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St. Hajrah Mansyur
Fakultas Ilmu Komputer, Universitas Muslim Indonesia, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Journal : Journal of Embedded Systems, Security and Intelligent Systems

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Enhancing The Quality of College Decisions Through Decision Tree and Random Forest Models Sitti Rahmah Jabir; Huzain Azis; St. Hajrah Mansyur
Journal of Embedded Systems, Security and Intelligent Systems Vol 5, No 1 (2024): March 2024
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v5i1.1225

Abstract

One of the key indicators of the growth of a college is the number of students that are enrolled in the institution on an annual basis. Student enrollment is a crucial element in the growth of a college, particularly in the case of private institutions. When examining students' aspirations for higher education, several studies use data mining techniques to forecast the interests of students who will pursue college. Researchers adopt various ways to extract valuable information from data. Prior research has shown that the decision tree technique outperforms alternative methods. The random forest, in addition to the decision tree, is often used for predicting data mining tasks. Given the above background information, the author will conduct a study titled "Comparative Analysis of Decision Tree and Random Forest Algorithms in Predicting College Interests." According to the study findings, the decision tree outperforms the random forest in terms of outcomes. The accuracy of the decision tree model is 0.81, whereas the accuracy of the Random Forest model is 0.74. All in all, the Decision Tree approach will be used as the ultimate outcome for the implementation of Business Analytics.
Co-Authors Andi Indah Ayu Lestari Andi Nurwanah Bayu Adrian Ashad Dedy Atmajaya Dolly Indra Erick Irawadi Alwi Fitriani Hasbullah Fitriani Hasbullah Harlinda Lahuddin Herman Herman Heryanti Heryanti Lutfi Budi Ilmawan Muhammad Aqil Amin Muhammad Arfah Iswaniah Muhammad Nusran Nur Amanah Nurul Ilmy Husain Paramadina Mulya Majid Purnawansyah Purnawansyah Ramdaniah Ramdaniah Rini Indriani Alashari Syarif Roufur Rohim Shulfiani Shulfiani Siska Anraeni Sitti Ajriana Amin Sitti Rahmah Jabir Sriwijanaka Sriwijanaka Tasmil Tasmil Ummu Kalsum Wd. Shaqina Rafa Naura