Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen
Ervan Febriyanto
Informatika, Universitas Amikom Yogyakarta
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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

Found 1 Documents
Search

 1 
PENERAPAN DATA MINING UNTUK KLASIFIKASI SELEKSI CALON ANGGOTA HMIF AMIKOM YOGYAKARTA MENGGUNAKAN METODE ALGORITMA DECISION TREE C4.5 Windha Mega Pradnya Dhuhita; Mita Pertiwi; Ervan Febriyanto; Zian Fahrudy
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 17 No 1 (2019): Januari 2019
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v17i1.78

Abstract

Members of the Amikom HMIF Organizational Committee then have the authority to carry out their vision and mission or main objectives at Amikom Yogyakarta University, one of which is the implementation of recruitment and placement according to requirements and procedures that have been determined by the Chairperson and other authorized members according to the needs of the formation in each organizational unit or body. Many cases have been found that the selection of members from the same year and during the interview. In the case of the initial C4.5 algorithm, in determining whether or not candidates are eligible to use graduation parameters, namely oral and written interviews, enthusiasm and achievement in organization in the history of education that has been followed. Data mining benefits can be implemented in data parts that contain data in large quantities. The technique used in data mining is a decision tree with a C45 decision tree. The results of the study used 80 data from prospective members who had registered. Producing experimental and evaluation results that C4.5 decision tree algorithm is accurate in determining whether or not prospective members pass.
Co-Authors Mita Pertiwi Windha Mega Pradnya Dhuhita Zian Fahrudy