Article Per Year (5 Year)
p-Index From 2021 - 2026
0.817
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All Journal Journal of Information Systems and Informatics
Rudi Setiawan
Trilogi University
Computer Science & IT

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Application-Driven Parallel Differential Evolution: A Systematic Mapping Review of Scalable Optimization Applications Said Iskandar Al Idrus; Rudi Setiawan
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1695

Abstract

This systematic mapping review examines application-driven parallel Differential Evolution (DE) as a scalable optimization approach for engineering, energy, computing, and intelligent systems. The review is based on a Scopus-only corpus searched on 24 May 2026 using TITLE-ABS-KEY queries related to DE, parallel computing, distributed computing, GPU acceleration, and scalable optimization. From 220 records, 25 unique studies published between 2021 and 2026 were included after screening by year, document type, language, relevance, and methodological alignment. Because the corpus contains heterogeneous benchmark studies, application studies, and hybrid intelligent-system frameworks, the evidence was synthesized thematically rather than through meta-analysis. The synthesis distinguishes explicit parallel-DE implementations from broader scalable DE applications in which scalability is achieved through decomposition, model reformulation, or integration with computationally expensive systems. The findings indicate that GPU acceleration, CUDA, MPI migration, cooperative coevolution, Spark/Hadoop distribution, and resource-aware dispatch frequently report reduced computational cost while preserving or improving solution quality under the evaluated conditions. However, cross-study comparison remains limited by heterogeneous benchmarks, incomplete hardware reporting, inconsistent scalability metrics, and uneven baseline selection. This review contributes a cross-domain taxonomy linking application constraints, DE mechanisms, computational architectures, evaluation metrics, and reported outcomes.
Comparative Evaluation of Machine Learning Models with Class Imbalance Techniques for Employee Turnover Prediction Rudi Setiawan; Gatot Tri Pranoto; Zed Abdullah; Satria Abadi
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1732

Abstract

Employee turnover prediction remains challenging in Human Resource (HR) analytics because class imbalance can reduce the ability of machine learning models to identify employees at genuine risk of leaving. This study develops and evaluates a comprehensive machine learning framework that balances minority-class detection and false-positive control. A publicly available HR dataset containing demographic, organizational, performance, and training-related attributes was analyzed using seven algorithms: Logistic Regression, Support Vector Machine, Multilayer Perceptron, Random Forest, XGBoost, LightGBM, and CatBoost. Cost-sensitive learning and three resampling methods, SMOTEENN, ADASYN, and Tomek Links, were compared through stratified 10-fold cross-validation. Performance was evaluated using ROC-AUC, PR-AUC, Balanced Accuracy, Matthews Correlation Coefficient, G-Mean, Sensitivity, and Specificity, followed by threshold adjustment and SHAP analysis. Original LightGBM achieved the highest discrimination performance (ROC-AUC = 0.5975 ± 0.0546; PR-AUC = 0.2020 ± 0.0426), while cost-sensitive LightGBM produced the most balanced results (Balanced Accuracy = 0.5221 ± 0.0303; MCC = 0.0499 ± 0.0685). SHAP identified Department Type, Current Employee Rating, Training Cost, and Age as key predictors. Overall, integrating cost-sensitive learning, threshold optimization, and explainability improved model interpretability and practical utility for evidence-based HR decision-making processes in employee retention management and planning.
Determining Promotion Priority Using TOPSIS on Trilogi University Qassandra Chaidir; Rudi Setiawan
Journal of Information System and Informatics Vol 4 No 4 (2022): Journal of Information Systems and Informatics
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v4i4.383

Abstract

Promotion is one way to achieve sales targets, private universities are also inseparable from the need for promotions to increase the number of student admissions so it is necessary to carry out various strategies to achieve the targets that have been set so that the promotions carried out by Trilogy University are right on target, a target analysis is needed. In this study, the TOPSIS method was used to rank schools that would be the target of promotion in the hope that promotional activities would be right on target and could increase the income of the number of new students. The results showed that the TOPSIS method was able to rank the target schools to be visited, from the ranking results showed that the number of applicants, the distance from school to university and the status of the school in collaboration with the education office had no significant effect on the ranking results.
Analysis of Frequently Appearing Words in the Titles of 2023 Research Grant Winners in Indonesia Using the TF-IDF Method Rudi Setiawan; Zainul Kisman; Asep Imam
Journal of Information System and Informatics Vol 5 No 4 (2023): Journal of Information Systems and Informatics
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v5i4.599

Abstract

Research activities are an obligation to be carried out by a lecturer, each year the Government of Indonesia through the Ministry of Education, Culture, Research and Technology encourages the improvement of research through a large amount of research funding aid through several schemes of grant competition. By 2023, the percentage of proposals funded was only 22.7% of the total of research proposals submitted as 28.404. One of the problems that arises for the lecturer who follows the research grant is to determine the title of the research. The research aims to identify the words that often appear on the research titles that escape funding from each grant scheme by performing word grinding using the TF-IDF method. The results of this research indicate that in the novice lecturer research grant scheme (PDP) the word that often appears is the word "based" with a total of 434 proposals, in the regular fundamental research (PFR) the word that often appears is "development" of 374 proposals , domestic cooperation research (PKDN) the word that often appears is "based" with 117 proposals, post-graduate research doctoral dissertation research (PPS-PDD) the word that often appears is "model" with 154 proposals, in post-graduate research master's thesis research (PTS-PTM) words that often appear "based" are 191 proposals and in the downstream applied research scheme (PT-JH) words that often appear "based" are 82 proposals. This research can provide an overview of the names of titles funded based on the highest number of occurrences of a word from all titles funded. The words "based", "development" and "model" are the 3 largest words that appear in the titles of proposals funded in the PFR, PKDN, PPS-PDD, PPS-PTM, and PT-JH schemes. For the PDP scheme, the order of the 3 largest words that appear in the title of the proposal is "based", "regency", and "development".
Co-Authors Asep Imam Gatot Tri Pranoto Qassandra Chaidir Said . Iskandar Satria Abadi Zainul Kisman Zed Abdullah