Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Pendidikan Informatika dan Sains Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Roselilie Simbulan
Universitas Teknologi Yogyakarta
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Physics

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

Found 2 Documents
Search

 1 
Implementasi REST API Web Services pada Aplikasi Sumber Daya Manusia Roselilie Simbulan; Joko Aryanto
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 1 (2024): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v5i1.511

Abstract

Human resources management is a vital component within any organization, serving as a valuable asset and a driving force. However, many companies face challenges when it comes to managing employee data, processing leave requests, and generating accurate salary reports, often relying on cumbersome manual processes. This research aims to address these challenges by implementing a REST API (Representational State Transfer Application Programming Interface) in a web-based human resources application. The REST API is carefully built using the Laravel framework, adhering to REST architecture principles to ensure security during internet-based information exchange. The development methodology employed for this application is the waterfall method, a sequential and structured approach to software development. Through these enhancements, this application is expected to significantly assist HR departments in efficiently managing employee data, expediting leave request processing, and simplifying the generation of comprehensive reports. The anticipated outcomes of this research are expected to equip HR professionals with the necessary tools to efficiently navigate these intricate tasks, ultimately enhancing organizational efficiency and effectiveness.
Educational data mining for informatics grade prediction using a hybrid K-means framework Roselilie Simbulan; Arief Hermawan; Donny Avianto
Jurnal Pendidikan Informatika dan Sains Vol. 15 No. 1 (2026): Jurnal Pendidikan Informatika dan Sains
Publisher : Universitas PGRI Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31571/saintek.v15i1.10728

Abstract

This study proposes a two-phase hybrid Educational Data Mining (EDM) framework that integrates K-Means clustering with supervised classification to predict students' final grade categories in Informatics within the Kurikulum Merdeka competency-based assessment system. The dataset consists of 281 Grade X students, all of whom achieved scores above the Minimum Achievement Criterion (KKTP = 75), with prediction focused on three grade categories: A (≥86), B (80–85), and C (75–79). Four summative assessment scores (S1, S7, S8, and S9) were used as input features. In the first phase, K-Means generated three clusters (Silhouette Score = 0.3198), and the resulting cluster labels were added as an additional feature. In the second phase, Random Forest and Logistic Regression were optimized using Grid Search with 5-Fold Stratified Cross-Validation, while SMOTE was employed to address class imbalance. The results show that Logistic Regression outperformed Random Forest, achieving a test accuracy of 59.65% and a Macro F1-Score of 0.5899, whereas Random Forest achieved 49.12% accuracy and a Macro F1-Score of 0.4799 and exhibited signs of overfitting. Feature importance analysis identified S7, S8, and S9 as the most influential predictors, while the cluster-derived feature contributed more strongly to Random Forest than to Logistic Regression. These findings suggest that well-regularized linear models may generalize better than ensemble methods on small datasets with narrow score distributions. The proposed framework is best positioned as a screening-support tool for early formative intervention in competency-based educational settings.
Co-Authors Donny Avianto Joko Aryanto