scholarly journals Application of Data Mining Classification Method for Student Graduation Prediction Using K-Nearest Neighbor (K-NN) Algorithm

2018 ◽  
Vol 1 (1) ◽  
pp. 1-8
Author(s):  
Mohammad Imron ◽  
Satia Angga Kusumah
Keyword(s):  
Data Mining ◽  
Graduation Rate ◽  
Nearest Neighbor ◽  
High Accuracy ◽  
Classification Method ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Study Program ◽  
Mining Technique ◽  
Student Graduation

The student graduation rate is one of the indicators to improve the accreditation of a course. It is needed to monitor and evaluate student graduation tendencies, timely or not. One of them is to predict the graduation rate by utilizing the data mining technique. Data Mining Classification method used is the algorithm K-Nearest Neighbor (K-NN). The data used comes from student data, student value data, and student graduation data for the year 2010-2012 with a total of 2,189 records. The attributes used are gender, school of origin, IP study program Semester 1-6. The results showed that the K-NN method produced a high accuracy of 89.04%.

scholarly journals Aplikasi Data Mining Untuk Memprediksi Kelulusan Mahasiswa Berbasis Web Menggunakan Algoritma C45

2021 ◽  
Vol 5 (1) ◽  
pp. 41
Author(s):  
Fietri Setiawati Sulaeman ◽  
Mufti Ahmad Rilmansyah
Keyword(s):  
Data Mining ◽  
Data Mining Technique ◽  
Study Program ◽  
Achievement Data ◽  
Mining Technique ◽  
Student Graduation ◽  
Employee Data ◽  
Data Mining Application ◽  
Support Decision Making ◽  
Analyze Data

The Informatics Engineering Study Program, Faculty of Engineering, Suryakancana University in carrying out its operations stores its data in a database consisting of student data, lecturer data, employee data, grade data, student achievement data, and data on the number of student graduations each year, as well as various other data related to all operations of the Faculty of Engineering and Suryakancana University. One of the uses of data in databases is to support decision-making activities, so a data mining   technique is needed to analyze data in order to produce information early on whether the student will graduate on time or not by using a data mining   application to predict student graduation using the C45 algorithm.  Program Studi Teknik Informatika Fakultas Teknik Universitas Suryakancana dalam menjalankan operasionalnya menyimpan datanya dalam sebuah database yang terdiri dari data mahasiswa, data dosen, data pegawai, data nilai, data prestasi mahasiswa, dan data jumlah kelulusan mahasiswa tiap tahun, serta berbagai data lainnya yang berhubungan dengan seluruh operasional Fakultas Teknik dan Universitas Suryakancana. Pemanfaatan data dalam database salah satunya adalah untuk menunjang kegiatan pengambilan keputusan, sehingga diperlukan suatu teknik data mining   untuk menganalisis data agar dapat menghasilkan suatu informasi secara dini apakah mahasiswa itu lulus tepat waktu atau tidak dengan menggunakan aplikasi data mining untuk memprediksi kelulusan mahasiswa menggunakan algoritma C45.

Predicting Customers' Churn Using Data Mining Technique and its Effect on the Development of Marketing Applications in Value-Added Services in Telecom Industry

2018 ◽  
Vol 10 (4) ◽  
pp. 59-72 ◽  
Author(s):  
Sajjad Shokouhyar ◽  
Parna Saeidpour ◽  
Ali Otarkhani
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
General Trend ◽  
Value Added ◽  
Marketing Strategies ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Hidden Patterns ◽  
Using Data

This article aims to predict reasons behind customers' churn in the mobile communication market. In this study, different data mining techniques such as logistic regression, decision trees, artificial neural networks, and K-nearest neighbor were examined. In addition, the general trend of the use of the techniques is presented, in order to identify and analyze customers' behavior and discover hidden patterns in the database of an active Coin the field of VAS1for mobile phones. Based on the results of this article, organizations and companies active in this area can identify customers' behavior and develop the required marketing strategies for each group of customers.

scholarly journals Performance of Naïve Bayes, C4.5 and KNN using Breast Cancer, Iris and Hypothyroid Datasets

2020 ◽  
Vol 9 (3) ◽  
pp. 2193-2197
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Specific Pattern ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Digital Format ◽  
Tree Classifier

Data mining usually specifies the discovery of specific pattern or analysis of data from a large dataset. Classification is one of an efficient data mining technique, in which class the data are classified are already predefined using the existing datasets. The classification of medical records in terms of its symptoms using computerized method and storing the predicted information in the digital format is of great importance in the diagnosis of various diseases in the medical field. In this paper, finding the algorithm with highest accuracy range is concentrated so that a cost-effective algorithm can be found. Here the data mining classification algorithms are compared with their accuracy of finding exact data according to the diagnosis report and their execution rate to identify how fast the records are classified. The classification technique based algorithms used in this study are the Naive Bayes Classifier, the C4.5 tree classifier and the K-Nearest Neighbor (KNN) to predict which algorithm is the best suited for classifying any kind of medical dataset. Here the datasets such as Breast Cancer, Iris and Hypothyroid are used to predict which of the three algorithms is suitable for classifying the datasets with highest accuracy of finding the records of patients with the particular health problems. The experimental results represented in the form of table and graph shows the performance and the importance of Naïve Bayes, C4.5 and K-Nearest Neighbor algorithms. From the performance outcome of the three algorithms the C4.5 algorithm is a lot better than the Naïve Bayes and the K-Nearest Neighbor algorithm.

scholarly journals Analysis K-Nearest Neighbor Algorithm for Improving Prediction Student Graduation Time

SinkrOn ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 42
Author(s):  
Rizki Muliono ◽  
Juanda Hakim Lubis ◽  
Nurul Khairina
Keyword(s):  
Higher Education ◽  
Nearest Neighbor ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Study Program ◽  
Sample Data ◽  
Student Graduation ◽  
K Nearest Neighbor Algorithm

Higher education plays a major role in improving the quality of education in Indonesia. The BAN-PT institution established by the government has a standard of higher education accreditation and study program accreditation. With the 4.0-based accreditation instrument, it encourages university leaders to improve the quality and quality of their education. One indicator that determines the accreditation of study programs is the timely graduation of students. This study uses the K-Nearest Neighbor algorithm to predict student graduation times. Students' GPA at the time of the seventh semester will be used as training data, and data of students who graduate are used as sample data. K-Nearest Neighbor works in accordance with the given sample data. The results of prediction testing on 60 data for students of 2015-2016, obtained the highest level of accuracy of 98.5% can be achieved when k = 3. Prediction results depend on the pattern of data entered, the more samples and training data used, the calculation of the K-Nearest Neighbor algorithm is also more accurate.

scholarly journals The Application Of Fuzzy K-Nearest Neighbour Methods for A Student Graduation Rate

2018 ◽  
Vol 1 (1) ◽  
pp. 47
Author(s):  
Imam Ahmad ◽  
Heni Sulistiani ◽  
Hendrik Saputra
Keyword(s):  
Graduation Rates ◽  
Graduation Rate ◽  
Nearest Neighbor ◽  
Training Data ◽  
Pass Rate ◽  
K Nearest Neighbor ◽  
First Semester ◽  
Long Time ◽  
Student Graduation ◽  
Graduating Students

The absence of prediction system that can provide prediction analysis on the graduation rate of students becomes the reason for the research on the prediction of the level of graduation rate of students. Determining predictions of graduation rates of students in large numbers is not possible to do manually because it takes a long time. For that we need an algorithm that can categorize predictions of students' graduation rates in computing. The Fuzzy Method and KNN or K-Nearest Neighbor Methods are selected as the algorithm for the prediction process. In this study using 10 criteria as a material to predict students' graduation rate consisting of: NPM, Student Name, Semester 1 achievement index, Semester 2 achievement index, Semester 3 achievement index, Semester 4 achievement index, SPMB, origin SMA, Gender , and Study Period. Fuzzyfication process aims to change the value of the first semester achievement index until the fourth semester achievement index into three sets of fuzzy values are satisfactory, very satisfying, and cum laude. Make predictions to improve the quality of students and implement KNN method into prediction, where there are some attributes that have preprocess data so that obtained a value, and the value is compared with training data, so as to produce predictions of graduating students will be on time and graduating students will be late. This study produces a prediction of student pass rate and accuracy.

scholarly journals Penerapan Algoritma K-Nearest Neighbour Dalam Menentukan Pembinaan Koperasi Kabupaten Kotawaringin Timur

2019 ◽  
Vol 5 (3) ◽  
pp. 232
Author(s):  
Yuni Ambar Setianto ◽  
Kusrini Kusrini ◽  
Henderi Henderi
Keyword(s):  
Data Mining ◽  
Human Resources ◽  
Nearest Neighbor ◽  
Classification Method ◽  
Nearest Neighbour ◽  
K Nearest Neighbor ◽  
Cooperative Management ◽  
Length Of Service ◽  
Service Category ◽  
Technical Guidance

Saat ini pembinaan terhadap koperasi yang ada di lingkungan Pemerintah Kabupaten Kotawaringin Timur sangat diperlukan karena adanya koperasi yang baru berdiri maupun yang telah lama berdiri kinerjanya menurun yaitu omset koperasi turun sebesar 30% dan lambat dalam melaksanakan Rapat Anggota Tahunan (RAT) yaitu pada tahun 2016 sebanyak 164 koperasi dan tahun 2017 sebanyak 140 koperasi. Dengan jumlah koperasi saat ini Dinas Koperasi kekurangan SDM pembina, oleh karena itu perlu menentukan koperasi yang diprioritaskan mendapatkan pembinaandengankriteria yaitu jenis koperasi, masa kerja, kategori, jumlah anggota, modal sendiri, volume usaha, dan SHU. Dinas Koperasi dan UKM Kabupaten Kotawaringin Timur melakukan pembinaan dengan bimbingan teknis terhadap koperasidan audit terhadap kepengurusan dan keuangan Koperasi, namun karena belum adanya pedoman dalam menentukan koperasi yang layak untuk dilakukan pembinaan sehingga sering mengakibatkan salah sasaran dalam memilih koperasi yaitu koperasi yang seharusnya mendapat pembinaan tetapi tidak dilaksanakan pembinaan.. Salah satu cara untuk mengatasi permasalah tersebut adalah dengan Data Mining dengan metode klasifikasi menggunakan algoritma K-Nearest Neighbour (K-NN). Penelitian ini menerapkan algoritma K-NN dalam menentukan koperasi yang layak mendapatkan pembina. Hasil yang dari penelitian ini adalah klasifikasi koperasi yang layak mendapatkan pembinaan dengan akurasi yang diperoleh sebesar 96,33%.Kata Kunci — Pembinaan, Klasifikasi, KNNCurrently coaching of cooperatives in Kotawaringin Timur District is very necessary because the existence of cooperatives that established and have long been established, their performance has decreased, namely the turnover of cooperatives fell by 30% and slow in carrying out the RAT, namely in 2016 as many as 164 cooperatives and in 2017 there were 140 cooperatives. Cooperatives currently the Department of Cooperatives is lacking in human resources, it is therefore necessary to determine which cooperatives are prioritized to get guidance with criteria such as type of cooperative, length of service, category, number of members, own capital, business volume, and SHU. Cooperative District conducts coaching with technical guidance on cooperatives and audits of Cooperative management and finance, but due to the lack of guidelines in determining appropriate cooperatives for coaching that often results in mis-targeting in choosing cooperatives, namely cooperatives that should receive guidance but not coaching is carried out. One way to overcome these problems is by Data Mining using a classification method with K-Nearest Neighbor (K-NN) algorithm. This study applies the K-NN algorithm in determining cooperatives that are eligible for guidance. The results is with an accuracy of 96.33%.Keywords — Coaching, Classification, KNN

scholarly journals Aplikasi Prediksi Kelulusan Mahasiswa Berbasis K-Nearest Neighbor (K-NN)

2019 ◽  
Vol 1 (1) ◽  
pp. 30-36 ◽  
Author(s):  
Lalu Abd Rahman Hakim ◽  
Ahmad Ashril Rizal ◽  
Dwi Ratnasari
Keyword(s):  
Nearest Neighbor ◽  
Educational Institution ◽  
Confusion Matrix ◽  
K Nearest Neighbor ◽  
Study Program ◽  
K Value ◽  
Student Graduation ◽  
K Nearest Neighbor Algorithm ◽  
Communication Planning ◽  
Fold Cross Validation

Students are important assets for an educational institution and for this reason, it is necessary to pay attention to the student's graduation rate on time. Presentation of the ups and downs of students' ability to complete their studies on time is one of the elements of campus accreditation assessment. Based on data from the Study Program Section in the last 3 years the student graduation presentation is only 25% of the total students who can complete their studies on time. In this study using the K-Nearest Neighbor algorithm which aims to be able to identify student graduation in new cases by adapting solutions from previous cases that have closeness to new cases. This algorithm has the role to get the value of the closeness of the new case to the old case, which in turn the most population in area K with the closest value obtained by the student is predicted whether to pass on time or not on time. This study uses Roger S. Pressman's waterfalll method, namely Communication, Planning, Modeling, and Construction. Based on the tests carried out using K-Fold Cross Validation, the highest accuracy in the third model was 80% when folded 4th and 61% when the K value = 1. While testing using the Confusion Matrix obtained the highest accuracy of 98% at K = 1 for classification "Timely", and 98% at K = 2 for classification "Not Timely"

scholarly journals PREDIKSI POLA KELULUSAN MAHASISWA MENGGUNAKAN TEKNIK DATA MINING CLASSIFICATION EMERGING PATTERN

Petir ◽  
2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Ida Farida ◽  
Spits Warnars Harco Leslie Hendric
Keyword(s):  
Data Mining ◽  
Teaching And Learning ◽  
Graduation Rate ◽  
Large Data ◽  
University Management ◽  
Study Program ◽  
Student Graduation ◽  
The University ◽  
Process Strategy ◽  
Emerging Pattern

Mercu Buana University is one of the private universities in Indonesia, especially in DKI Jakarta, which has a large number of students and a number of graduations. However, the University management has difficulty predicting a pattern and graduation rate from existing student data in each academic year. Most researchers use data mining techniques to find a regularity of patterns or relationships set on large data. In this paper, to predict patterns and analyze student graduation rates researchers use data mining by focusing on the classification process using emerging pattern algorithms on the timeliness of student studies. In this study the data used came from combined data between student master data and graduation data. The results of testing the data carried out by researchers in the data mining application produce graduation patterns with various variations according to the learning attributes used, namely gender, class, study program, lecture system and student GPA. By using the results of testing this study, it is expected that the resulting data can help the management of the University as a basis for analysis in planning the teaching and learning process strategy to increase the graduation rate on time and as a support for the management of Mercu Buana University

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