scholarly journals Penerapan Algoritma Decision Tree C4.5 Untuk Klasifikasi Mahasiswa Berpotensi Drop out Di Universitas Advent Indonesia

TeIKa ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 167-173
Author(s):  
Daniel Sinaga ◽  
Edwin J Solaiman ◽  
Fergie Joanda Kaunang
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Drop Out

Salah satu faktor yang menentukan kualitas perguruan tinggi adalah persentasi kemampuan mahasiswa untuk menyelesaikan studi tepat waktu. Saat ini, masalah kegagalan studi siswa dan faktor - faktor penyebabnya menjadi topik yang menarik untuk diteliti. Perguruan tinggi perlu mendeteksi perilaku mahasiswa yang memiliki status “tidak diinginkan” tersebut sehingga dapat diketahui faktor - faktor penyebab kegagalannya. Berdasarkan uraian di atas, diperlukan analisa terhadap data-data mahasiswa sepeti Jenis Kelamin, Umur, Agama, Tempat tinggal, IPS, Disiplin, dan Hutang, berdasarkan data mahasiswa yang dimiliki sebanyak 97 data sehingga bisa dimanfaatkan dalam pengolahan data mining. Di mana data mining digunakan untuk menggali dan mendapatkan informasi dari data dengan jumlah besar. Salah satu metode data mining adalah pengklasifikasian data. Dengan menggunakan Metode Klasifikasi dengan konsep Algoritma Decision tree C4.5 menghasilkan accuracy sebesar 90.00%, hasil dari precision adalah 87.50, dan hasil dari recall sebesar 100%. Diharapkan dapat meningkatkan keinginan Lembaga Universitas atau Perguruan tinggi untuk memberikan pikiran yang baik, pandangan, dan  kebijakan  baru kepada mahasiswa yang memiliki permasalahan dalam perkuliahan, dengan kata lain memaksimalkan mahasiswa dalam upaya peningkatan persentase minat kuliah mahasiswa.

Assessing Data Mining Approaches for Analyzing Actuarial Student Success Rate

Data Mining ◽  
2013 ◽  
pp. 1819-1834
Author(s):  
Alan Olinsky ◽  
Phyllis A. Schumacher ◽  
John Quinn
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Logistic Regression ◽  
Decision Tree ◽  
Student Success ◽  
Predictive Models ◽  
Drop Out ◽  
Predicting Success ◽  
Best Fitting ◽  
Fitting Model

One way to enhance the likelihood that more students will graduate within the specific major that they begin with is to attract the type of students who have typically (historically) done well in that field of study. This chapter details a study that utilizes data mining techniques to analyze the characteristics of students who enroll as actuarial students and then either drop out of the major or graduate as actuarial students. Several predictive models including logistic regression, neural networks and decision trees are obtained. The models are then compared and the best fitting model is determined. The regression model turns out to be the best predictor. Since this is a very well understood method, it can easily be explained. The decision tree, although its underpinnings are somewhat difficult to explain, gives a clear and well understood output. Not only is the resulting model a good one for predicting success in the major, it also allows us the ability to better counsel students.

scholarly journals PREDIKSI MAHASISWA LULUS TEPAT WAKTU

2018 ◽  
Vol 5 (2) ◽  
pp. 7-13
Author(s):  
Agus Budiyantara ◽  
Irwansyah A
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Drop Out

Prediksi Mahasiswa Lulus Tepat Waktu dibutuhkan oleh Manajemen Perguruan Tinggi dalam menentukan kebijakan preventif terkait pencegahan dini kasus Drop Out (DO). Prediksi ini bertujuan untuk menentukan faktor akademis yang berpengaruh terhadap masa studi dan membangun model prediksi terbaik dengan teknik Data Mining. Atribut yang digunakan untuk Klasifikasi Data Mining ada 11 atribut yaitu NPM, Jenis Kelamin, Usia, Jurusan, Kelas, Pekerjaan, Indek Prestasi Semester 1, Indek Prestasi Semester 2, Indek Prestasi Semester 3, Indek Prestasi Semester 4 dan Keterangan sebagai atribut hasil. Dari hasil evaluasi dan validasi yang telah dilakukan menggunakan tools RapidMiner diperoleh hasil Accuracy dari Metode Decision Tree (C4.5).

scholarly journals KOMPARASI ALGORITMA DECISION TREE, NAIVE BAYES DAN K-NEAREST NEIGHBOR UNTUK MEMPREDIKSI MAHASISWA LULUS TEPAT WAKTU

2020 ◽  
Vol 5 (2) ◽  
pp. 265-270 ◽  
Author(s):  
Agus Budiyantara ◽  
Irwansyah Irwansyah ◽  
Egi Prengki ◽  
Pandi Ahmad Pratama ◽  
Ninuk Wiliani
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Drop Out ◽  
University Management ◽  
K Nearest Neighbor ◽  
Academic Factors ◽  
The University

Private Universities (PTS) compete so tight in providing performance in producing quality graduates. In addition, the number of universities in Indonesia which counts a lot both PTN and PTS makes the higher competition between universities as well. So the university strives to improve quality and provide the best education for service recipients, namely students, where one of the problems if there are some students who are late graduating or not on time so that it becomes an obstacle to the progress of the college. Prediction of students graduating on time is needed by university management in determining preventive policies related to early prevention of Drop Out (DO) cases. This prediction aims to determine the academic factors that influence the period of study and build the best prediction model with Data Mining techniques. There are 11 attributes used for Data Mining Classification, namely NPM, Gender, Age, Department, Class, Occupation, Semester 1 Achievement Index, Semester 2 Achievement Index, Semester 3 Achievement Index, Semester 4 Achievement Index and Information as result attributes. From the results of evaluations and validations that have been carried out using the RapidMiner tools the accuracy of the Decision Tree (C4.5) method is 98.04% in the 3rd test. The accuracy of the Naïve Bayes Method is 96.00% in the 4th test. And the accuracy of the K-Nearest Neighbor Method (K-NN) of 90.00% in the second test.

Assessing Data Mining Approaches for Analyzing Actuarial Student Success Rate

2011 ◽  
pp. 169-185
Author(s):  
Alan Olinsky ◽  
Phyllis A. Schumacher ◽  
John Quinn
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Logistic Regression ◽  
Decision Tree ◽  
Student Success ◽  
Predictive Models ◽  
Drop Out ◽  
Predicting Success ◽  
Best Fitting ◽  
Fitting Model

One way to enhance the likelihood that more students will graduate within the specific major that they begin with is to attract the type of students who have typically (historically) done well in that field of study. This chapter details a study that utilizes data mining techniques to analyze the characteristics of students who enroll as actuarial students and then either drop out of the major or graduate as actuarial students. Several predictive models including logistic regression, neural networks and decision trees are obtained. The models are then compared and the best fitting model is determined. The regression model turns out to be the best predictor. Since this is a very well understood method, it can easily be explained. The decision tree, although its underpinnings are somewhat difficult to explain, gives a clear and well understood output. Not only is the resulting model a good one for predicting success in the major, it also allows us the ability to better counsel students.

scholarly journals SOLUSI PREDIKSI MAHASISWA DROP OUT PADA PROGRAM STUDI SISTEM INFORMASI FAKULTAS ILMU KOMPUTER UNIVERSITAS BINA DARMA

2017 ◽  
Vol 8 (1) ◽  
pp. 177-184
Author(s):  
Ade Putra
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Knowledge Discovery ◽  
Drop Out ◽  
Knowledge Discovery In Database

Data mining merupakan salah satu pengetahuan yang bergerak di bidang penggalian dan pengkajian data, dimana data mining mampu memberikan solusi dalam pemecahan permasalahan, khususnya yang di hadapi oleh program studi Sistem Informasi Fakultas Ilmu Komputer guna menjamin agar mahasiswa Program Studi Sistem Informasi Fakultas Ilmu Komputer Universitas Bina Darma dapat lulus dengan tepat waktu. Adapun tahapan yang digunakan yaitu menggunakan konsep Knowledge Discovery in Database (KDD) yang terdiri dari Selection, Pre – Processing, Transformation, Data Mining dan Interprestation / Evaluation. Pada penelitian ini digunakan metode Clasificassion dengan algoritma Decision Tree atau C4.5., Pada algoritma ini, hasil penilaian yang dipakai untuk menentukan Node sebagai kunci dalam menilai kelayakan mahasiswa yang Drop Out dilihat dari nilai Entropi dan Gain pada masing – masing attribute,. Adapun attribute yang digunakan untuk penilaian Entropi dan Gain pada penelitian ini adalah Indeks Prestasi Kumulatif (IPK), Jumlah SKS yang telah ditempuh, Semester dan Status perkuliahan mahasiswa angkatan 2013. Pada penelitian ini attribute SKS ditetapkan sebagai node 1 dengan nilai Gain terbesar yaitu 0.3276,  yang kemudian di ikuti oleh attribute Semester sebagai node 1.1 dengan nilai Gain sebesar 0.0874.

scholarly journals Prediksi Kelulusan dan Putus Studi Mahasiswa dengan Pendekatan Bertingkat pada Perguruan Tinggi

2021 ◽  
Vol 3 (2) ◽  
pp. 140-148
Author(s):  
Hermanto Hermanto
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Drop Out ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Quality Of Higher Education

Currently, the problem of college failure, its on-time graduation, and the factors that cause it is still an interesting research topic (C. Marquez-Vera, C. Romero and S. Ventura, 2011). This study compares three data mining classification algorithms namely Naive Bayes, Decision Tree and K-Nearest Neighbor to predict graduation and dropout risk for students to improve the quality of higher education and the most accurate algorithms to use Prepare graduation and dropout prediction Student studies. The best algorithm for predicting graduation and dropout is the decision tree with the best accuracy value of 99.15% with a training data ratio of 30%. Keyword : Data Mining; Algoritma Naive Bayes; Decision Tree; K-Nearest Neighbor; Predict Graduation; Drop Out.

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.

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.

Penerapan Metode Klasifikasi Decision Tree dan Algoritma C4.5 dalam Memprediksi Kriteria Nasabah Kredit Mega Auto Finance

2020 ◽  
Vol 7 (2) ◽  
pp. 200
Author(s):  
Puji Santoso ◽  
Rudy Setiawan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Microsoft Excel ◽  
Customer Data ◽  
Data Mining Techniques ◽  
C4.5 Algorithm ◽  
Marketing Costs ◽  
Excel Format ◽  
Data Mining Application

One of the tasks in the field of marketing finance is to analyze customer data to find out which customers have the potential to do credit again. The method used to analyze customer data is by classifying all customers who have completed their credit installments into marketing targets, so this method causes high operational marketing costs. Therefore this research was conducted to help solve the above problems by designing a data mining application that serves to predict the criteria of credit customers with the potential to lend (credit) to Mega Auto Finance. The Mega Auto finance Fund Section located in Kotim Regency is a place chosen by researchers as a case study, assuming the Mega Auto finance Fund Section has experienced the same problems as described above. Data mining techniques that are applied to the application built is a classification while the classification method used is the Decision Tree (decision tree). While the algorithm used as a decision tree forming algorithm is the C4.5 Algorithm. The data processed in this study is the installment data of Mega Auto finance loan customers in July 2018 in Microsoft Excel format. The results of this study are an application that can facilitate the Mega Auto finance Funds Section in obtaining credit marketing targets in the future

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