scholarly journals Systematic Review on Inclusive Education, Sustainability in Engineering: An Analysis with Mixed Methods and Data Mining Techniques

2020 ◽  
Vol 12 (17) ◽  
pp. 6861
Author(s):  
María Consuelo Sáiz-Manzanares ◽  
Sara Gutiérrez-González ◽  
Ángel Rodríguez ◽  
Lourdes Alameda Cuenca-Romero ◽  
Verónica Calderón ◽  
...  
Keyword(s):  
Data Mining ◽  
Inclusive Education ◽  
Social Transformation ◽  
Educational Innovation ◽  
Heat Map ◽  
Data Mining Techniques ◽  
Active Teaching ◽  
Scientific Papers ◽  
Development Of Competencies ◽  
Inclusive Society

In the last few years, research in the field of sustainability has experienced a significant increase in interest between sustainability and other areas (inclusive education, active methodologies, and society). Moreover, the use of mixed research methods (quantitative and qualitative) along with the application of data mining techniques, enables the analysis of information and the connection between the different studies. The objectives of this paper were: (1) To establish the results of the research related to the concepts of sustainability, inclusive education, and disability. (2) To study the key concepts that are detected in the articles selected with respect to the concepts of sustainability, inclusive education, disability, and their relations. In order to do so, two studies were carried out (quantitative and qualitative). In the first study, K-means and heat map clustering techniques were applied. In the second study, the technique of text mining was applied. One hundred and thirty-three scientific papers were studied, of which 54 fulfilled all the inclusion criteria. Three clusters were found in the first study; cluster 1 included the categories: inclusive society, educational innovation, and active methodologies. Cluster 2 included active methodologies and society and economy and cluster 3 included inclusive society and society and economy. In the second study, the highest Krippendorff’s Alpha coefficient were found in articles that linked sustainability with social transformation stemming from a change in education by means of the use of active teaching methods and technological resources. The research moves towards the development of competencies in sustainability at all stages of the educational system, and in all areas of knowledge.

scholarly journals Analisa Faktor Kesulitan Mahasiswa Dalam Menyelesaikan Skripsi Dengan Algoritma C4.5

2020 ◽  
Vol 2 (1) ◽  
pp. 16-24
Author(s):  
Azi Arisandi ◽  
Irfan Sudahri Damanik ◽  
Ilham Syahputra Saragih
Keyword(s):  
Data Mining ◽  
Research Data ◽  
Dominant Factor ◽  
Learning Resources ◽  
Data Mining Techniques ◽  
C4.5 Algorithm ◽  
Scientific Papers ◽  
Peer Environment

The purpose of this study was to determine the most dominant factor in students' difficulties in completing their thesis. In this study, researchers used data mining techniques using the C4.5 algorithm. Sources of research data used were obtained from interviews and questionnaires to students randomly in the STIKOM Tunas Bangsa Pematangsiantar environment. The research variables used were motivation to graduate on time (C1), ability to write scientific papers (C2), availability of learning resources (C3), guidance process (C4), and peer environment (C5). In this study, the alternative used as a sample is the student of STIKOM Tunas Bangsa, semester eight. The results of the classification using the C4.5 algorithm and testing with rapidminer software found that the most dominant factor in student difficulty in completing theses was the motivation to graduate on time (C1) with a gain value of 0.204593531.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

How Sweet and Ripe are the Fruits? Data Mining Techniques for Classifying and Predicting ‘Quick-Wins’ Direct Capital Investment in Indonesia as One Approach to Business intelligence Orientation and Knowledge Management Scenarios of Indonesian Enterprises

2019 ◽  
Vol 1 (1) ◽  
pp. 121-131
Author(s):  
Ali Fauzi
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Direct Investment ◽  
Capital Investment ◽  
Export Market ◽  
Added Value ◽  
Knowledge Generation ◽  
Management Scenarios ◽  
Data Mining Techniques ◽  
Knowledge Based Economy

The existence of big data of Indonesian FDI (foreign direct investment)/ CDI (capital direct investment) has not been exploited somehow to give further ideas and decision making basis. Example of data exploitation by data mining techniques are for clustering/labeling using K-Mean and classification/prediction using Naïve Bayesian of such DCI categories. One of DCI form is the ‘Quick-Wins’, a.k.a. ‘Low-Hanging-Fruits’ Direct Capital Investment (DCI), or named shortly as QWDI. Despite its mentioned unfavorable factors, i.e. exploitation of natural resources, low added-value creation, low skill-low wages employment, environmental impacts, etc., QWDI , to have great contribution for quick and high job creation, export market penetration and advancement of technology potential. By using some basic data mining techniques as complements to usual statistical/query analysis, or analysis by similar studies or researches, this study has been intended to enable government planners, starting-up companies or financial institutions for further CDI development. The idea of business intelligence orientation and knowledge generation scenarios is also one of precious basis. At its turn, Information and Communication Technology (ICT)’s enablement will have strategic role for Indonesian enterprises growth and as a fundamental for ‘knowledge based economy’ in Indonesia.

Literature Study –Data Mining Techniques on Detecting Fradulent Activities in Credit Card

2017 ◽  
Vol 6 (10) ◽  
pp. 60
Author(s):  
S. K. Saravanan ◽  
G. N. K. Suresh Babu
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Data Transfer ◽  
Study Data ◽  
Time Requirement ◽  
Literature Study ◽  
Data Mining Techniques ◽  
Online Transactions ◽  
Secured Data ◽  
Credit Card Usage

In contemporary days the more secured data transfer occurs almost through internet. At same duration the risk also augments in secure data transfer. Having the rise and also light progressiveness in e – commerce, the usage of credit card (CC) online transactions has been also dramatically augmenting. The CC (credit card) usage for a safety balance transfer has been a time requirement. Credit-card fraud finding is the most significant thing like fraudsters that are augmenting every day. The intention of this survey has been assaying regarding the issues associated with credit card deception behavior utilizing data-mining methodologies. Data mining has been a clear procedure which takes data like input and also proffers throughput in the models forms or patterns forms. This investigation is very beneficial for any credit card supplier for choosing a suitable solution for their issue and for the researchers for having a comprehensive assessment of the literature in this field.

Survey of Data Mining Techniques Used for Real Time Churn Prediction

2017 ◽  
Vol 7 (3) ◽  
pp. 219-222 ◽  
Author(s):  
Jean Claude Turiho ◽  
◽  
Wilson Cheruiyot ◽  
Anne Kibe ◽  
Irénée Mungwarakarama ◽  
...  
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Churn Prediction ◽  
Data Mining Techniques

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

Data Mining Techniques for Diagnosis of Diabetes: A Review

2018 ◽  
Vol 6 (9) ◽  
pp. 212
Author(s):  
Tushar Deshmukh ◽  
H. S. Fadewar
Keyword(s):  
Data Mining ◽  
Blood Glucose ◽  
The Body ◽  
Data Mining Techniques ◽  
Early Stages ◽  
Historic Data ◽  
Prediction Of Diabetes

This Diabetes is such a common dieses found all over the globe, in which blood glucose or in normal terminology the sugar level in blood is increased. It is the condition of the body in which the insulin which is required for the metabolism of the food is not created or body cannot use the insulin produced properly. Doctors say that diabetes can be controlled if it is detected in its early stages. Data mining is the process in which the data can be used for the prediction based on historic data. The intention here is to analysis how various researchers have used the data mining for better prediction of diabetes so that it could be controlled and possible even cured.

The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques

2018 ◽  
Vol 7 (1) ◽  
pp. 72
Author(s):  
Khalid AA Abakar ◽  
Chongwen Yu
Keyword(s):  
Data Mining ◽  
Kernel Functions ◽  
Support Vector ◽  
Ann Model ◽  
Data Mining Techniques ◽  
Yarn Quality ◽  
Yarn Properties ◽  
Svm Model ◽  
Rbf Kernel

This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.

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