Insufficient knowledge about the decomposition of microplastics from plastic waste in landfills hinders community involvement in waste management and sorting, posing a new threat to the environment and public health. The present study identifies, characterizes, and quantifies the microplastics in landfills soil sample to determine the latest threats posed by microplastics in the environment, particularly in landfills that are close to residential areas. This research is a descriptive study, with soil samples taken from six points in landfill site in Depok City. The abundance and shape of microplastics were characterized using a microscope, while the microplastic types were identified using Fourier Transform Infrared Spectroscopy (FTIR). The results showed that the abundance of microplastics in the Depok City landfill soil was 60,111.67 particles/kg, with the largest percentage being fragments at 63 %. FTIR functional group characterization showed the presence of plastic types, such as Polyethylene (PE), Polyvinyl Chloride (PVC), Polystyrene (PS), Polypropylene (PP), Polyethylene Terephthalate (PET), and Polyamide. The differences in waste types entering the Depok Landfill caused variations in the number, shape, and type of microplastic samples, and this study provides a foundation for mitigating and biodegrading microplastics in the landfill to minimize environmental impact and protect public health.

Early risk prediction and appropriate treatment are believed to be able to delay the occurrence of hypertension and attendant conditions. Many hypertension prediction models have been developed across the world, but they cannot be generalized directly to all populations, including for Indonesian population. This study aimed to develop and validate a hypertension risk-prediction model using machine learning (ML). The modifiable risk factors are used as the predictor, while the target variable on the algorithm is hypertension status. This study compared several machine-learning algorithms such as decision tree, random forest, gradient boosting, and logistic regression to develop a hypertension prediction model. Several parameters, including the area under the receiver operator characteristic curve (AUC), classification accuracy (CA), F1 score, precision, and recall were used to evaluate the models. Most of the predictors used in this study were significantly correlated with hypertension. Logistic regression algorithm showed better parameter values, with AUC 0.829, CA 89.6%, recall 0.896, precision 0.878, and F1 score 0.877. ML offers the ability to develop a quick prediction model for hypertension screening using non-invasive factors. From this study, we estimate that 89.6% of people with elevated blood pressure obtained on home blood pressure measurement will show clinical hypertension.

The morbidity and mortality rate of Dengue Hemorrhagic Fever (DHF) causes social and economic impacts. Dengue prevention efforts have not been effective in reducing the incidence of DHF. This research aims to design and develop a prototype early warning system for DHF. This prototype is designed to determine the potential risk of DHF in Padang City. Method: System development in this research uses the Systems Development Life Cycle (SDLC) method of rapid application development with a choice of prototyping system models. Result: Information system development is done by the surveillance of puskesmas and health cadres who perform system input. Presentation of the information is in the form of tables, charts, and maps showing the situational risk of DHF in the work area of Padang. This information system can be accessed online via smartphone or computer. In Conclusion, the development of the DHF awareness information system can facilitate decision making and planning in the Padang City Health Office.

Introduction: One of the major public health problems is anemia, especially affecting newborn and infant children, adolescent girls, young women, pregnant women, and postpartum women. The cause of anemia is the reduced supply of red blood cells in the human body or the damage or weakening of the structure of red blood cells. One of the preferences of utilizing machine learning is the prediction of results. Objective: The purpose of this study is to compare effective algorithms, related to the origin or source of the data set, data set size, metric evaluation and accuracy and produce predictors in predicting anemia using machine learning. Method: This research uses a scoping review method on 4 databases, namely Scopus, EBSCO, PubMed, and IEEE Xplore from 2019 - 2024 with keywords anemia, algorithms, machine learning, and prediction. The results of screening articles on the Scopus, EBSCO, PubMed, and IEEE Xplore databases obtained 384 articles which were then selected through several stages and obtained 9 articles. Result: The review found that the highest algorithm performance in anemia prediction, namely Penalized Regression (LASSO regression) accuracy above 64%, XGboost accuracy 100% and execution time 0.2404 seconds, Catboost accuracy 97.6%, Random Forest accuracy 95.49% and 72%, J48 algorithm accuracy of 97.7%, Logistic Regression accuracy 66% and AUC 69%, and SVM linear AUC 79.9%. Conclusion: Machine learning can assist in the development of anemia prediction models by exploring large amounts of data and producing precise and fast predictors. The predictors obtained are determined by the selection of algorithms in the study.

Background and Objective: Chromium (Cr) has been widely used in many industrial application. Chromium application in tannery industry has potential adverse effects to workers. This study was aimed to determine the effect of chromium exposure to health effects by measuring malondialdehyde (MDA) content in blood and pulmonary dysfunction among tannery workers. Methodology: A total of 25 workers were included. Ambient air chromium was measured in 6 different locations. Blood and urine were collected to measure MDA content and total chromium. The MDA was determined by using spectrophotometric assay TBAR reaction method and chromium was determined by using graphic furnace Atomic Absorption Spectrometry (AAS). Spirometry was used to evaluate pulmonary dysfunction. Results: Result revealed that urinary Cr in workers was 19.65 μg L–1 (n = 25). Twenty five percent of workers urinary Cr was above Biological Exposure Indices (BEI) ACGIH (≥25 μg L–1). Malondialdehyde (MDA) was not significantly different among high exposed workers (Cr>BEI) and low exposed workers (Cr