Identification of Factors Affecting Disk Drive’s Performance in Data Server by Use of Decision Tree Learning Method

2021 ◽  
Author(s):  
Yi-Ju Liao ◽  
Jen-Yuan (James) Chang
Keyword(s):  
Decision Tree ◽  
Information Gain ◽  
Learning Method ◽  
Training Set ◽  
Decision Tree Learning ◽  
Maximum Information ◽  
Factors Affecting ◽  
Id3 Algorithm ◽  
Maximum Information Entropy ◽  
Smart Machine

Abstract To identify factors affecting magnetic disk drive’s data recording performance in data server, decision tree learning method is proposed and validated in this paper. Aiming at improving classification efficiency of various causes of HDD performance degradation, the ID3 algorithm of decision tree was first used showing the training set model would be able to achieve 100% accuracy. The maximum information entropy and information gain theory of ID3 algorithm were then adopted, from which accuracy range of 0.5–0.6 can be further achieved. The proposed method was validated to be effective for leveraging the data sever into Industry 4.0 ready smart machine.

scholarly journals Predicting Students’ Performance In Basic Algorithms Programming In an E-Learning Environment Using Decision Tree Approach

2022 ◽  
Vol 7 (1) ◽  
pp. 498
Author(s):  
Jonas De Deus Guterres ◽  
Kusuma Ayu Laksitowening ◽  
Febryanti Sthevanie
Keyword(s):  
Learning Environment ◽  
Decision Tree ◽  
Student Performance ◽  
Information Gain ◽  
Quality Data ◽  
Basic Algorithm ◽  
Id3 Algorithm ◽  
E Learning ◽  
Advanced Analysis ◽  
Quality In Higher Education

Predicting the performance of students plays an important role in every institution to protect their students from failures and leverage their quality in higher education. Algorithm and Programming is a fundamental course for the students who start their studies in Informatics. Hence, the scope of this research is to identify the critical attributes which influence student performance in the E-learning Environment on Moodle LMS (Learning Management System) Platform and its accuracy. Data mining helps the process of preprocessing data in a dataset from raw data to quality data for advanced analysis. Dataset set is consisting of student academic performance such as grades of Quizzes, Mid exams, Final exams, and Final projects. Moreover, the dataset from LMS is considered as well in the process of modeling, in terms of constructing the decision tree, such as punctuality submission of Quizzes, Assignments, and Final Projects. Regarding the Basic Algorithm and Programming course, which is separated into two subjects in the first and second semester, thus the research will predict the student performance in the Basic Algorithm and programming course in the second semester based on the Introduction to programming course in the first semester. Decision Tree techniques are applied by using information gain in ID3 algorithm to get the important feature which is the PP index has the highest information gain with value 0.44, also the accuracy between ID3 and J48 algorithm that shows ID3 has the highest accuracy of modeling which is 84.80% compared to J48 82.34%.

scholarly journals PENERAPAN METODE ITERATIVE DICHOTOMIZER 3 (ID 3) UNTUK MENENTUKAN BEASISWA BERPRESTASI PADA SMP PGRI CARINGIN SUKABUMI

2019 ◽  
Vol 15 (1) ◽  
pp. 35-40
Author(s):  
Rizal Amegia Saputra ◽  
Lis Saumi Ramdhani ◽  
Supriatman Supriatman
Keyword(s):  
Decision Tree ◽  
Learning Algorithm ◽  
Accuracy Rate ◽  
Decision Tree Learning ◽  
Academic Fields ◽  
Id3 Algorithm ◽  
Scholarship Recipients ◽  
The Government ◽  
To Come ◽  
To Receive

Scholarships are assistance from the government to students / students who are less able or have the ability in the academic and non-academic fields that are given individually to reduce the burden in terms of material. Frequently stalling time in selection, the number of students who apply for scholarships, the number of students whose homes are far from school, the number of students who race to come early as one of the criteria eligible to receive scholarships as well as the most scholarship applicants feel disadvantaged by unfavorable decisions. Iterative Dichotomizer 3 (ID3) algorithm is the most basic decision tree learning algorithm (decision tree learning algorithm). This algorithm conducts a thorough search on all possible decisions. In this research, it will be analyzed the application of the iterative dichotomizer 3 method in the case of determining achievement scholarships. In order to make decisions quickly and accurately. From 708 scholarship candidates including 28 eligible and 680 scholarship recipients, 136 scholarship recipients were obtained from ID3 algorithm with 3 eligible and 133 who had not, and obtained an accuracy rate of 97.75% so that it could be concluded that good and can help the school.

An Improved ID3 Decision Tree Algorithm

2014 ◽  
Vol 962-965 ◽  
pp. 2842-2847 ◽  
Author(s):  
Xiao Juan Chen ◽  
Zhi Gang Zhang ◽  
Yue Tong
Keyword(s):  
Decision Tree ◽  
Information Entropy ◽  
Information Gain ◽  
Classification Algorithm ◽  
Learning Ability ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Decision Tree Classification ◽  
Id3 Algorithm ◽  
Improved Algorithm

As the classical algorithm of the decision tree classification algorithm, ID3 algorithm is famous for the merits of high classifying speed, strong learning ability and easy construction. But when used to make classification, the problem of inclining to choose attributions which have many values affect its practicality. This paper presents an improved algorithm based on the expectation information entropy and Association Function instead of the traditional information gain. In the improved algorithm, it modified the expectation information entropy with the improved Association Function and the number of the attributes values. The experiment result shows that the improved algorithm can get more reasonable and more effective rules.

scholarly journals Network selection algorithm based on decision tree in heterogeneous wireless networks

2018 ◽  
Vol 189 ◽  
pp. 04010 ◽  
Author(s):  
Wang-hong Li ◽  
Zhu Qi
Keyword(s):  
Decision Tree ◽  
Information Gain ◽  
Heterogeneous Wireless Networks ◽  
Training Data ◽  
Network Selection ◽  
User Requirements ◽  
Selection Algorithm ◽  
Training Set ◽  
Service Characteristics

A network selection algorithm based on Decision Tree is proposed to solve the problem. Users can select the appropriate network according to their service characteristics and requirements when they decide which network to access. First, we get the training data under the Interactive Class service from the synergetic algorithm which can be used for training set. The network attributes are used for attribute set. And then we can choose the attribute with the largest information gain as the division attribute after the discretization of continuous features by the bisection method. Keep going this step recursively, we can finally get a decision tree with high generalization ability by which we can make the network selection. Simulation results show that the algorithm we proposed is simple and effective and demonstrate the effectiveness of our scheme in improving the quality of service according to the user requirements under the Interactive Class service.

The Application of Data Mining Technology in the Teaching Evaluation in Colleges and Universities

2017 ◽  
Vol 14 (1) ◽  
pp. 7-12 ◽  
Author(s):  
Xiaoqi Liu
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Evaluation System ◽  
Information Gain ◽  
Original Data ◽  
Teaching Evaluation ◽  
Personal Factors ◽  
Mining Technology ◽  
Id3 Algorithm ◽  
College Work

As the teaching management informationization level is higher and higher, Network based teaching evaluation system has been widely used, and a lot of evaluation of the original data has been accumulated. This research, taking recent five years teaching evaluation data of the college work for as basis, analyzes teachers’ personal factors and teaching operation factors respectively with the data mining technology of decision tree ID3 algorithm. By calculating the factors of information entropy and information gain value, the corresponding decision tree is gained. The teaching evaluation results are made use of really rather than become a mere formality, and thus provide powerful basis for the effectiveness and scientificalness of teaching evaluation.

scholarly journals Analyzing Student Performance in Programming Education Using Classification Techniques

2020 ◽  
Vol 15 (02) ◽  
pp. 127 ◽  
Author(s):  
Kissinger Sunday ◽  
Patrick Ocheja ◽  
Sadiq Hussain ◽  
Solomon Sunday Oyelere ◽  
Balogun Oluwafemi Samson ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Computer Science ◽  
Student Performance ◽  
Test Score ◽  
Information Gain ◽  
Search Method ◽  
Experimental Results ◽  
Id3 Algorithm ◽  
Tree Algorithms

In this research, we aggregated students log data such as Class Test Score (CTS), Assignment Completed (ASC), Class Lab Work (CLW) and Class Attendance (CATT) from the Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, Sokoto, Nigeria. Similarly, we employed data mining techniques such as ID3 & J48 Decision Tree Algorithms to analyze these data. We compared these algorithms on 239 classification instances. The experimental results show that the J48 algorithm has higher accuracy in the classification task compared to the ID3 algorithm. The important feature attributes such as Information Gain and Gain Ratio feature evaluators were also compared. Both the methods applied were able to rank search method and the experimental results confirmed that the two methods derived the same set of attributes with a slight deviation in the ranking. From the results analyzed, we discovered that 67.36 percent failed the course titled Introduction to Computer Programming, while 32.64 percent passed the course. Since the CATT has the highest gain value from our analysis; we concluded that it is largely responsible for the success or failure of the students.

scholarly journals PENDEKATAN METODE POHON KEPUTUSAN MENGGUNAKAN ALGORITMA ITERATIVE DICHOTOMIZER (ID3) UNTUK PENGUKURAN KINERJA PEGAWAI NEGERI SIPIL

SAINTEKBU ◽  
2016 ◽  
Vol 9 (1) ◽  
Author(s):  
Yoseph Pius Kurniawan Kelen ◽  
Yohanis Ndapa Deda
Keyword(s):  
Decision Tree ◽  
Information Gain ◽  
Employee Performance ◽  
Objective Assessment ◽  
Decision Tree Classification ◽  
Id3 Algorithm ◽  
Training Samples ◽  
Tree Methods ◽  
Decision Tree Method ◽  
Tree Method

Decision tree method is a classification method that has been widely used for the solution of problems of classification. Decision tree classification provides a rapid and effective method. The approach has been proven decision tree method can be applied in various fields of life. Capability classification is indicated by the decision tree method is what encourages authors to use decision tree methods approach to measure the performance of civil servants.  To build a decision tree induction algorithms used. In this study, the ID3 algorithm method is used to construct a decision tree. Starting with the data collecting training samples and then measuring the entropy and information gain. Information Gain value will be used as the root of a decision tree. And translates it into a decision tree classification rules.The results show that the decision tree method is used to produce classification rules into groups employee performance Good and Bad. The resulting rules are used to measure the performance of employees and classifying employees into two groups.The result to assist management in making more objective assessment process. Keywords: ID3 Algorithm, Decision Tree, Employee Performance.

PREDIKSI KUALITAS AIR SUNGAI CILIWUNG DENGAN MENGGUNAKAN ALGORITMA POHON KEPUTUSAN

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Mohammad Haekal ◽  
Henki Bayu Seta ◽  
Mayanda Mega Santoni
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Cross Validation ◽  
Online Monitoring ◽  
Training Set ◽  
Microsoft Excel ◽  
Test Set

Untuk memprediksi kualitas air sungai Ciliwung, telah dilakukan pengolahan data-data hasil pemantauan secara Online Monitoring dengan menggunakan Metode Data Mining. Pada metode ini, pertama-tama data-data hasil pemantauan dibuat dalam bentuk tabel Microsoft Excel, kemudian diolah menjadi bentuk Pohon Keputusan yang disebut Algoritma Pohon Keputusan (Decision Tree) mengunakan aplikasi WEKA. Metode Pohon Keputusan dipilih karena lebih sederhana, mudah dipahami dan mempunyai tingkat akurasi yang sangat tinggi. Jumlah data hasil pemantauan kualitas air sungai Ciliwung yang diolah sebanyak 5.476 data. Hasil klarifikasi dengan Pohon Keputusan, dari 5.476 data ini diperoleh jumlah data yang mengindikasikan sungai Ciliwung Tidak Tercemar sebanyak 1.059 data atau sebesar 19,3242%, dan yang mengindikasikan Tercemar sebanyak 4.417 data atau 80,6758%. Selanjutnya data-data hasil pemantauan ini dievaluasi menggunakan 4 Opsi Tes (Test Option) yaitu dengan Use Training Set, Supplied Test Set, Cross-Validation folds 10, dan Percentage Split 66%. Hasil evaluasi dengan 4 opsi tes yang digunakan ini, semuanya menunjukkan tingkat akurasi yang sangat tinggi, yaitu diatas 99%. Dari data-data hasil peneltian ini dapat diprediksi bahwa sungai Ciliwung terindikasi sebagai sungai tercemar bila mereferensi kepada Peraturan Pemerintah Republik Indonesia nomor 82 tahun 2001 dan diketahui pula bahwa penggunaan aplikasi WEKA dengan Algoritma Pohon Keputusan untuk mengolah data-data hasil pemantauan dengan mengambil tiga parameter (pH, DO dan Nitrat) adalah sangat akuran dan tepat. Kata Kunci : Kualitas air sungai, Data Mining, Algoritma Pohon Keputusan, Aplikasi WEKA.

DIFFERENTIAL EVOLUTION IN THE DECISION TREE LEARNING ALGORITHM

2019 ◽  
Vol 20 (3) ◽  
pp. 312-319
Author(s):  
S. A. Mitrofanov ◽  
◽  
E. S. Semenkin ◽  
Keyword(s):  
Decision Tree ◽  
Differential Evolution ◽  
Learning Algorithm ◽  
Decision Tree Learning

Applying design patterns to decision tree learning system

1998 ◽  
Vol 23 (6) ◽  
pp. 111-120 ◽  
Author(s):  
Gou Masuda ◽  
Norihiro Sakamoto ◽  
Kazuo Ushijima
Keyword(s):  
Decision Tree ◽  
Design Patterns ◽  
Learning System ◽  
Decision Tree Learning
Sign in / Sign up

Export Citation Format

Share Document