1,720,958 research outputs found
Comparison of Convolutional Neural Network Models for Feasibility of Selling Orchids
No full textOrchid flowers are one of the most popular ornamental plants, widely appreciated for their unique features and aesthetic appeal, making them highly potential for sales in the global market. While numerous studies have explored Orchid flower characteristics and disease detection, research on the classification of Orchid salability remains unexplored. This study addresses this gap by classifying Orchid flowers into three categories: saleable, potential saleable, and not saleable. Convolutional Neural Networks (CNN), known for their effectiveness in image-based classification, were employed in this study with performance enhancement through the application of transfer learning. Two prominent transfer learning architectures, VGG-16 and ResNet-50, were implemented and compared to evaluate their suitability for Orchid salability classification. The results demonstrated that the VGG-16 model significantly outperformed ResNet-50 in all evaluation metrics. The VGG-16 model achieved an accuracy of 98%, precision of 99%, recall of 97%, and an F1 score of 98%. In contrast, the ResNet-50 model yielded lower performance, with an accuracy of 69%, precision of 68%, recall of 56%, and an F1 score of 56%. The study also observed that increasing the training epochs from 25 to 50 had no significant impact on the performance of either model. This research highlights the superior performance of VGG-16 in Orchid salability classification and underscores the potential of transfer learning in advancing ornamental plant research
Analysis of the Sentiment of Social Media Users to the Teacher\u27s Room Using the K-Nearest Neighbor (K-NN) Algorithm
No full textThis study was made to classify the KNN (K - Nearest Neighbor) algorithm in Twitter user sentiment analysis from Ruangguru in June during the pandemic 2020. Tweet data used were 700 Indonesian-language tweet data with the distribution of training data and test data using a combination of 80% - 20%. Using the KNN algorithm with TF-IDF word weighting, the sentiment values will be classified into two classes, positive and negative. From the test results it is known that the best accuracy value is 88.21% in the parameter value of k = 13, the highest precision is 70.98% in the parameter k = 15, the results of several tests show that the sentiment towards the Teacher\u27s Roomin June tends to be positive
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Penggunaan Sistem Pendukung Keputusan Dalam Evaluasi Program Pembagian Bantuan Sosial Covid-19
No full textAnalysis of the Sentiment of Social Media Users to the Teacher's Room Using the K-Nearest Neighbor (K-NN) Algorithm
No full textThis study was made to classify the KNN (K - Nearest Neighbor) algorithm in Twitter user sentiment analysis from Ruangguru in June during the pandemic 2020. Tweet data used were 700 Indonesian-language tweet data with the distribution of training data and test data using a combination of 80% - 20%. Using the KNN algorithm with TF-IDF word weighting, the sentiment values will be classified into two classes, positive and negative. From the test results it is known that the best accuracy value is 88.21% in the parameter value of k = 13, the highest precision is 70.98% in the parameter k = 15, the results of several tests show that the sentiment towards the Teacher's Roomin June tends to be positive
Variations on the Author
No full text“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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