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Contact Name
Hendra Kurniawan
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hendra.kurniawan@darmajaya.ac.id
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jodmapps@darmajaya.ac.id
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Jl. Z.A. Pagar Alam No. 93 Gedong Meneng, Bandar Lampung Lampung
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Journal of Data Science Methods and Applications
ISSN : -     EISSN : 30905605     DOI : https://doi.org/10.30873/jodmapps
Theoretical Foundations: Architecture, Management and Process for Data Science Artificial Intelligence Classification and Clustering Data Pre-Processing, Sampling and Reduction Deep Learning Educational Data Mining Forecasting High Performance Computing for Data Analytics Learning Classifiers Learning Theory Optimization Methods Probabilistic and Statistical Models and Theories Scientific Data and Big Data Analytics Statistical Learning Machine Learning and Knowledge Discovery: Big Data Visualization, Modeling and Analytics Data and Knowledge Visualization Database Technology Knowledge Based Neural Networks Knowledge Discovery (Heterogeneous, Unstructured and Multimedia Data) Knowledge Discovery in Network and Link Data Knowledge Discovery in Social Networks Learning for Streaming Data Machine Learning for High-Performance Computing Multimedia/Stream/Text/Visual Analytics Spatial/Temporal Data Computational Data Science: Big Data Computational for Big Data Analysis Computational Intelligence for Pattern Recognition and Medical Imaging Computer Application for Data Analytics Computer Architecture for Data Analytics Computer Graphics for Data Analytics Data Acquisition, Integration, Cleaning Data Visualizations Data Wrangling Databases Decision Making from İnsights, Hidden Patterns Intelligent Information Retrieval Optimization for Data Analytics Probabilistic And İnformation-Theoretic Methods Search and Mining Support Vector Machines Time Series Analysis Applications: Bioinformatics Applications Biomedical Informatics Applications Biometrics Applications Collaborative Filtering Applications Data and Information Semantics Applications Data Mining Algorithms Applications Data Mining Systems Applications Data Streams Mining Applications Database and Information System Performance Applications Database Systems & Applications Electronic Commerce and Web Technologies Applications Electronic Government & E-participation Applications Graph Mining Applications Healthcare Applications Image Analysis Applications Information Retrieval Applications Multimedia Data Mining Applications Natural Language Processing Applications Pre-Processing Techniques Applications Spatial Data Mining Applications Statistical and Scientific Databases Applications Web Search Applications
Articles 21 Documents
Komparasi Model Machine Learning dalam Memprediksi Penyakit Jantung dengan Pengoptimalisasian Hyperparameter Tunning Permata, Maharani Aulia; Saprianti, Assyifa; Chandra, Aurea Ivana; Yuni, Sundari Putri; Salsabila, Aghitsna; Ningrum, Margareta Oktavia
Journal of Data Science Methods and Applications Vol. 2 No. 1 (2026)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

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Abstract

Heart disease remains one of the leading causes of death worldwide, making early detection efforts essential to minimize more serious risks. This study compares four machine learning algorithms—Random Forest, CatBoost, LightGBM, and XGBoost—to determine which model is most effective in predicting heart disease risk. The dataset used was sourced from Kaggle, comprising a total of 918 data points and 12 clinical features related to cardiovascular conditions. The research process included data pre-processing, class balancing using SMOTE, data partitioning, model training with hyperparameter tuning, and evaluation using various performance metrics. The results showed that Random Forest had the highest discriminatory ability with an AUC value of 0.9385. CatBoost, on the other hand, showed the most stable performance with an accuracy of 0.91 after tuning, and had balanced precision and recall in both classes. LightGBM and XGBoost also provided competitive results, although they were still slightly below the two best models. Overall, this study shows that ensemble methods such as Random Forest and CatBoost have great potential for use as decision support in detecting heart disease earlier and more accurately

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