https://sanscientific.com/journal/index.php/ise <table width="801"> <tbody> <tr> <td> <p><img src="https://images2.imgbox.com/f9/60/bU8Xse65_o.jpg" alt="image host" width="176" height="247" /></p> </td> <td> </td> <td> <p align="justify"><strong>The Informatics and Software Engineering (ISE)</strong> 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 journal's 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.</p> <p align="justify">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. </p> </td> </tr> </tbody> </table> <p align="justify">The journal's goal is to promote communication and collaboration between and among academic and other research groups, as well as the founders of start-ups and technology decision-makers at private and public institutions, national and global, and their regulators.</p> <p align="justify">This journal is published semi-annually (<strong>June</strong> and <strong> December</strong>) with a continuous publication system to keep readers and authors updated with the latest progress. If you have questions about the journal, please chat with WhatsApp (+62 81188809646) or/and email us (info-ise@sanscientific.com). You are invited to keep us up-to-date on recent academic research and study areas. </p> <p><strong><em>E-ISSN/P-ISSN: 2988-2222/2988-2818</em></strong></p> <p><strong><em>Submission in English/Bahasa Indonesia</em></strong></p> <p>The online and continuous publication system</p> <h2> </h2> <h2>Indexed By:</h2> <p> </p> <table> <tbody> <tr> <td> <p><a title="GS" href="https://scholar.google.com/citations?hl=en&amp;user=trmqGwUAAAAJ" target="_blank" rel="noopener"><img src="https://images2.imgbox.com/78/6c/9sKp7ytp_o.jpg" alt="imgbox" /></a></p> </td> <td> </td> <td> <p><a title="GARUDA" href="https://garuda.kemdiktisaintek.go.id/journal/view/34510" target="_blank" rel="noopener"><img src="https://images2.imgbox.com/35/1f/s33jAYZV_o.png" alt="imgbox" /></a></p> </td> <td> </td> <td> <p><a title="SCILIT" href="https://www.scilit.net/sources/136773" target="_blank" rel="noopener"><img src="https://images2.imgbox.com/72/82/zyD7OYll_o.png" alt="image host" width="153" height="57" /></a></p> </td> <td> </td> <td> <p><a title="BASE" href="https://www.base-search.net/Search/Results?type=all&amp;lookfor=https%3A%2F%2Fsanscientific.com%2Fjournal%2Findex.php%2Fise&amp;ling=0&amp;oaboost=1&amp;name=&amp;thes=&amp;refid=dcresen&amp;newsearch=1" target="_blank" rel="noopener"><img src="https://images2.imgbox.com/3c/04/03UbLTkR_o.png" alt="imgbox" /></a></p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><a href="https://portal.issn.org/resource/ISSN/2988-2222" target="_blank" rel="noopener"><img src="https://images2.imgbox.com/0a/15/MiwKWaGk_o.png" alt="image host" /></a></p> </td> <td> </td> <td> <p><a title="Dimensions" href="https://app.dimensions.ai/discover/publication?search_mode=content&amp;amp;or_facet_source_title=jour.1457806" target="_blank" rel="noopener"><img src="https://images2.imgbox.com/b1/aa/ZEfEgk8G_o.png" alt="imgbox" /></a></p> </td> <td> </td> <td> <p><a title="CROSSREF" href="https://search.crossref.org/search/works?q=Informatics+and+Software+Engineering+%28ISE%29&amp;from_ui=yes" target="_blank" rel="noopener"><img src="https://images2.imgbox.com/c6/25/PY9xSR2d_o.png" alt="imgbox" /></a></p> </td> <td> </td> <td> <p><a href="https://journalstories.ai/journal/2988-2222" target="_blank" rel="noopener"><img src="https://images2.imgbox.com/f9/82/vO8rFkVY_o.png" alt="imgbox" /></a></p> </td> </tr> </tbody> </table> <p> </p> SAN Scientific en-US Informatics and Software Engineering 2988-2818 <p><a href="http://creativecommons.org/licenses/by-sa/4.0/" rel="license"><img style="border-width: 0;" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" alt="Creative Commons License" /></a><br />This work is licensed under a <a href="https://creativecommons.org/licenses/by-sa/4.0/" target="_blank" rel="noopener">CC Attribution-ShareAlike 4.0</a></p> https://sanscientific.com/journal/index.php/ise/article/view/609 <p>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.</p> Sari Susanti Rui Septiansyah Putra Copyright (c) 2026 Sari Susanti, Rui Septiansyah Putra https://creativecommons.org/licenses/by-sa/4.0 2026-06-28 2026-06-28 4 1 1 7 10.58777/ise.v4i1.609 https://sanscientific.com/journal/index.php/ise/article/view/613 <p>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.</p> Muhaqiqin Muhaqiqin Ridho Sholehurrohman Agung Pambudi Copyright (c) 2026 Muhaqiqin, Ridho Sholehurrohman, Agung Pambudi https://creativecommons.org/licenses/by-sa/4.0 2026-06-29 2026-06-29 4 1 8 15 10.58777/ise.v4i1.613