Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Manajemen Sistem Informasi
Pradipta , Rahman
Unknown Affiliation
Computer Science & IT

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

Found 2 Documents
Search

 1 
Sistem Informasi Karyawan Berbasis Web pada PT. Yamani Lautan Berkah Jambi Pradipta , Rahman; Effiyaldi, Effiyaldi
Jurnal Manajemen Sistem Informasi Vol 8 No 2 (2023): MANAJEMEN SISTEM INFORMASI
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2609.269 KB)

Abstract

Yamani Lautan Berkah Jambi is one of the private companies engaged in ship agency services by serving inter-island sea freight services (domestic), export and import. There is no good HRM/Staff and is considered very less than optimal where the process is still manual and the number of employees available. As for the obstacles that are often faced in the HRM/Staff process ranging from the process of hiring new employees, employee data updates, leave application processes, warning letters, duty letters, and the creation of employee pay slips that are still done manually with recording / creation using agenda books and Ms. Word / Ms. Excel that causes these files often damaged and lost so that it takes a long time when needed. The purpose of this research is to analyze and design Web-Based Employee Information Systems at PT. Yamani Lautan Berkah Jambi for the problems that are being faced. Data collection is done by interview method to the director and HRD section. In addition, system modeling methods using UML (Unified Modelling Language) include: usecase diagrams, activity diagrams, and class diagrams and Balsamiq Mockups to design user interfaces. The output of this research is in the form of a Design or Prototype of a Web-Based Employee Information System at PT. Yamani Lautan Berkah Jambi
Optimization of Machine Learning Models in Student Graduation Prediction Systems Using Ensemble Learning with PSO and SMOTE Hamdani, Hamdani; Susanti, Susanti; Lathifah, Lathifah; Anam, M. Khairul; Pradipta, Rahman
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15335

Abstract

The timely graduation of students is a key metric in evaluating the academic effectiveness of higher education institutions. However, accurately identifying students at risk of delayed graduation remains challenging due to imbalanced data distributions and the instability of single-model prediction approaches. This study proposes an optimized ensemble-based machine learning system for predicting on-time graduation among university students. The model integrates C4.5, K-Nearest Neighbor (KNN), and Random Forest algorithms through a hard voting classifier, which is further optimized using Particle Swarm Optimization (PSO) to determine the most effective weighting configuration. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is implemented, ensuring balanced representation between timely and delayed graduates. A dataset of 809 student academic records from Universitas Sains dan Teknologi Indonesia (USTI) was used, and performance was evaluated using 5-fold cross-validation. The proposed ensemble model achieved an average accuracy of 93.70%, a precision of 0.94, a recall of 0.93, and an F1-score of 0.94, outperforming each individual classifier. These results confirm that the combination of ensemble learning, PSO-based optimization, and data balancing effectively improves both accuracy and model stability. The findings highlight the system’s potential as a reliable decision-support tool for educational institutions to anticipate delayed graduations and improve academic supervision strategies.
Co-Authors Anam, M Khairul Effiyaldi, Effiyaldi Hamdani Hamdani Lathifah, Lathifah Susanti Susanti