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All Journal Infotekmesin Journal of Information Systems and Informatics
Inka Saputri
Universitas Amikom Purwokerto
Computer Science & IT Electrical & Electronics Engineering Mechanical Engineering

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Pengembangan Model Machine Learning Berbasis Linear Discriminant Analysis (LDA) untuk Deteksi Gejala Penyakit Jantung Menggunakan Python Inka Saputri; Chyntia Raras Ajeng Widiawati; Sarmini Sarmini; Ika Romadoni Yunita
Infotekmesin Vol 16 No 2 (2025): Infotekmesin: Juli 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i2.2377

Abstract

Heart disease is the leading cause of death globally and is often not detected early due to limited awareness and the high cost of medical diagnosis. This study aims to develop an accurate and efficient prediction model for heart disease using the Linear Discriminant Analysis (LDA) algorithm. The dataset, obtained from Kaggle, contains 1,024 patient records with 14 clinical attributes, including age, blood pressure, cholesterol, and ECG results. The preprocessing steps include handling outliers, duplicates, class imbalance using SMOTE, and feature standardization. The model was evaluated using cross-validation and learning curve analysis. Results show that the optimized LDA model, tuned with GridSearchCV, achieved an accuracy of 82.54%, a recall of 88.91%, a precision of 79.03%, and an F1-score of 83.54%. The model demonstrates balanced and stable performance, although some misclassification in the positive class remains. This study highlights LDA as a promising method for the early detection of heart disease based on structured clinical data.
Clustering Sugar Content in Children's Snacks for Diabetes Prevention Using Unsupervised Learning Irma Darmayanti; Dhanar Intan Surya Saputra; Inka Saputri; Nurul Hidayati; Nandang Hermanto
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.932

Abstract

Diabetes is a chronic health problem with increasing prevalence, especially among children, due to the consumption of sugary foods/beverages. This study aims to cluster children's snack products based on sugar content using unsupervised learning by combining Hierarchical Clustering and K-Means algorithms optimized using Silhouette Score. This combined approach utilizes Hierarchical Clustering to determine the optimal value (????) of K-Means, ensuring the efficiency and accuracy of data clustering. A total of 157 sample data were effectively clustered with K-means. The test results with Silhouette Score yielded the highest value of 0.380 for 2 clusters, while 3 clusters scored 0.350 and 0.277 for 4 clusters. For this reason, 2 clusters better represent the homogeneity of the data in the cluster, although it has not reached the ideal condition. Further analysis showed that high sugar and calorie content in sugary drinks, including milk, could increase blood glucose levels. These findings can be the basis for the development of consumer-friendly nutrition labels. However, support is needed from the government to create a labelling policy to ensure the sustainability of implementation in the community as an educational effort to prevent the risk of diabetes in children.
Co-Authors Chyntia Raras Ajeng Widiawati Dhanar Intan Surya Saputra Ika Romadoni Yunita Irma Darmayanti Nandang Hermanto Nurul Hidayati Sarmini - Sarmini