Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Journal of Students‘ Research in Computer Science (JSRCS)
Putro , Efrianto Nugroho
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Pemetaan Sasaran Marketing Calon Mahasiswa Baru UBHARA Jaya Menggunakan Algoritma C4.5 Putro , Efrianto Nugroho; Sari , Rafika
Journal of Students‘ Research in Computer Science Vol. 4 No. 1 (2023): Mei 2023
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/3yb0eh36

Abstract

Information technology developed by higher education institutions must be able to solve problems and be the right solution for the effectiveness of the performance of a higher education institution. One of them is in predicting the acceptance of prospective new students which is an important process to test how much public interest is in becoming a student on a campus. In this study, a predictive model was built using a data mining approach. The purpose of this study is to show predictions of the acceptance of prospective new students regarding the attractiveness of prospective students at Bhayangkara Jakarta Raya University (Ubhara) when viewed from the location data of the prospective student's school of origin. So that Ubhara can adjust his marketing strategy in introducing the Ubhara to prospective students. This study uses a dataset of prospective new students who registered at Ubhara in 2019 and 2020 and selected 50 datasets as samples and applied data mining techniques using the C.45 algorithm. Data mining techniques are built through Python programming using the stages of data selection, checking for missing data values ​​and data transformation. The results of this study are used as an evaluation and recommendation for marketing using a confusion matrix, an accuracy rate of 60% is obtained. This can be improved by using more datasets.
Co-Authors Sari , Rafika