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Objective: The purpose of this research was to evaluate and select the best architecture among native convolutional neural network (CNN), MobileNetV2, ResNet50V2, and EfficientNetB0 for predicting the endpoint of the high shear wet granulation process, with accuracy as the main evaluation metric. Methods: The dataset was captured from an industrial camera using static image analysis and was manually labeled as “NOT READY” and “READY” according to the traditional endpoint method based on the mixer’s ampere point in the granulator. The dataset contained a total of 180 images, which were split between training and validation sets. Native CNN and TensorFlow Keras application programming interface (API) were utilized with MobileNetV2, EfficientNetB0, and ResNet50V2 as base feature encoders. Hyperparameters, such as final Fully Connected (FC) layer width, dropout rate, and learning rate, were optimized for binary classification using Keras hyper tuning. Results: The best was the native CNN, it was also the fastest among the three other models, taking only 20-30 ms per step for inference during runtime, though it requires 9000 ms time for training, the longest time among the models. It achieved an accuracy of 98%, and a validation accuracy of 97%. Conclusion: The system was able to determine when a wet granulation process has reached its endpoint based on live images from a camera after being trained on previously labeled data. The native CNN was the best model, offering the fastest runtime performance and the highest accuracy. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is one example of how data science innovation has advanced quickly in recent years and has greatly improved human existence. Neural networks, which are a type of machine learning model, are a fundamental component of deep learning in the field of AI. Deep learning models can carry out feature extraction and classification tasks in a single design because of their numerous neural network layers. Modern machine learning algorithms have been shown to perform worse than this model on tasks including text classification, audio recognition, imaginary, and pattern recognition. Deep learning models have outperformed AI-based methods in sentiment analysis and other text categorization tasks. Text data can originate from a number of places, including social media. Sentiment analysis is the computational examination of textual expressions of ideas and feelings. This study employs the convolutional neural network (CNN), longshort term memory (LSTM), CNN-LSTM, and LSTM-CNN models in a deep learning framework using bidirectional encoder representations from transformers (BERT) data representation to assess the performance of machine learning. The implementation of the model utilises YouTube discussion data pertaining to political films associated with the Indonesian presidential election of 2024. Confusion metrics, including as accuracy, precision, and recall, are then used to analyse the model's performance. [ABSTRACT FROM AUTHOR]

In Indonesia, the number of cases infected with Covid-19 peaked between mid-June and August 2021, resulting in the denial of hospital treatment to some people due to insufficient capacity. Therefore, people treat themselves by using natural ingredients. This study aims to identify plants that can treat Covid-19 with self-medication. This study took a description approach to investigate people using traditional herbs in self-medication for COVID-19 recovery. Snowball sampling is the approach used. Interviews and an online system (gform) were used to collect data. The number of samples collected allows for a more thorough analysis of the 104 respondents. The results showed that women (51.0%), people aged between 56 and 65 (52.9%), Banten residents (32.7%), secondary education (57. 7%), and people with comorbidities (86.5). %) were all interested in using traditional herbal medicine. Reduction of symptoms and results of laboratory tests were used to measure recovery. The most common comorbidities were hypertension (7-14 days to recovery), diabetes mellitus (15-21 days to recovery), and cardiovascular disease (7-14 days and 15-21 days to recovery). The Zingiberaceae family dominates traditional herbs. The most widely used traditional ingredients are Empon-Empon, which consists of red ginger, turmeric, nutmeg, aromatic ginger, bay leaves, lemongrass, galangal, cinnamon, and cloves. The average healing time is 7-14 days (32.7%). In conclusion people who utilized herbal medicines reported a speedy recovery and reduction in the severity of their symptoms. [ABSTRACT FROM AUTHOR]