The lungs are two of the most crucial organs in the human body because they are connected to the respiratory and circulatory systems. Lung cancer, COVID-19, pneumonia, and other severe diseases are just a few of the many threats. The patient is subjected to an X-ray examination to evaluate the health of their lungs. A radiologist must interpret the X-ray results. The rapid advancement of technology today can help people in many different ways. One use of deep learning in the health industry is in the detection of diseases, which can decrease the amount of money, time, and energy needed while increasing effectiveness and efficiency. There are other methods that can be used, but in this research, the convolutional neural network (CNN) method is only used with three architectures, namely ResNet-50, ResNet-101, and ResNet-152, to aid radiologists in identifying lung diseases in patients. The 21,885 images that make up the dataset for this study are split into four groups: COVID-19, pneumonia, lung opacity, and normal. The three algorithms have fairly high evaluation scores per the experiment results. F1 scores of 91%, 93%, and 94% are assigned to the ResNet-50, ResNet-101, and ResNet-152 architectures, respectively. Therefore, it is advised to use the ResNet-152 architecture, which has better performance values than the other two designs in this study, to categorize lung diseases experienced by patients.

Because the COVID-19 epidemic has limited human activities, it has touched almost every sector. Education is one of the most affected areas. To prevent physical touch between students, schools and campuses must adapt their complete learning system to an online environment. The difficulty with this technique arises when the teachers or lecturers administer exams. It is difficult to oversee pupils one by one online. This research proposes the development of a computer program to aid in this effort. By applying deep learning models, this program can detect a person’s activities during an online exam based on a web camera. The reliability of this system is 84.52% based on the parameter F1-score. This study built an Indonesian-language web-based application. Teachers and lecturers in Indonesia can use this tool to evaluate whether students are cheating on online exams. Unquestionably, this application is a tool that may be utilized to develop distance learning educational technology in Indonesia.

In Indonesia, tomato is one of the horticultural products with the highest economic value. To maintain enhanced tomato plant production, it is necessary to monitor the growth of tomato plants, particularly the leaves. The quality and quantity of tomato plant production can be preserved with the aid of computer technology. It can identify diseases in tomato plant leaves. An algorithm for deep learning with a DenseNet architecture was implemented in this study. Multiple hyperparameter tests were conducted to determine the optimal model. Using two hidden layers, a DenseNet trainable layer on dense block 5, and a dropout rate of 0.4, the optimal model was constructed. The 10-fold cross-validation evaluation of the model yielded an accuracy value of 95.7 percent and an F1-score of 95.4 percent. To recognize tomato plant leaves, the model with the best assessment results was implemented in a mobile application.

The primary strategy for mitigating lost productivity entails promptly, accurately, and efficiently detecting plant pests. Although detection by humans can be useful in detecting certain pests, it is often slower compared to automated methods, such as machine learning. Hence, this study employs a Convolutional Neural Network (CNN) model, specifically GoogleNet, to detect pests within mobile applications. The technique of detection involves the input of images depicting plant pests, which are subsequently subjected to further processing. This study employed many experimental methods to determine the most effective model. The model exhibiting a 93.78% accuracy stands out as the most superior model within the scope of this investigation. The aforementioned model has been included in a smartphone application with the purpose of facilitating Indonesian farmers in the identification of pests affecting their crops. The implementation of an Indonesian language application is a contribution to this research. Using this local language makes it easier for Indonesian farmers to use it. The potential impact of this application on Indonesian farmers is anticipated to be significant. By enhancing pest identification capabilities, farmers may employ more suitable pest management strategies, leading to improved crop yields in the long run.

Posyandu is an Indonesian mother-child health, community-based healthcare. The provision of the Posyandu data quality map is crucial for analyzing results but is limited. This research aimed to (a) demonstrate data quality analysis on its completeness, accuracy, and consistency and (b) map the data quality in Indonesia for evaluation and improvement. An observational study was conducted using the Posyandu application. We observed data in Indonesia from 2019 to 2021. Data completeness was identified using children’s visits/year. Data accuracy was analyzed using WHO anthropometry z-score and implausible z-score values analyzing the outliers. Cronbach’s α of variables was used to know data consistency. STATA 15.1 SE and QGIS 3.10 was used to analyze and map the quality. Data completeness and accuracy in three years show a good start for the pilot project area, continued with declines in pandemic time, while some other areas demonstrated a small start, then slightly increased. The overall consistency decreased through the study period. A good report on data completeness can occur initially in a pilot project area, followed by others. Data accuracy and consistency can decrease during the pandemic. The app can be promising when synchronized with the government health information system.