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Tegar Wahyu Yudha Pratama
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Journal of Public Health and Community Systems
Published by Litera Integra Nusantara
ISSN : -     EISSN : 31640672     DOI : https://doi.org/10.67580/nexura
Core Subject :
Journal of Public Health and Community Systems NE XURA is a peer reviewed open access scientific journal published by PT Litera Integra Nusantara. The journal provides an international platform for the dissemination of high quality original research articles reviews papers and case studies that advance public health community health systems health policy healthcare management and interdisciplinary health sciences. The journal welcomes original research review articles and case studies in public health population health global health healthcare management and disease prevention. The journal focuses on public health promotion disease prevention health service management community empowerment health policy health economics health education health literacy social determinants of health health equity environmental and occupational health water sanitation and hygiene food safety climate change public health digital health telemedicine public health information systems health information management health surveillance systems and other emerging topics in public health and community health systems. The journal is published in electronic online format three times per year in February June and October. The journal applies a rigorous double blind peer review process to ensure scientific quality validity academic integrity and relevance to the journal scope. NE XURA is derived from the concepts of Nexus and Cura representing collaboration connectivity and healthcare. The journal aims to strengthen public health through integrated community health systems evidence based practices and sustainable healthcare development. The journal encourages collaboration among researchers academics healthcare professionals practitioners and policymakers to address current and emerging public health challenges while improving community wellbeing.
Arjuna Subject : -
Articles 5 Documents
 1 
Application of the XGBoost Algorithm for Stroke Disease Prediction Vira Arsy Dwi Pristyanu; Chalista Nesya Prita Wardani; Melanie Putri Salsavina; Niyalatul Muna
Journal of Public Health and Community Systems Vol. 1 No. 1 (2026): June
Publisher : PT Litera Integra Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67580/nexura.v1i1.3

Abstract

Stroke is one of the non-communicable diseases with a relatively high rate of mortality and disability, making early detection very important to support fast and appropriate patient treatment. This study aims to apply the Extreme Gradient Boosting (XGBoost) algorithm to predict stroke disease based on patient health data. The dataset used was obtained from Kaggle, consisting of 150 patient records, which were divided into 100 training data and 50 testing data. The data processing was carried out using Google Colab, including preprocessing, model training, and performance evaluation stages. The results show that the model achieved an accuracy of 68%, an F1-score of 0.43, and a ROC-AUC of 0.717, indicating that the model has a fairly good classification ability in distinguishing stroke and non-stroke patients. In addition, age, average glucose level, and BMI were the most influential variables in stroke prediction. This study also produced a simple web-based application used to support early stroke detection by allowing input of patient health data and automatically displaying prediction results. Thus, the XGBoost algorithm has potential as a supporting method for early stroke detection using machine learning.
Sleep Disorder Classification Using the Naïve Bayes Algorithm Olivia Natania Anjani; Khusnul Fatimah Azzahra; Anisa Fitria Rahma; Aida Rahma Saqina; Muhammad Yunus
Journal of Public Health and Community Systems Vol. 1 No. 1 (2026): June
Publisher : PT Litera Integra Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67580/nexura.v1i1.8

Abstract

Sleep disorders are a medical condition with a continuously increasing prevalence globally, including in Indonesia, and have a serious impact on an individual’s quality of life, cognitive function, and cardiovascular health. This study aims to classify types of sleep disorders using the Naïve Bayes algorithm as a probabilistic approach in health data mining. The dataset used is the Sleep Health and Lifestyle Dataset, consisting of 400 data records with 13 attributes, covering demographic factors, lifestyle, and physiological parameters. The research stages included data preprocessing, blood pressure feature engineering, and splitting the data into training and test sets with an 80:20 ratio. Model evaluation was performed using a confusion matrix and accuracy, precision, recall, and F1-score metrics. The results showed that the Naïve Bayes model achieved an accuracy of 71.60% on the 81-sample test set. The best performance was observed in the "None" class with an F1-score of 0.83, while the "Insomnia" and "Sleep Apnea" classes could not be successfully identified by the model due to the dominance of the majority class (class imbalance). An example prediction for a 47-year-old female subject with a sleep quality of 7/10, a stress level of 4/10, and a Normal BMI resulted in a classification of "No Sleep Disorder" with a probability of 72.08%. These findings indicate that addressing class imbalance is necessary to improve the classification performance of the Naïve Bayes model on the sleep disorder dataset.
Breast Cancer Classification Using the C4.5 Method with the Breast Cancer Wisconsin Dataset Najwa Meilani; Intan Novitasari; Aprilia Wulandari; Naurah Ananda; Muhammad Yunus
Journal of Public Health and Community Systems Vol. 1 No. 1 (2026): June
Publisher : PT Litera Integra Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67580/nexura.v1i1.14

Abstract

Breast cancer is one of the most life-threatening diseases in the world, particularly among women. This study applies the C4.5 algorithm to classify breast cancer using the Breast Cancer Wisconsin (Diagnostic) Dataset from the UCI Machine Learning Repository, consisting of 569 samples with 30 numerical features. The methods employed include data preprocessing (removal of the ID column), application of the Decision Tree algorithm with entropy criterion representing the Information Gain Ratio in a Python- and Streamlit-based implementation, and model evaluation using an 80:20 data split. Experimental results show that the C4.5 model achieves an accuracy of 95.6%, with an average Precision of 95.7%, average Recall of 95.6%, and average F1-Score of 95.6%. The perimeter_worst attribute was identified as the root node of the decision tree with a threshold value of 114.45, confirming the dominant role of tumor cell geometry size as a predictor of malignancy. This study concludes that the C4.5 algorithm is an effective and interpretable approach for breast cancer classification and has the potential to serve as the basis for a clinical decision support system in oncology.
Classification of Oral Cancer Using the Random Forest Algorithm Zaskia Putri Ratnaning Pratiwi; Alviani Putri Brilianti AJie; Shafira Maharani Ayunindya; Muhammad Ibnu Sa'ad; Mudafiq Riyan Pratama
Journal of Public Health and Community Systems Vol. 1 No. 1 (2026): June
Publisher : PT Litera Integra Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67580/nexura.v1i1.28

Abstract

Oral cancer ranks as the sixth most common cancer worldwide and can be prevented if detected early. The lack of universally available, reliable screening methods has led to the use of machine learning as a promising alternative approach. This study aims to develop an oral cancer classification model using a Random Forest algorithm optimized with GridSearchCV, and to implement it into a Streamlit-based web application. The data is sourced from the Oral Cancer Prediction Dataset on Kaggle, using 1,500 samples from a total of 84,922 data points. Preprocessing included removing irrelevant columns, data cleaning, encoding categorical variables, and normalizing numerical features, resulting in 17 predictor features. The dataset was stratified into training and testing sets at a 70:30 ratio. GridSearchCV optimization yielded the best hyperparameters: n_estimators = 200, max_depth = 10, min_samples_split = 10, and min_samples_leaf = 1. The model achieved an accuracy of 79.91%, a precision of 85.8%, a recall of 68.72%, and an F1-Score of 76.32%. Feature importance analysis shows that the most influential variables, in order, are Treatment Type, Age, Diet, HPV Infection, Oral Hygiene, Alcohol Consumption, Gender, Unexplained Bleeding, Difficulty Swallowing, and Betel Nut Use. The model was successfully integrated into a Streamlit application that displays real-time predictions along with probability values and follow-up recommendations. This system can support early screening for oral cancer in primary healthcare facilities. Further research is recommended using the full dataset and exploring other algorithms to improve performance, particularly the recall value to minimize false negatives.
Heart Disease Prediction Using Support Vector Machine (SVM) Classification Based on Clinical Data Leonita Yulyta Agustin; Nur Alisa Qiroati Sholeha; Siti Aisa Nur Apriliana; Niyalatul Muna
Journal of Public Health and Community Systems Vol. 1 No. 1 (2026): June
Publisher : PT Litera Integra Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67580/nexura.v1i1.34

Abstract

Heart disease is the leading cause of death globally and requires an accurate early prediction system. This study aimed to develop a heart disease classification model using the Support Vector Machine (SVM) method with a Radial Basis Function (RBF) kernel based on the Heart Disease Dataset, which consists of 100 patient records and 13 clinical attributes. The research stages included data preprocessing, feature standardization using StandardScaler, data splitting with an 80:20 ratio, SVM model training, and evaluation using accuracy, precision, recall, F1-score, and confusion matrix metrics. The evaluation results showed that the SVM model with the RBF kernel achieved an accuracy of 85%, with a precision of 0.83 for the negative class and 1.00 for the positive class, recall of 1.00 for the negative class and 0.40 for the positive class, and F1-score of 0.91 for the negative class and 0.57 for the positive class. The confusion matrix produced TN=15, FP=0, FN=3, and TP=2. The low recall of the positive class indicates the model’s limitation in detecting actual heart disease cases (false negatives), mainly caused by class imbalance in the dataset (77 negative : 23 positive) and the limited sample size. Prediction on new clinical data resulted in class 0 (negative), although the data were actually classified as positive, confirming the model’s tendency to produce false negatives in borderline cases. This study highlights the potential of SVM as a tool for early heart disease diagnosis while emphasizing the importance of handling class imbalance and hyperparameter optimization to improve model sensitivity.

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