Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Building of Informatics, Technology and Science Dinasti International Journal of Education Management and Social Science
Kusumaningtyas, Grasiella Yustika Rezka Talita
Unknown Affiliation
Computer Science & IT Education

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Implementasi Algoritma C4.5 dan Simple Additive Weight Untuk Menentukan KPI Karyawan Kusumaningtyas, Grasiella Yustika Rezka Talita; Wahyuddin, Mohammad Iwan
Building of Informatics, Technology and Science (BITS) Vol 3 No 4 (2022): March 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (529.953 KB) | DOI: 10.47065/bits.v3i4.1356

Abstract

PT Kreatif Dinamika is the one of Microsoft's partner company that provides industrial solutions that consist of ERP and CRM, Power BI, Office 365 mobile applications and custom solutions. Every year conducts employee performance evaluation for employee who have worked at least one year to get an increase sallary. The primary idea of the Simple Additive Weighting technique is to get the full weighted value of every worker's overall performance on all of the attributes of the evaluation component. The C4.5 is an algorithm that used to create a decision tree. Decision tree is the structure division method and a famous predictions. In addition, after deretmine of weighted value of every attribute, SAW can select the good opportunity for classifications process from many alternatives. There are five standards that used for evaluation, there are Delivery On Time, Delivery on budget, Team Satisfaction, Soft Skill and Project Handled. The application of C4.5 algorithm on information explore in large amount include changed it into decision tree which could represent terms well. In the C4.5 algorithm, the choice of attributes as an evaluation could be very influential in acquiring an accuracy value cost primarily based totally at the outcomes of the ranking
Enhancing Organizational Performance Through KPI-Based Employee Prediction Using the C4.5 and Random Forest Methods Kusumaningtyas, Grasiella Yustika Rezka Talita; Triayudi, Agung
Dinasti International Journal of Education Management And Social Science Vol. 6 No. 4 (2025): Dinasti International Journal of Education Management and Social Science (April
Publisher : Dinasti Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/dijemss.v6i4.4502

Abstract

Employee performance evaluation is a crucial process for organizations in achieving strategic goals. PT Kreatif Dinamika Integrasi currently conducts employee assessments manually using Microsoft Excel, leading to inefficiencies, potential errors, and subjectivity. This study aims to develop a decision support system (DSS) using the C4.5 and Random Forest algorithms to improve accuracy and fairness in performance evaluation. The research adopts a quantitative approach, encompassing problem analysis, literature review, data collection, machine learning model implementation, and result evaluation. The key performance indicators (KPIs) used include discipline, punctuality, skills, appearance, and education, which serve as attributes in the classification process. The C4.5 algorithm constructs a decision tree to identify patterns, while Random Forest, as an ensemble method, enhances classification accuracy and reduces the risk of overfitting. The results indicate that using Random Forest improves evaluation accuracy from 85% to 87.5%. The implementation of this DSS provides a more reliable framework for salary and bonus prediction, minimizes bias, and enhances decision-making quality. Overall, integrating machine learning models into employee performance evaluation significantly improves efficiency and transparency.
Co-Authors Agung Triayudi Wahyuddin, Mohammad Iwan