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The high need for artificial intelligence applications in all fields impacts the appropriate dataset for building a good machine. Labeling datasets becomes one of the main tasks needed before training the machine, especially in sentiment analysis. Aspect-based sentiment analysis has more labels in its process than others. The high number of data also impacts the high cost of processing the data, including the labeling process. Nevertheless, all the problems still need to be solved, including multi-label in Indonesian. It is a potential task that needs to be done by giving several labels in an instance. ChatGPT, as one of the Large Language Models (LLM), has a high potential to carry out the labeling process. ChatGPT-3.5 was examined to label for aspect-based sentiment analysis in the Indonesian language in this study. CASA dataset containing 1080 rows was used to evaluate the performance of the model. The results show that comprehensive exploration must be applied to produce optimal ChatGPT performance for classifying multi-label in Indonesian. The study results will impact the efficiency of the labeling process in multi-label case that needs more effort to be finished. [ABSTRACT FROM AUTHOR]

The use of YouTube channels for publication media is increasing. The use of this YouTube channel is also increasingly being used by local governments. The making of topic models is done to trend topics that are currently on various government channels. Topic modeling is done by latent Dirichlet allocation. The problem raised is how to apply this method for modeling Topics on YouTube channels? The research steps include (1) identification of Youtube channels, (2) retrieval of government channel data, (3) modeling. Using the latent Dirichlet allocation method, a model of news topics on the government's YouTube channel is obtained [ABSTRACT FROM AUTHOR]

This paper is the result of research which is the development of previous research. In the previous studies, it was explained about the prediction of COVID-19 based on symptoms experienced in humans. The method used is Recurrent Neural Network (RNN) model. The level of accuraacy in COVID-19 prediction produced in previous studies was 88%. By using the same dataset, the optimal parameters of Maximum Epoch, Learning Rate, Hidden Node, and Momentum are applied to Recurrent Neural Network model. The result of this study indicates that there is an increase in accuracy to 90% with Maximum Epoch value is 400, Learning Rate value is 0.3, 7 Hidden Nodes, and Momentum value is 0.3. [ABSTRACT FROM AUTHOR]

Computer vision is an area of machine learning that enables computers and systems to take actions and make recommendations based on information from digital images, videos, and other visual inputs. The purpose of this paper is to discuss the selection of optimal parameters in car classification training. This paper discusses computer vision training using the CNN algorithm on vehicle miniature classification. One of the factors that affect the performance of Convolutional Neural Network (CNN) is the selection of appropriate parameters. The data we use comes from the primary data we collect. We took photos of 3 types of vehicles, namely buses, cars, and motorcycles with a total of 486 instances. Next we do data preprocessing by resizing 256*256px. Then we conducted various trials of data augmentation i.e: including "horizontal", "vertical", "horizontal and vertical". We experimented with different augmentations to get the highest accuracy. We present several variations of the epoch to identify the best model of CNN. The accuracy results obtained are 97.34%. [ABSTRACT FROM AUTHOR]