Analisis Sentimen Masyarakat Terhadap Virus Corona Berdasarkan Opini Masyarakat Menggunakan Metode Naïve Bayes Classifier

The current use of social media is not only to communicate between friends, but is often also used as a means to convey an aspiration to the community, especially the Indonesian people regarding government issues, or problems related to health and other problems. One of the uses of this social media is to use it as a means of conveying digital aspirations, such as some slogans that are used as hashtags, namely #dirumahaja #lockdown, #usemasker, #protocol, #imun, #vaccine. From the slogan used as a hashtag, researchers are interested in conducting research on how much negative sentiment and positive sentiment there are, using the Naïve Bayes Classifier method, which is a machine learning method that uses probability calculations. The basic concept used by Nave Bayes is the Bayes Classifier Theorem, which is a theorem in statistics to calculate probability, the Bayes Optimal Classifier calculates the probability of one class from each existing attribute group, and determines which class is the most optimal, as for the advantages of using Nave Bayes Classifier in document classification can be viewed from the process that takes action based on existing data to provide solutions to these sentiments.

On Supervised Classification of Feature Vectors with Independent and Non-Identically Distributed Elements

In this paper, we investigate the problem of classifying feature vectors with mutually independent but non-identically distributed elements that take values from a finite alphabet set. First, we show the importance of this problem. Next, we propose a classifier and derive an analytical upper bound on its error probability. We show that the error probability moves to zero as the length of the feature vectors grows, even when there is only one training feature vector per label available. Thereby, we show that for this important problem at least one asymptotically optimal classifier exists. Finally, we provide numerical examples where we show that the performance of the proposed classifier outperforms conventional classification algorithms when the number of training data is small and the length of the feature vectors is sufficiently high.

A Novel Class-Specific Object-Based Method for Urban Change Detection Using High-Resolution Remote Sensing Imagery

A single-scale object-based change-detection classifier can distinguish only global changes in land cover, not the more granular and local changes in urban areas. To overcome this issue, a novel class-specific object-based change-detection method is proposed. This method includes three steps: class-specific scale selection, class-specific classifier selection, and land cover change detection. The first step combines multi-resolution segmentation and a random forest to select the optimal scale for each change type in land cover. The second step links multi-scale hierarchical sampling with a classifier such as random forest, support vector machine, gradient-boosting decision tree, or Adaboost; the algorithm automatically selects the optimal classifier for each change type in land cover. The final step employs the optimal classifier to detect binary changes and from-to changes for each change type in land cover. To validate the proposed method, we applied it to two high-resolution data sets in urban areas and compared the change-detection results of our proposed method with that of principal component analysis k-means, object-based change vector analysis, and support vector machine. The experimental results show that our proposed method is more accurate than the other methods. The proposed method can address the high levels of complexity found in urban areas, although it requires historical land cover maps as auxiliary data.

Evaluation of methods for the reduction of contaminating host reads when performing shotgun metagenomic sequencing of the milk microbiome

Shotgun metagenomic sequencing is a valuable tool for the taxonomic and functional profiling of microbial communities. However, this approach is challenging in samples, such as milk, where a low microbial abundance, combined with high levels of host DNA, result in inefficient and uneconomical sequencing. Here we evaluate approaches to deplete host DNA or enrich microbial DNA prior to sequencing using three commercially available kits. We compared the percentage of microbial reads obtained from each kit after shotgun metagenomic sequencing. Using bovine and human milk samples, we determined that host depletion with the MolYsis complete5 kit significantly improved microbial sequencing depth compared to other approaches tested. Importantly, no biases were introduced. Additionally, the increased microbial sequencing depth allowed for further characterization of the microbiome through the generation of metagenome-assembled genomes (MAGs). Furthermore, with the use of a mock community, we compared three common classifiers and determined that Kraken2 was the optimal classifier for these samples. This evaluation shows that microbiome analysis can be performed on both bovine and human milk samples at a much greater resolution without the need for more expensive deep-sequencing approaches.

Selecting Optimal Portfolio in Generalised Feed Forward Networks And Self Organized Features Maps Hybrids

We evaluate the performance of 70 Generalised FeedForward and 60 Self Organized Feature Maps models of plainand hybrid form to define the optimal classifier in portfolioselection. We also apply it on a novel model of optimal portfolioselection in hedging aspects.

Contributive Representation-Based Reconstruction for Online 3D Action Recognition

Recent years have seen an increasing trend in developing 3D action recognition methods. However, despite the advances, existing models still suffer from some major drawbacks including the lack of any provision for recognizing action sequences with some missing frames. This significantly hampers the applicability of these methods for online scenarios, where only an initial part of sequences are already provided. In this paper, we introduce a novel sequence-to-sequence representation-based algorithm in which a query sample is characterized using a collaborative frame representation of all the training sequences. This way, an optimal classifier is tailored for the existing frames of each query sample, making the model robust to the effect of missing frames in sequences (e.g. in online scenarios). Moreover, due to the collaborative nature of the representation, it implicitly handles the problem of varying styles during the course of activities. Experimental results on three publicly available databases, UTKinect, TST fall, and UTD-MHAD, respectively, show 95.48%, 90.91%, and 91.67% accuracy when using the beginning 75% portion of query sequences and 84.42%, 60.98%, and 87.27% accuracy for their initial 50%.

Maximizing Welfare with Incentive-Aware Evaluation Mechanisms

Motivated by applications such as college admission and insurance rate determination, we study a classification problem where the inputs are controlled by strategic individuals who can modify their features at a cost. A learner can only partially observe the features, and aims to classify individuals with respect to a quality score. The goal is to design a classification mechanism that maximizes the overall quality score in the population, taking any strategic updating into account. When scores are linear and mechanisms can assign their own scores to agents, we show that the optimal classifier is an appropriate projection of the quality score. For the more restrictive task of binary classification via linear thresholds, we construct a (1/4)-approximation to the optimal classifier when the underlying feature distribution is sufficiently smooth and admits an oracle for finding dense regions. We extend our results to settings where the prior distribution is unknown and must be learned from samples.