scholarly journals SISTEM DETEKSI DINI UNTUK MENINGKATKAN PERFORMANCE KELULUSAN MAHASISWA DENGAN ID3

2018 ◽  
Author(s):  
Slamet Kacung ◽  
Budi Santoso
Keyword(s):  
Higher Education ◽  
Early Detection ◽  
Decision Tree ◽  
Student Performance ◽  
Detection System ◽  
Academic Programs ◽  
Decision Tree Classifier ◽  
Study Program ◽  
Tree Classifier

The performance of academic programs higher education is measured by the number of graduates are produced by each study program as reflected in the standard III accreditation form in point 3.1.1 and 3.1.4. The study program is required to have a good performance is marked by the increasing number of graduates in proportion to the number of students received so that ratio lecturers with students can be maintained. The more students who are accepted in college if they are not comparable with the number of graduates in each year will have an impact on the quality of learning. The result of this graduation becomes the evaluation material of the study program which will be the input of the study program and the Academic Advisors (DPAM) in order to provide treatment to the problem students so that they can improve the performance of the graduates. DPAM has a very important role in the progress of the learning process of students Guide, but with the amount of guidance that is increasingly causing students to be misdirected and in the end the student performance becomes bad, for that need an early detection system to improve the performance of graduates based on the results of the recommendation from the decision tree classifier. this method can generate a decision tree and give recommendations to students problems with accuracy.

Data Mining: A Bagged Decision Tree Classifier Algorithm For Ids Intrusion Detection System Based Attacks Classification

2021 ◽  
pp. 1826-1839
Author(s):  
Sandeep Adhikari, Dr. Sunita Chaudhary
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Intrusion Detection System ◽  
Detection System ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Tree Classifier

The exponential growth in the use of computers over networks, as well as the proliferation of applications that operate on different platforms, has drawn attention to network security. This paradigm takes advantage of security flaws in all operating systems that are both technically difficult and costly to fix. As a result, intrusion is used as a key to worldwide a computer resource's credibility, availability, and confidentiality. The Intrusion Detection System (IDS) is critical in detecting network anomalies and attacks. In this paper, the data mining principle is combined with IDS to efficiently and quickly identify important, secret data of interest to the user. The proposed algorithm addresses four issues: data classification, high levels of human interaction, lack of labeled data, and the effectiveness of distributed denial of service attacks. We're also working on a decision tree classifier that has a variety of parameters. The previous algorithm classified IDS up to 90% of the time and was not appropriate for large data sets. Our proposed algorithm was designed to accurately classify large data sets. Aside from that, we quantify a few more decision tree classifier parameters.

scholarly journals Student Performance Predictions Using Knowledge Discovery Database and Data Mining, DPU Students Records as Sample

2021 ◽  
Vol 10 (3) ◽  
pp. 121-127
Author(s):  
Bareen Haval ◽  
Karwan Jameel Abdulrahman ◽  
Araz Rajab
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Student Performance ◽  
Educational Data Mining ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Data Mining Techniques ◽  
Academic History ◽  
Tree Classifier ◽  
Using Data

This article presents the results of connecting an educational data mining techniques to the academic performance of students. Three classification models (Decision Tree, Random Forest and Deep Learning) have been developed to analyze data sets and predict the performance of students. The projected submission of the three classificatory was calculated and matched. The academic history and data of the students from the Office of the Registrar were used to train the models. Our analysis aims to evaluate the results of students using various variables such as the student's grade. Data from (221) students with (9) different attributes were used. The results of this study are very important, provide a better understanding of student success assessments and stress the importance of data mining in education. The main purpose of this study is to show the student successful forecast using data mining techniques to improve academic programs. The results of this research indicate that the Decision Tree classifier overtakes two other classifiers by achieving a total prediction accuracy of 97%.

scholarly journals An Efficient Hemorrhage Detection System using Decision Tree Classifier

2019 ◽  
Vol 8 (3) ◽  
pp. 5728-5732
Keyword(s):  
Decision Tree ◽  
Active Contours ◽  
Detection System ◽  
Input Image ◽  
Morphological Operations ◽  
Decision Tree Classifier ◽  
Average Accuracy ◽  
Tree Classifier ◽  
Processing Techniques ◽  
Magnetic Resonant Imaging

the visual representations of the inner constituents of body along with the functions of either organs or tissues comprising its physiology are developed in medical imaging. These images can be obtained by various techniques such as computed tomography (CT), magnetic resonant imaging (MRI), and x-ray. The objective of the system mentioned in this paper is to detect the presence of hemorrhage and to classify the type of it when detected. CT images are considered here to find the hemorrhage. Pre-processing techniques such as grayscale conversion, image resizing, edge detection and sharpening are done to make the input image suitable for further processing. After preprocessing the images go through morphological operations to help identify the shape related features in correspondence to the hemorrhage. Sobel and markers are used in the processed ct image to highlight the interested region. Then watershed algorithm is employed for the purpose of segmentation. The presence of hemorrhage can be detected as a result of segmentation. Once hemorrhage is detected feature extraction is done to classify its type. Active contours are drawn and features extracted are fed to the decision tree. The classifier helps in finding the type of hemorrhage with the detected features. The classifier result can be viewed, interpreted and evaluated by medical assistance. The aim of this research is to increase the chance of predicting hemorrhage in the image and then to classify its type. The proposed system classifies three types of hemorrhages. The average accuracy of the system in classifying the three types of hemorrhage is found as 98%

scholarly journals Sistem Cerdas untuk Mendeteksi Dini Penyakit Jantung Dengan Decision Tree

Jurnal INFORM ◽  
2016 ◽  
Vol 1 (2) ◽  
Author(s):  
Slamet Kacung
Keyword(s):  
Heart Disease ◽  
Early Detection ◽  
Decision Tree ◽  
Heart Attack ◽  
Intelligent System ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
The World ◽  
The Many ◽  
Heart Foundation

Abstract - Heart attack is the deadliest disease in the world including Indonesia. According to the report the heart Foundation Indonesia showed that the death toll reached more than 27 of 100 people due to heart disease. Early detection of heart disease is very needed considering the many people who suffer from heart disease on average already advanced stage. Intelligent system of early detection of heart disease is a method to know the symptoms that need to be alerted immediately so that heart disease could be known as early as possible. The methods used in this study using Decision Tree Classifier, the datasheet used are taken from the UCI Machine Learning Repository consisting of thirteen 270 instance, attribute input and 1 target attribute.The results of this research will result in a decision tree that can help the community and or used as a reference for a doctor in diagnosing early heart disease. The second is this research can also predict a person can be diagnosed with heart disease or not by giving the input a few symptoms that are already established, the research results cannot replace an existing heart examination but at least it can help society in General nor the doctor.

scholarly journals Prediction of Student Performance Using Decision Tree Classifier

2019 ◽  
Vol 131 ◽  
pp. 1-12
Author(s):  
V Ramakrishna Sajja ◽  
P Jhansi Lakshmi ◽  
DS Bhupal Naik ◽  
Hemantha Kumar Kalluri
Keyword(s):  
Decision Tree ◽  
Student Performance ◽  
Decision Tree Classifier ◽  
Tree Classifier

scholarly journals Hybrid Intrusion Detection System Based on the Stacking Ensemble of C5 Decision Tree Classifier and One Class Support Vector Machine

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 173 ◽  
Author(s):  
Ansam Khraisat ◽  
Iqbal Gondal ◽  
Peter Vamplew ◽  
Joarder Kamruzzaman ◽  
Ammar Alazab
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Decision Tree ◽  
False Alarm ◽  
Intrusion Detection System ◽  
Detection System ◽  
Support Vector ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
False Alarm Rates

Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion detection mechanisms to monitor computer resources and generate reports on anomalous or suspicious activities. Many Intrusion Detection Systems (IDSs) use a single classifier for identifying intrusions. Single classifier IDSs are unable to achieve high accuracy and low false alarm rates due to polymorphic, metamorphic, and zero-day behaviors of malware. In this paper, a Hybrid IDS (HIDS) is proposed by combining the C5 decision tree classifier and One Class Support Vector Machine (OC-SVM). HIDS combines the strengths of SIDS) and Anomaly-based Intrusion Detection System (AIDS). The SIDS was developed based on the C5.0 Decision tree classifier and AIDS was developed based on the one-class Support Vector Machine (SVM). This framework aims to identify both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the benchmark datasets, namely, Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Australian Defence Force Academy (ADFA) datasets. Studies show that the performance of HIDS is enhanced, compared to SIDS and AIDS in terms of detection rate and low false-alarm rates.

FALL DETECTION USING THREE WEARABLE TRIAXIAL ACCELEROMETERS AND A DECISION-TREE CLASSIFIER

2014 ◽  
Vol 26 (05) ◽  
pp. 1450059 ◽  
Author(s):  
Kan Luo ◽  
Jianqing Li ◽  
Jianfeng Wu ◽  
Hua Yang ◽  
Gaozhi Xu
Keyword(s):  
Decision Tree ◽  
Detection System ◽  
Human Movement ◽  
Fall Detection ◽  
The Body ◽  
Body Tilt ◽  
Decision Tree Classifier ◽  
Tree Model ◽  
Tree Classifier ◽  
Unintentional Falls

Unintentional falls cause serious health problem and high medical cost, particularly among the elders. Efficient fall detection can ensure fallen subjects with timely rescue, less pain and lower health-care expense. However, the accuracy of the present fall detection system with single accelerometer does not meet the requirement of practical application. In this paper, a fall detection method using three wearable triaxial accelerometers and a decision-tree classifier is proposed. The three triaxial accelerometers are, respectively mounted on the head, the waist and the ankle to capture the acceleration signals of human movement. A Kalman filter is adopted to estimate the body tilt angle. After the features are extracted, the trained decision-tree model is used to predict the fall. The efficiency improvement is evidenced by the scripted and unscripted lateral fall experiments, involving five young healthy volunteers (three males and two females; age: 23.3 ± 1 years). The classification of fall and activities of daily living (ADL) achieve recall, precision and F-value of 93.1%, 95.9%, and 94.5%, respectively, and the system detects all falls during the extended unscripted trials. The experimental results indicate that the complementary movement information coming from three accelerometers can enhance the performance of fall detection. The proposed method is efficient, and it has remarkable improvements in comparison to the method of using one or two accelerometers.

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