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Informatics and Software Engineering (ISE)
Published by Santoso Academy Network
ISSN : 29882818     EISSN : 29882222     DOI : https://doi.org/10.58777/ise
Core Subject : Science,
Computer Science & IT
The Informatics and Software Engineering is an open-access and peer-reviewed journal that publishes theoretical and empirical research articles, review papers, and case studies on all major Informatics and Software Engineering topics. The journals mission is to offer a forum for the growing amount of scholarly research on Information Technology and Software Engineering in which it operates. The journal emphasizes theoretical advancements, their application, and empirical, practical, and policy research in global IT technology. The journal provides a platform for professionals in the field of IT to exchange their expertise and experiences. It aims to promote discussions on the design, development, implementation, management, and assessment of diverse IT applications among practitioners, researchers, managers, and IT policymakers. The journals goal is to promote communication and collaboration between and among academic and other research groups, as well as the founder of start-up and technology decision-makers at private and public institutions, national and global, and their regulators.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 32 Documents
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Improving Survival Prediction For Heart Failure Patients Using Random Forest And Grid Search CV Sari Susanti; Rui Septiansyah Putra
Informatics and Software Engineering Vol. 4 No. 1 (2026): June
Publisher : SAN Scientific

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58777/ise.v4i1.609

Abstract

Heart failure remains a major cause of mortality worldwide, and predicting patient survival has become a key area where machine learning can support clinical decision-making. This study aims to improve the accuracy of survival prediction for heart failure patients by applying hyperparameter tuning to the Random Forest algorithm. Using a publicly available dataset from the UCI Machine Learning Repository, a structured machine learning pipeline was developed. This includes data preprocessing, outlier treatment using the capping method, stratified data splitting, and model training. The Random Forest model was first trained using default parameters to establish a baseline, and then optimized using Grid Search Cross Validation to identify the best hyperparameter configuration. Results show that the optimized model achieved improved accuracy (80.83%), recall (66.00%), and F1-score (0.7416) compared to the baseline. These improvements demonstrate that systematic tuning of machine learning models can significantly enhance their predictive capability in clinical settings. The model showed greater sensitivity in identifying high-risk patients, which is essential for early intervention strategies. Although limited by the dataset size, this study offers a replicable framework for predictive modeling in healthcare and underscores the potential of machine learning as a tool for mortality risk stratification.
Application of the SAW Method in a Decision Support System for Determining Non-Academic Achievement Students at XYZ High School Muhaqiqin Muhaqiqin; Ridho Sholehurrohman; Agung Pambudi
Informatics and Software Engineering Vol. 4 No. 1 (2026): June
Publisher : SAN Scientific

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58777/ise.v4i1.613

Abstract

The Simple Additive Weighting (SAW) method was applied in a Decision Support System (DSS) to identify non-academic high-achieving students at SMA XYZ, Central Lampung. The assessment includes four main criteria: competition achievements, organizational involvement, discipline and attendance and ethics and social behavior. This method used weighting, data normalization, and a final score calculation to rank the students objectively. The results showed that SAW effectively reduced subjectivity and produced fair and structured rankings. Among the ten students evaluated, Student 2 achieved the highest score of 9.4. The implementation of SAW in this DSS provided a more accountable basis for decision-making. It can serve as a data-driven evaluation model for non-academic performance in educational institutions.

Page 4 of 4 | Total Record : 32

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