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Journal of Computer Science and Research
Published by Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer Provinsi Sumatera Utara
ISSN : -     EISSN : 29862337     DOI : -
Core Subject : Economy, Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Library & Information Science
Journal of Computer Science and Research (JoCoSiR) is aimed to publish research articles on theoretical foundations of information and computation, and of practical techniques for their implementation and application in computer systems. Journal of Computer Science and Research (JoCoSiR) published quarterly and is a peer reviewed journal covers the latest and most compelling research of the time. Journal of Computer Science and Research (JoCoSiR) is managed and published by APTIKOM Wilayah 1 Sumatera Utara.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
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Search results for , issue "Vol. 3 No. 3 (2025): July: Health Science Informatic" : 5 Documents clear
Predicting Public Health Risks Based on Lifestyle Factors Using the Support Vector Machine Andri, Andri Ismail Sitepu; Muhammad Iqbal
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 3 (2025): July: Health Science Informatic
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

Public health risks are often influenced by multiple lifestyle factors, such as age, diet, exercise, smoking, and alcohol consumption. This study aims to develop a predictive model for assessing individual health risks using the Support Vector Machine (SVM) algorithm. The dataset used consists of lifestyle attributes, including age, weight, height, exercise frequency, sleep duration, sugar intake, smoking habits, alcohol consumption, marital status, profession, and body mass index (BMI). The data were preprocessed through normalization and label encoding, followed by training and testing using a 70:30 data split. The SVM model employed the Radial Basis Function (RBF) kernel to capture non-linear relationships between variables. Experimental results show that the proposed SVM model achieved an accuracy of approximately 89%, demonstrating strong predictive capability. The confusion matrix analysis revealed that the model effectively distinguishes between high and low health risk categories, while the PCA visualization confirmed clear clustering of classified data. Moreover, the feature importance analysis indicated that age, smoking habits, BMI, and alcohol consumption were the most significant contributors to health risk prediction. Overall, the results suggest that the SVM algorithm is a robust and efficient approach for predicting public health risks based on lifestyle factors. This model can serve as a foundation for preventive health monitoring systems, providing valuable insights for promoting healthier lifestyles and supporting data-driven public health strategies.
Heart Disease Prediction Using Logistic Regression and Random Forest with SHAP Explainability Dimas Prayogi
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 3 (2025): July: Health Science Informatic
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

This study presents a web-based Heart Disease Prediction System developed using Logistic Regression and Random Forest algorithms, enhanced with SHAP explainability. The system predicts the likelihood of heart disease based on key clinical parameters such as age, sex, chest pain type, blood pressure, cholesterol, and heart rate. SHAP values are integrated to provide transparent and interpretable explanations of model predictions. The Random Forest model demonstrated superior performance in capturing nonlinear relationships compared to Logistic Regression. The web application offers an interactive and user-friendly interface that displays correlation heatmaps, feature importance plots, and SHAP visualizations to aid in clinical interpretation. The results indicate that chest pain type, ST depression, and exercise-induced angina are among the most influential predictors. The proposed system successfully achieves accurate and explainable heart disease prediction, contributing to early diagnosis and decision support in healthcare.
Comparative Study of Machine Learning Approaches Based on Artificial Neural Network, Regression, and Clustering for Diabetes Prediction Nauval Alfarizi; Adi Putra; Prima Lydia Yosophin Batubara; Satria Sinurat
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 3 (2025): July: Health Science Informatic
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

This study presents a comparative analysis of three machine learning model and algorithms Artificial Neural Network (ANN), Logistic Regression, and K-Means Clustering using the Pima Indians Diabetes dataset. The main objective is to evaluate the performance of supervised and unsupervised methods in predicting diabetes based on physiological and clinical features. he ANN model was developed using a feedforward and backpropagation approach, Logistic Regression applied the fundamental logit equation, and K-Means Clustering was employed as an unsupervised reference. Model performance was assessed using Accuracy, Precision, Recall, and F1-score for supervised models, and Adjusted Rand Index (ARI) for clustering. Experimental results indicate that Logistic Regression achieved the best accuracy of 0.7573, followed by ANN with 0.7078, while K-Means obtained an ARI of 0.1614. The heatmap comparison shows that supervised models outperform unsupervised approaches, with Logistic Regression offering better interpretability and stability, and ANN demonstrating the ability to model nonlinear relationships. K-Means, though less accurate, provided valuable insight into data structure and natural grouping. Overall, the findings confirm that supervised learning models, particularly Logistic Regression and ANN, are more effective for medical prediction tasks. Future research may explore hybrid or ensemble models that combine the interpretability of Logistic Regression, the adaptability of ANN, and the exploratory capability of clustering to enhance medical diagnostic performance.
Comparison of Naïve Bayes, K-Nearest Neighbors, and Decision Tree Methods for Classifying Heart Disease Risk Factors Ahmad Jihad Al Fayed; Surya Darma; Zailani Sinabariba; Surya Maruli P Pardede
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 3 (2025): July: Health Science Informatic
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

Heart disease is the leading cause of death and poses a major challenge to global health systems. The classification of heart disease risk factors is crucial for preventing serious indications, but the challenge is that detection of this disease is often hampered because the classification process is not yet sufficiently accurate. This study aims to develop a heart disease risk classification model using a machine learning approach on a 2025 dataset consisting of 6025 patient data with 14 features. After going through the data collection stage and determining the attributes for comparing the performance of machine learning algorithms (Naive Bayes, K-Nearest Neighbors, and Decision Tree), it was found that the Decision Tree algorithm provided the best performance with an accuracy of 86%, followed by the K -Nearest Neighbors algorithm with an accuracy of 78% and the Naive Bayes algorithm with an accuracy of 76%.
Comparison of Decision Tree and Random Forest Algorithm Performance for Nutrition Classification in Fast Food Lombu, Perianus; Kiki wulandari
Journal of Computer Science and Research (JoCoSiR) Vol. 3 No. 3 (2025): July: Health Science Informatic
Publisher : Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) Provinsi Sumatera Utara

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Abstract

Fast food has become an essential part of the busy modern lifestyle, fast food is more popular because it makes eating easy and convenient. Today's young people are very fond of instant food. However, excessive consumption of instant food can trigger various health problems, including obsessive eating patterns. This raises the need to develop more accurate analytical methods for classifying fast food nutritional data, the purpose of classification is to obtain a decision tree model that can be used to anticipate and pay attention to how variables in the data are related to each other. In comparing the performance of the Decision Tree and Random Forest Algorithms in processing fast food nutritional data, it was found that all variables were correlated. The implementation results found that both models have extraordinary capabilities. The performance of the Decision Tree and Random Forest Algorithms on the same dataset, Random Forest outperformed Decision Tree with an accuracy value of 66.67%, while Decision Tree only achieved 55.56%, indicating that Random Forest is able to provide more accurate predictions for the test data class. In addition, the characteristics of the Random Forest group, where several decision trees are combined, provide advantages in handling data complexity and improve model generalization. These results indicate that group learning can improve the performance and reliability of predictions in building classification models, especially in the case of complex datasets.

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