Article Per Year (5 Year)
p-Index From 2021 - 2026
1.633
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Jurnal Informatika dan Teknik Elektro Terapan JUTIK : Jurnal Teknologi Informasi dan Komputer JURIKOM (Jurnal Riset Komputer) Jurnal Kridatama Sains dan Teknologi INFOKUM Journal of Computer Networks, Architecture and High Performance Computing Aptekmas : Jurnal Pengabdian Kepada Masyarakat
Dananjaya, Md Wira Putra
Universitas Pendidikan Nasional
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
Model Machine Learning yang Dioptimalkan untuk Prediksi Penyakit Jantung Menggunakan R Shiny Amritha, Yadhurani Dewi; Candrawengi, Ni Luh Putu Ika; Dananjaya, Md Wira Putra; Dayanti, Made Ari Riska
Jurnal Kridatama Sains dan Teknologi Vol 8 No 01 (2026): Jurnal Kridatama Sains dan Teknologi (In Progress)
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v8i01.1994

Abstract

Heart disease continues to be a major contributor to global mortality, highlighting the critical importance of early detection in enhancing patient outcomes. The increasing availability of structured clinical datasets has enabled the application of intelligent systems for risk prediction and diagnostic support. In this paper, the effectiveness of three supervised learning algo- rithms—Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT)—is evaluated for the task of heart disease prediction. This investigation is based on the Heart Failure Prediction dataset sourced from the Kaggle platform. The training process for each model involved a 10-fold cross- validation, with its hyperparameters later being tuned using grid search optimization. Model efficacy was measured against standard classification benchmarks, including accuracy, sensitivity, specificity, and the area under the ROC curve (AUC). The Random Forest model emerged as the most effective, demon- strating superior performance with an AUC of 0.9517, sensitivity of 81.18%, and specificity of 90.44%. To facilitate clinical use, this model was subsequently integrated into a user-friendly web tool built with the R Shiny framework. The interface allows users to input patient-level clinical data and obtain real-time predictions, along with visualizations of feature importance and risk probability. This implementation bridges the gap between algorithm development and practical application, offering a user- friendly decision support tool for early heart disease screening. The findings affirm that machine learning models, when properly tuned and validated, can serve as effective and interpretable tools in clinical decision-making. This work contributes to the advancement of e-health and the integration of AI-driven models into medical workflows
Co-Authors Alicia Alfia Asokawati Amritha, Yadhurani Dewi Candrawengi, Ni Luh Putu Ika Darmaastawan, Kadek Dayanti, Made Ari Riska Gede Humaswara Prathama Gede Indrawan Gusti Agung Ayu Putri Ni Luh Putu Ika Candrawengi Pujayanti, Putu Gita Putu Gita Pujayanti Putu Gita Pujayanti Putu Wira Buana Sariyasa . Yadhurani Dewi Amritha