Analysis of the Decision Tree Method for Determining Interest in Prospective Student College

Authors

  • Safrina Maizura Universitas Labuhanbatu, Indonesia
  • Volvo Sihombing Universitas Labuhanbatu, Indonesia
  • Muhammad Halmi Dar Universitas Labuhanbatu, Indonesia

DOI:

10.33395/sinkron.v8i2.12258

Keywords:

Confusion Matrix, Data Mining, Decision Tree, Orange, Tree Review

Abstract

Education is learning science, skills that are carried out by a person or a group of people. The education level starts from Elementary School Education, Junior High School and High School. Apart from that, the highest level of education is college. Lectures are further education carried out by people to gain knowledge and degrees. In college education everyone can choose their respective majors, according to their wishes and desires. With college education, there will be many prospective students who will go to college. But the interest of prospective students to study varies, there are some prospective students who want to study in public and there are some who want to study privately. Therefore the author will make research about prospective students' interest in college. This study aims to see the college interest of prospective students. For this research a data classification of prospective students will be carried out using the Decision Tree method. For this research stage using the Decision Tree method, the first is data analysis, then data preprocessing, then the Decision Tree method design and finally data mining testing. The classification was carried out using the Decision Tree method using 65 prospective student data. From the results of the classification using the Decision Tree method, the results of the Classification of prospective students who are interested in studying are 46 prospective students. The classification results above show that many prospective students are interested in studying.

GS Cited Analysis

Downloads

Download data is not yet available.

References

M. O. Arowolo, M. O. Adebiyi, A. A. Ariyo, and O. J. Okesola, “A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 19, no. 1, pp. 310–316, 2021, doi: 10.12928/TELKOMNIKA.V19I1.16381.

R. Ali, M. M. Yusro, M. S. Hitam, and M. Ikhwanuddin, “Machine Learning With Multistage Classifiers For Identification Of Of Ectoparasite Infected Mud Crab Genus Scylla,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 19, no. 2, pp. 406–413, 2021, doi: 10.12928/TELKOMNIKA.v19i2.16724.

F. D. Adhinata, N. G. Ramadhan, A. Amrulloh, and A. R. Bahtiar, “Comparison of Supervised Learning Methods for COVID-19 Classification on Chest X-Ray Image,” CommIT J., vol. 16, no. 2, pp. 195–201, 2022, doi: 10.21512/commit.v16i2.7970.

H. Elmannai and A. D. Al-Garni, “Classification using semantic feature and machine learning: Land-use case application,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 19, no. 4, pp. 1242–1250, 2021, doi: 10.12928/TELKOMNIKA.v19i4.18359.

G. Pattnaik and K. Parvathi, “Machine learning-based approaches for tomato pest classification,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 20, no. 2, pp. 321–328, 2022, doi: 10.12928/TELKOMNIKA.v20i2.19740.

M. A. Ahmed, R. A. Hasan, A. H. Ali, and M. A. Mohammed, “The classification of the modern Arabic poetry using machine learning,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 17, no. 5, pp. 2667–2674, 2019, doi: 10.12928/TELKOMNIKA.v17i5.12646.

T. Uçar and A. Karahoca, “Benchmarking data mining approaches for traveler segmentation,” Int. J. Electr. Comput. Eng., vol. 11, no. 1, pp. 409–415, 2021, doi: 10.11591/ijece.v11i1.pp409-415.

E. M. T. A. Alsaadi, S. F. Khlebus, and A. Alabaichi, “Identification of human resource analytics using machine learning algorithms,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 20, no. 5, pp. 1004–1015, 2022, doi: 10.12928/TELKOMNIKA.v20i5.21818.

J. M. Muñoz-Rodríguez, C. P. Alonso, T. Pessoa, and J. Martín-Lucas, “Identity profile of young people experiencing a sense of risk on the internet: A data mining application of decision tree with CHAID algorithm,” Comput. Educ., vol. 197, no. January, p. 104743, 2023, doi: 10.1016/j.compedu.2023.104743.

N. E. Binti Md Isa, A. Amir, M. Z. Ilyas, and M. S. Razalli, “Motor imagery classification in brain computer interface (BCI) based on EEG signal by using machine learning technique,” Bull. Electr. Eng. Informatics, vol. 8, no. 1, pp. 269–275, 2019, doi: 10.11591/eei.v8i1.1402.

F. I. M. Redzuan and M. Yusoff, “Knots timber detection and classification with C-support vector machine,” Bull. Electr. Eng. Informatics, vol. 8, no. 1, pp. 246–252, 2019, doi: 10.11591/eei.v8i1.1444.

Y. Lian, J. Chen, Z. Guan, and J. Song, “Development of a monitoring system for grain loss of paddy rice based on a decision tree algorithm,” Int. J. Agric. Biol. Eng., vol. 14, no. 1, pp. 224–229, 2021, doi: 10.25165/j.ijabe.20211401.5731.

H. Yun, “Prediction model of algal blooms using logistic regression and confusion matrix,” Int. J. Electr. Comput. Eng., vol. 11, no. 3, pp. 2407–2413, 2021, doi: 10.11591/ijece.v11i3.pp2407-2413.

D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 697–711, 2021, [Online]. Available: http://ejurnal.tunasbangsa.ac.id/index.php/jsakti/article/view/369

N. Agustina, A. Adrian, and M. Hermawati, “Implementasi Algoritma Naïve Bayes Classifier untuk Mendeteksi Berita Palsu pada Sosial Media,” Fakt. Exacta, vol. 14, no. 4, pp. 1979–276, 2021, doi: 10.30998/faktorexacta.v14i4.11259.

Downloads


Crossmark Updates

How to Cite

Maizura, S. ., Sihombing, V. ., & Dar, M. H. . (2023). Analysis of the Decision Tree Method for Determining Interest in Prospective Student College. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(2), 956-979. https://doi.org/10.33395/sinkron.v8i2.12258

Most read articles by the same author(s)

1 2 > >>