  
Main Components

The supplier selection process is one of the main components of the Freight Activity Microsimulation Estimator (FAME), which is a disaggregated and comprehensive framework that simulates the freight movements for all industries and all commodities in the U.S. However, the supplier selection and supplier evaluation models in the FAME face computational issues. Using the result of a nationwide establishment survey, this study analyzes the supplier selection problem by evaluating the potential suppliers. The buyer’s behavior on selecting the distance range in which the trade forms is analyzed using both machine-learning and statistical approaches. A decision-tree model and an ordered probit model are estimated and compared to better comprehend the supplier evaluation process. The results indicate that several factors such as the type of the business, commodity type, number of orders, and the value of orders are significant factors. In addition, the decision-tree model is reliable in forecasting the consumer’s behavior.

scholarly journals Research on hybrid intrusion detection method based on the ADASYN and ID3 algorithms

2021 ◽  
Vol 19 (2) ◽  
pp. 2030-2042
Author(s):  
Yue Li ◽  
◽  
Wusheng Xu ◽  
Wei Li ◽  
Ang Li ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Decision Tree ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Method ◽  
Detection System ◽  
Performance Comparison ◽  
Tree Model ◽  
Network Intrusion ◽  
Id3 Algorithm

<abstract> <p>Intrusion detection system plays an important role in network security. Early detection of the potential attacks can prevent the further network intrusion from adversaries. To improve the effectiveness of the intrusion detection rate, this paper proposes a hybrid intrusion detection method that utilizes ADASYN (Adaptive Synthetic) and the decision tree based on ID3 algorithm. At first, the intrusion detection dataset is transformed by coding technology and normalized. Subsequently, the ADASYN algorithm is applied to implement oversampling on the training set, and the ID3 algorithm is employed to build a decision tree model. In addition, the model proposed by the research is evaluated by accuracy, precision, recall, and false alarm rate. Besides, a performance comparison is conducted with other models. Consequently, it is found that the combined model based on ADASYN and ID3 decision tree proposed in this research possesses higher accuracy as well as lower false alarm rate, which is more suitable for intrusion detection tasks.</p> </abstract>

scholarly journals Penerapan Algoritma C4.5 untuk Seleksi Penerimaan Siswa Baru pada SD Islam Terpadu Permata Bunda Demak Implementation of Decision Tree Algorithm for Selection of New Student Admission on Permata Bunda Integrated Islamic Elementary School

CCIT Journal ◽  
2018 ◽  
Vol 11 (2) ◽  
pp. 158-170
Author(s):  
Linda Monizah Fitriani ◽  
Andik Setyono
Keyword(s):  
Decision Making ◽  
Decision Tree ◽  
Selection Process ◽  
Application Form ◽  
Assessment Tests ◽  
Data Mining Approach ◽  
C4.5 Decision Tree ◽  
New Students ◽  
Screening Assessment ◽  
Support Decision Making

The new admission process of selection is a basic rules for determining studying and learning in schools. This process requires precision so that the results are accurate and precise. The selection process for new students are divided into two types of screening, assessment tests and interviews. The purpose of this study is to assist schools in selecting prospective students so that they can be a decision support for new students. Thus, the need for data mining approach to generate information that can support decision-making for new admissions. The algorithm used is a C4.5 decision tree. C4.5 algorithms can support decision making new admissions through the rules generated. The testing process with RapidMiner yield 90.50% accuracy. Based on these tests, the researchers reprocess into the application form to help the school. So, do the questionnaire to the school to investigate the role of applications in the form of 10 questions by 20 teachers and an index of 81.5%. Thus, schools are satisfied with the application and can help the selection process by the school

A novel enhanced decision tree model for detecting chronic kidney disease

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Avijit Kumar Chaudhuri ◽  
Deepankar Sinha ◽  
Dilip K. Banerjee ◽  
Anirban Das
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Decision Tree ◽  
Decision Tree Model ◽  
Tree Model

scholarly journals Decision Tree-Based Adaptive Reconfigurable Cache Scheme

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 176
Author(s):  
Wei Zhu ◽  
Xiaoyang Zeng
Keyword(s):  
Decision Tree ◽  
Adaptive Algorithms ◽  
Memory Access ◽  
Access Time ◽  
Decision Tree Algorithm ◽  
Verilog Hdl ◽  
Tree Model ◽  
Cache Associativity ◽  
Cache Scheme ◽  
Reconfigurable Cache

Applications have different preferences for caches, sometimes even within the different running phases. Caches with fixed parameters may compromise the performance of a system. To solve this problem, we propose a real-time adaptive reconfigurable cache based on the decision tree algorithm, which can optimize the average memory access time of cache without modifying the cache coherent protocol. By monitoring the application running state, the cache associativity is periodically tuned to the optimal cache associativity, which is determined by the decision tree model. This paper implements the proposed decision tree-based adaptive reconfigurable cache in the GEM5 simulator and designs the key modules using Verilog HDL. The simulation results show that the proposed decision tree-based adaptive reconfigurable cache reduces the average memory access time compared with other adaptive algorithms.

scholarly journals Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm

2021 ◽  
Vol 11 (15) ◽  
pp. 6728
Author(s):  
Muhammad Asfand Hafeez ◽  
Muhammad Rashid ◽  
Hassan Tariq ◽  
Zain Ul Abideen ◽  
Saud S. Alotaibi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tabu Search ◽  
Decision Tree ◽  
Decision Trees ◽  
Search Algorithm ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Tabu Search Algorithm

Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf and decision nodes in the corresponding decision trees. Additionally, the used tabu search algorithm is responsible to balance the entropy of the corresponding decision trees. For training the model, we used the clinical data of COVID-19 patients to predict whether a patient is suffering. The experimental results were obtained using our proposed classifier based on the built-in sci-kit learn library in Python. The extensive analysis for the performance comparison was presented using Big O and statistical analysis for conventional supervised machine learning algorithms. Moreover, the performance comparison to optimized state-of-the-art classifiers is also presented. The achieved accuracy of 98%, the required execution time of 55.6 ms and the area under receiver operating characteristic (AUROC) for proposed method of 0.95 reveals that the proposed classifier algorithm is convenient for large datasets.

Sign in / Sign up

Export Citation Format

Share Document