COVID-19 is the disease that has spread over the world since December 2019. This disease has a negative impact on individuals, governments, and even the global economy, which has caused the WHO to declare COVID-19 as a PHEIC (Public Health Emergency of International Concern). Until now, there has been no medicine that can completely cure COVID-19. Therefore, to prevent the spread and reduce the negative impact of COVID-19, an accurate and fast test is needed. The use of chest radiography imaging technology, such as CXR and CT-scan, plays a significant role in the diagnosis of COVID-19. In this study, CT-scan segmentation will be carried out using the 3D version of the most recommended segmentation algorithm for bio-medical images, namely 3D UNet, and three other architectures from the 3D UNet modifications, namely 3D ResUNet, 3D VGGUNet, and 3D DenseUNet. These four architectures will be used in two cases of segmentation: binary-class segmentation, where each architecture will segment the lung area from a CT scan; and multi-class segmentation, where each architecture will segment the lung and infection area from a CT scan. Before entering the model, the dataset is preprocessed first by applying a minmax scaler to scale the pixel value to a range of zero to one, and the CLAHE method is also applied to eliminate intensity in homogeneity and noise from the data. Of the four models tested in this study, surprisingly, the original 3D UNet produced the most satisfactory results compared to the other three architectures, although it requires more iterations to obtain the maximum results. For the binary-class segmentation case, 3D UNet produced IoU scores, Dice scores, and accuracy of 94.32%, 97.05%, and 99.37%, respectively. For the case of multi-class segmentation, 3D UNet produced IoU scores, Dice scores, and accuracy of 81.58%, 88.61%, and 98.78%, respectively. The use of 3D segmentation architecture will be very helpful for medical personnel because, apart from helping the process of diagnosing someone with COVID-19, they can also find out the severity of the disease through 3D infection projections.

Accurate lung segmentation plays a vital role in generating 3D projections of lung infections, which contribute to the diagnosis and treatment planning of various lung diseases, including cases like COVID-19. This study capitalizes on the capabilities of deep learning techniques to reconstruct 3D lung projections from CT-scans. In this pursuit, we employ well-established 2D architectural frameworks like UNet, LinkNet, Attention UNet, UNet 3+, and TransUNet. The dataset used comprises 20 3D CT-scans from COVID-19 patients, resulting in over 2900 raw 2D slices. Following preprocessing, the dataset is refined to encompass 2560 2D slices tailored for modeling. Preprocessing procedures involve mask refinement, image resizing, contrast limited adaptive histogram equalization (CLAHE), and image augmentation to enhance the data quality and diversity. Evaluation metrics, including Intersection over Union (IoU) and dice scores, are used to assess the models’ performance. Among the models tested, Attention UNet stands out, demonstrating the highest performance. Its key trait of harnessing attention mechanisms enhances its ability to focus on crucial features. This translates to exceptional results, with an IoU score of 85.36% and dice score of 91.49%. These findings provide valuable insights into guiding the selection of an appropriate architecture tailored to specific requirements, considering factors such as segmentation accuracy and computational resources, in the context of 3D lung projection reconstruction.

Facial recognition technology is now advancing quickly and is being used extensively in a number of industries, including banking, business, security systems, and human-computer interface. However, existing facial recognition models face significant challenges in real-time emotion classification, particularly in terms of computational efficiency and adaptability to varying environmental conditions such as lighting and occlusion. Addressing these challenges, this research proposes a lightweight, yet effective deep learning model based on MobileNetV2 to predict human facial emotions using a camera in real time. The model is trained on the FER-2013 dataset, which consists of seven emotion classes: anger, disgust, fear, joy, sadness, surprise, and neutral. The methodology includes deep learning-based feature extraction, convolutional neural networks (CNN), and optimization techniques to enhance real-time performance on resource-constrained devices. Experimental results demonstrate that the proposed model achieves a high accuracy of 94.23%, ensuring robust real-time emotion classification with a significantly reduced computational cost. Additionally, the model is validated using real-world camera data, confirming its effectiveness beyond static datasets and its applicability in practical real-time scenarios. The findings of this study contribute to advancing efficient emotion recognition systems, enabling their deployment in interactive AI applications, mental health monitoring, and smart environments. Real-world camera data is also used to evaluate the model, demonstrating its usefulness in real-time applications and its efficacy beyond static datasets. The results of this work advance effective emotion identification systems, making it possible to use them in smart settings, interactive AI applications, and mental health monitoring.

Dengue haemorrhagic fever (DHF) is endemic in major cities in Indonesia. West Java Province is one of the provinces in Indonesia with a high number of DHF cases every year. To minimise the spread and increase of DHF cases can be done by controlling the factors that influence it. This study aims to categorise cities/districts in West Java based on factors that influence DHF cases. The data used are population density (Soul/ ), proper sanitation (%), healthy and clean living behaviour (%), access to proper water sources (%), and health index (%) in 27 cities/districts in West Java in 2021. In this study, the method used is the Hierarchical clustering method; namely the single linkage method, the complete linkage method, the average linkage method, and the ward method. The clustering methods are then compared based on the value of their standard deviation. The analysis results show that the best method used is the complete linkage method. The results of clustering areas in West Java based on factors affecting dengue cases obtained three clusters. Cluster 1 is the cluster with the highest level of dengue cases compared to other clusters. Cluster 1 consists of Depok city, Bogor city, Cirebon city, Bandung city, and Cimahi city. The characteristic of cluster 1 is that it has the highest average population density compared to other clusters. Cluster 2 consists of Cirebon, Bekasi, Banjar City, Bogor, Bandung, Karawang, West Bandung, Purwakarta, Kuningan, Majalengka, Pangandaran, Indramayu, Garut, Ciamis, Subang, Sukabumi, Cianjur, Tasikmalaya, and Sumedang. Cluster 2 has the characteristics of having the highest average percentage of households with access to proper sanitation, percentage of households with clean and healthy behaviour, and health index. Cluster 3 is Sukabumi City and Tasikmalaya City. Cluster 3 has characteristics with the lowest average in the percentage of households that have access to proper sanitation and the percentage of households with clean and healthy behaviour.

Infectious diseases are a problem that is still a challenge and has not been resolved in Indonesia. The number of infectious disease cases continues to increase every year in Indonesia. Infectious diseases that are still a problem in Indonesia include tuberculosis (TB), pneumonia and leprosy (Leprosy). Identification of infectious disease endemic areas is an important issue in the health sector, the average rate of people with physical disabilities and deaths originating from infectious diseases. Indonesia, as a country consisting of 34 provinces, including East Java as one of the provinces that has a high rate of infectious disease cases. Therefore, research was conducted using correspondence analysis which aimed to determine the pattern of infectious disease trends and grouping districts / cities in East Java Province based on similarities between the spread of disease sufferers in each district / city. This type of research uses quantitative methods with secondary data obtained from the Central Bureau of Statistics of East Java Province. The results of correspondence analysis show that the spread of pneumonia and tuberculosis in East Java Province has a relative tendency to almost all regencies/cities in East Java, while the spread of leprosy is closer to Sampang Regency. From this research, it can be used to supervise and control infectious diseases in districts / cities in East Java Province, so that the government can adjust policy formulation and actions to prevent an increase in infectious disease cases in East Java