scholarly journals PENERAPAN ALGORITMA K-NEAREST NEIGHBOUR (K-NN) UNTUK PENENTUAN MAHASISWA BERPOTENSI DROP OUT

2019 ◽  
Vol 5 (3) ◽  
Author(s):  
Ni Luh Ratniasih
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Undergraduate Students ◽  
Potential Drop ◽  
Drop Out ◽  
Study Program ◽  
Student Data ◽  
The Status ◽  
High Level ◽  
Drop Outs

ABSTRACT<br />Drop out is a situation where students are expelled from college because of several factors, one of which is because the status of lectures is not active beyond 5 semesters for undergraduate students. The high level of success and low failure of students can reflect the quality of education in higher education. The high level of student drop outs can affect the value of Higher Education accreditation so that it will affect the level of public trust. Student data drop out becomes something important to be researched and analyzed, so it can be seen how the characteristics of students who have the potential to drop out as early as possible. The data of ITB STIKOM Bali students is very much so that they can utilize data mining techniques for data classification. In this study the K-NN method was implemented to classify students as potential drop outs and the student data used in this study were students of the 2014 Information Systems study program using 6 attributes, namely gender, age, religion, class status, practical work, and grades GPA. The results showed that the accuracy of the method was 81.50%.<br />Keywords: KNN, Drop Out, ITB STIKOM Bali<br />ABSTRAK<br />Drop out adalah suatu keadaan dimana mahasiswa dikeluarkan dari perguruan tinggi karena beberapa faktor salah satunya karena status perkuliahannya tidak aktif melebihi 5 semester untuk mahasiswa S1. Tingginya tingkat keberhasilan dan rendahnya kegagalan mahasiswa dapat mencerminkan kualitas pendidikan di perguruan tinggi. Tingginya tingkat drop out mahasiswa dapat mempengaruhi nilai akreditasi Perguruan Tinggi sehingga akan mempengaruhi tingkat kepercayaan masyarakat. Data mahasiswa drop out menjadi sesuatu hal yang penting untuk diteliti dan dianalisa, sehingga dapat diketahui bagaimana karakteristik mahasiswa yang berpotensi drop out sedini mungkin. Data mahasiswa ITB STIKOM Bali sangat banyak sehingga dapat memanfaatkan teknik data mining untuk klasifikasi data. Pada penelitian ini diimplementasikan metode K-NN untuk klasifikasi mahasiswa berpotensi drop out dan data mahasiswa yang digunakan dalam penelitian ini adalah mahasiswa program studi Sistem Informasi angkatan 2014 dengan menggunakan 6 atribut yaitu jenis kelamin, umur, agama, status kelas, kerja praktek, dan nilai IPK. Hasil penelitian menunjukkan bahwa tingkat akurasi metode sebesar 81.50%.<br />Kata kunci: KNN, Drop Out, ITB STIKOM Bali

scholarly journals SISTEM KLASIFIKASI MAHASISWA DROP OUT STIKOM BALI MENGGUNAKAN DATA MINING

2019 ◽  
Vol 5 (2) ◽  
Author(s):  
I Putu Ramayasa ◽  
Ida Bagus Ketut Surya Arnawa
Keyword(s):  
Data Mining ◽  
Data Flow ◽  
Drop Out ◽  
Flow Diagram ◽  
Hidden Information ◽  
Data Flow Diagram ◽  
Using Data ◽  
High Level ◽  
Drop Outs ◽  
Implementation Stage

<p>ABSTRACT<br />The problem that often occurs in the world of education is the high level of student drop outs. This must be addressed so that it does not get worse. There are several efforts that can be made to overcome these problems including finding hidden information from student data stacks in the database. The discovery of hidden information can be done by utilizing data mining techniques with the K-Nearst Neighbor algorithm. STIKOM Bali as one of the universities certainly needs to look for hidden information stored in a database pile that can later be used as a reference in making decisions to overcome student drop-out problems. The results of the research have been done in the form of designing applications using Data Flow Diagrams, Conceptual Databases, Designing Base Models and Table Structures. From the design that has been done and continued with the implementation stage of the K-Nearst Neighbor algorithm on the application. The application that has been built can classify students who are classified as prospective drop outs.<br />Keywords: K-Nearst Neightbor, Klasifikasi, Drop Out<br />ABSTRAK<br />Permasalahan yang banyak terjadi dalam dunia pendidikan adalah tingginya tingkat drop out mahasiswa. Hal ini harus segera ditanggulangi supaya tidak bertambah buruk. Ada beberapa upaya yang dapat dilakukan untuk mengatasi permasalahan tersebut diantaranya menemukan informasi tersembunyi dari tumpukan data mahasiswa dalam database. Penemuan informasi tersembunyi dapat dilakukan dengan cara memanfaatkan teknik data mining dengan algoritma K-Nearst Neighbor. STIKOM Bali sebagai salah satu perguruan tinggi tentunya perlu mencari informasi tersembunyi yang tersimpan dalam tumpukan database yang nantinya dapat dijadikan acuan dalam pengambilan keputusan untuk menanggulangi permasalahan drop out mahasiswa. Hasil penelitian yang telah dilakukan berupa perancangan aplikasi dengan menggunakan Data Flow Diagram, Konseptual Database, Perancangan Basis Model dan Struktur tabel. Dari perancangan yang telah dilakukan dan dilanjutkan dengan tahap implementasi algoritma K-Nearst Neighbor pada aplikasi.Aplikasi yang telah dibangun dapat menglasifikasikan mahasiswa yang tergolong calon drop out.<br />Kata Kunci : K-Nearst Neighbor, Klasifikasi, Drop out</p>

EU Strategies to Encourage Youth Entrepreneurship

2006 ◽  
Vol 20 (1) ◽  
pp. 43-50 ◽  
Author(s):  
Nikolaos Karanassios ◽  
Michail Pazarskis ◽  
Konstantinos Mitsopoulos ◽  
Petros Christodoulou
Keyword(s):  
Higher Education ◽  
Undergraduate Students ◽  
The European Union ◽  
Higher Education System ◽  
Technological Educational ◽  
The Status ◽  
Technological Educational Institute ◽  
Youth Entrepreneurship ◽  
Employment Education ◽  
Analyse Data

The authors present and discuss significant aspects of youth entrepreneurship in the European Union (EU) and, especially, in higher education institutions in Greece. The structure of this paper is as follows. First, the study introduces a conceptual basis for entrepreneurship as defined in the EU and looks at entrepreneurship in the context of actions taken by the European Council and especially by the European Commission. The significance of entrepreneurship, embedded in substantial economic factors such as growth, development, employment, education and training, etc, and its objectives are then discussed, particularly in relation to students in higher education. Second, the study refers briefly to current policies of the Organization for Economic Cooperation and Development (OECD) that could influence the EU's entrepreneurship strategies. Third, the authors assess the status of youth entrepreneurship and its influence on students in the Greek higher education system, applying an empirical methodology. To explore the behaviour and attitudes of HE students towards entrepreneurship, the authors analyse data collected by means of a specially designed questionnaire. The sample selected comprised male and female undergraduate students studying in various disciplines at the Technological Educational Institute (TEI) of Serres. The results are evaluated and their implications for educational programmes at universities, TEIs, business schools, etc, are considered.

scholarly journals Analyzing novices' fun and programming behaviours while playing a serious blocks-based game

2021 ◽  
Vol 29 ◽  
pp. 1337-1355
Author(s):  
Adilson Vahldick ◽  
Maria José Marcelino ◽  
António José Mendes
Keyword(s):  
Higher Education ◽  
Problem Solving ◽  
Middle Schools ◽  
Undergraduate Students ◽  
Perceived Learning ◽  
Trial And Error ◽  
Knowledge And Skills ◽  
Problem Solving Strategies ◽  
Programming Knowledge ◽  
High Level

Blocks-based environments have been used to promote programming learning mostly in elementary and middle schools. In many countries, isolated initiatives have been launched to promote programming learning among children, but until now there is no evidence of widespread use of this type of environment in Brazil and Portugal. Consequently, it is common that many students reach higher education with little or no programming knowledge and skills. NoBug’s SnackBar is a game designed to help promote programming learning. This study examined students' behavior and attitudes when playing the game on their initiative. It used a sample of 33 undergraduate students enrolled in an introductory programming course. The variables studied were students' performance and engagement, satisfaction, and problem-solving strategies. The main findings were (1) better performing students had a high level of perceived learning, (2) all the students had similar perceptions about their fun while playing, (3) the leader board was the most used game element not directly related to learning and (4) the top-ranked students access previous solutions to help them solve a new mission, while the others often use a trial-and-error approach.

scholarly journals Penerapan Metode C4.5 untuk Klasifikasi Mahasiswa Berpotensi Drop Out

2018 ◽  
Vol 10 (2) ◽  
pp. 244-250
Author(s):  
Asmaul Husnah Nasrullah
Keyword(s):  
Graduate Students ◽  
Student Success ◽  
Potential Drop ◽  
Drop Out ◽  
Quality Of Education ◽  
Computation Method ◽  
Correct Classification ◽  
Student Failure ◽  
High Level

The quality of education in universities can be seen from the high level of student success and the low failure of students. One indicator of student failure is the case of Drop Out (stop study). The problem of Drop Out becomes something interesting to study, because this can affect the quality of education. Faculty of Economics UNISAN Gorontalo is a favorite Faculty in UNISAN Gorontalo so it has a number of students of approximately 1000 students until 2017. But the ratio of the number of graduate students and not pass unbalanced. So as to produce the number of students Drop Out approximately 200 students per year. To solve the problem, we proposed a new model by utilizing a C4.5 computation method, in order to produce a pattern based on the results of the correct classification in determining the potential Drop Out students. The results obtained from the application of method C4.5 in this research is the discovery of 17 rules that can be used as a pattern to determine the potential students Drop Out.

scholarly journals Master plan for Vietnam’s higher education system - the status of education planning at Vietnam National University

2019 ◽  
Vol 15 (5b) ◽  
pp. 117
Author(s):  
Pham Thi Thanh Hai
Keyword(s):  
Higher Education ◽  
Curriculum Development ◽  
Master Plan ◽  
Ministry Of Education ◽  
Higher Education System ◽  
Education Planning ◽  
International Trends ◽  
National University ◽  
The Status ◽  
High Level

Master plan of Higher education (HE) plays an important role which should be based on the model of university governance in each country. Vietnam has  undergone a major renovation of higher education since 2009. Reorganizing and rearranging the system of universities and colleges is one of the solutions for the development of higher education. The status of training plan has changed in line with the international trends. This paper analyzes the policy of curriculum development of undergraduate, master and doctoral training fields promulgated by the Ministry of Education and Training and by Vietnam National University (VNU). The national university model has been given a high level of autonomy in the curriculum development. VNU develops cutting-edge scientific disciplines, adjusting training programs to meet the diverse and increasingly demands of domestic and international labor markets

scholarly journals Academic preparedness of students – an exploratory study

2012 ◽  
Vol 8 (1) ◽  
Author(s):  
Linda Du Plessis ◽  
Daleen Gerber
Keyword(s):  
Higher Education ◽  
Academic Success ◽  
Drop Out ◽  
First Year ◽  
Valuable Insight ◽  
Academic Preparedness ◽  
First Year Students ◽  
North West ◽  
Student Failure ◽  
High Level

The high level of student failure, accompanied by an increased drop-out rate, is problematic in higher education. It is especially a concern in programmes with the subjects of Mathematics, Accounting and Science. Over many years, models of student admission and selection have been widely researched both internationally and in South Africa. Research indicates that in the academic domain, underpreparedness results from a combination of a lack of English proficiency, mathematical ability and effective study skills. In view of the above, and government policy directives to broaden access in the scarce skills areas to increase student throughput, foundation provision was introduced for students of Commerce, Information Technology, Business, Mathematics and Informatics courses at the Vaal Triangle Campus (VTC) of North-West University (NWU) in 2010. The question at that time then arose as to what criteria should be used for placing students in the extended programme. The placement of first-year students in appropriate programmes should be done with sensitivity to enhance academic success but, at the same time, should not ‘label’ students as underprepared. This paper provides perspectives on the selection criteria available for predicting academic success/preparedness, and then reports on students’ own experiences. An action research study was conducted on the academic achievement of two cohorts of first-year students at the VTC of NWU. The quantitative results of the performance of first-year students in their core modules are compared to the results of predictive tests written after admission. The results provide valuable insight into the placement of students.Keywords: Academic preparedness, extended programmes, national senior certificate, national benchmark testDisciplines: Education management studies, higher education studies

scholarly journals A comparison between deep learning, naïve bayes and random forest for the application of data mining on the admission of new students

2021 ◽  
Vol 10 (2) ◽  
pp. 324
Author(s):  
Nurhachita Nurhachita ◽  
Edi Surya Negara
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Random Forest ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Birth Date ◽  
Study Program ◽  
Student Data ◽  
New Students ◽  
New Student

<span id="docs-internal-guid-5a78994c-7fff-41c1-c57f-91661e44674c"><span>The process of admitting new students at Universitas Islam Negeri Raden Fatah each year produces a lot of new student data. So that there is an accumulation of student data continuously. The purpose of this study is to compare deep learning, naïve bayes, and random forest on the admission of new students as well as being one of the bases for making decisions to determine the promotion strategy of each study program. The data mining method used is knowledge discovery in database (KDD). The tools used are rapid miner. The attributes used are student ID number, name, program study, faculty, gender, place of birth, date of birth, year of entry, school origin, national examination, type of payment, and nominal payment. The new student data used from 2016 to 2019 was an 18.930 item. The results of this study used deep learning bayes results resulted in an accuracy value of 52.65%, naïve bayes results resulted in an accuracy value of 99.79%, and random forest results resulted in an accuracy value of 44.65%.</span></span>

A Survey of the Job Profiles of Biomedical Informatics Graduates

2016 ◽  
Vol 55 (05) ◽  
pp. 473-477 ◽  
Author(s):  
Evandro Ruiz ◽  
José Baranauskas ◽  
Alessandra Macedo
Keyword(s):  
Higher Education ◽  
Undergraduate Students ◽  
Formal Education ◽  
Practical Knowledge ◽  
Biomedical Informatics ◽  
Database Systems ◽  
Object Oriented Programming ◽  
Systems Software ◽  
Level Of Satisfaction ◽  
High Level

SummaryBackground: In 2003, the University of São Paulo established the first Biomedical Infor -matics (BMI) undergraduate course in Brazil. Our mission is to provide undergraduate students with formal education on the fundamentals of BMI and its applied methods. This undergraduate course offers theoretical aspects, practical knowledge and scientifically oriented skills in the area of BMI, enabling students to contribute to research and methodical development in BMI. Course coordinators, professors and students frequently evaluate the BMI course and the curriculum to ensure that alumni receive quality higher education. Objectives: This study investigates (i) the main job activities undertake by USP BMI graduates, (ii) subjects that are fundamental important for graduates to pursue a career in BMI, and (iii) the course quality perceived by the alumni. Methods: Use of a structured questionnaire to conduct a survey involving all the BMI graduates who received their Bachelor degree before July, 2015 (attempted n = 205). Results: One hundred and forty-five gradu -ates (71 %) answered the questionnaire. Nine out of ten of our former students currently work as informaticians. Seventy-six gradu -ates (52 %) work within the biomedical informatics field. Fifty-five graduates (38 %) work outside the biomedical informatics field, but they work in other IT areas. Ten graduates (7 %) do not work with BMI or any other informatics activities, and four (3 %) are presently unemployed. Among the 145 surveyed BMI graduates, 46 (32 %) and seven (5 %) hold a Master‘s degree and a PhD degree, respectively. Database Systems, Software Engineering, Introduction to Computer Science, Object-Oriented Programming, and Data Structures are regarded as the most important subjects during the higher education course. The majority of the graduates (105 or 72 %) are satisfied with the BMI education and training they received during the undergraduate course. Conclusions: More than half of the gradu -ates from our BMI course work in their primary education area. Besides technical adequacy, the diverse job profiles, and the high level of satisfaction of our graduates indicate the importance of undergraduate courses specialized in the BMI domain are of utmost importance.

scholarly journals Data Warehouse dan Data Mining Pendidikan Tinggi: Studi Kasus Kategori Undur Diri di Universitas Bina Nusantara

2012 ◽  
Vol 3 (2) ◽  
pp. 808
Author(s):  
Sulistyo Heripracoyo
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Data Warehouse ◽  
Case Studies ◽  
Education Institution ◽  
Drop Out ◽  
Real Relationship ◽  
Higher Education Institution ◽  
Specific Meaning

Data warehouse and data mining is used to extract useful information and has a specific meaning and to develop a real relationship between some variables stored in the data/data warehouse. A data warehouse is appropriately designed and added a requirement to provide appropriate data and is useful in making better decisions. Hardware and software facilitate adequate access to such data, analyze and display the results interactively. Data mining software is a highly effective tool that can be used to interrogate the data contained in the data warehouse in order to find a relationship (Neary 1999). This study conducts some literature studies applies some models and case studies in a higher education institution, in terms of the benefits, functions and development. The case study conducted is objected to see the trend and prediction of the number of students who drop out (DO).

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