Implementation of Deep Learning to Detect Indonesian Hoax News with Convolutional Neural Network Method

This study aims to establish and test a model that is used to determine valid news and hoax news. The method used is the Convolutional Neural Network (CNN) method and Word2Vec as embeddings. The research stages consist of data collection, pre-processing, word embeddings, model formation and testing the results obtained. The data used is 958 news. After testing with the distribution of data by 80% as training data and 20% as test data and 5 times epoch, the model that has been formed can determine valid news and hoax news well. In this study, a model with a vector dimension of 400 as input data and a multiple filter size of 3,4,5 became the best model. The resulting accuracy, precision and recall are 0.91. These results are influenced by the selection of the size of the vector dimensions on the output of Word2Vec, the selection of the filter size on the convolution layer and the addition of the Indonesian Wikipedia corpus into the corpus used

TEKNOLOGI MACHINE LEARNING PADA SISTEM PENDUKUNG PENELITIAN RSYS (RESEARCH SUPPORT SYSTEM)

Students who are still unfamiliar with the revising process focus on the local level, such as grammar, spelling, punctuation, and sentence level. Meanwhile, students who are experts focus on the global level, such as focusing on improving writing goals, ideas, and meanings. When making revisions, students become too focused on the local level rather than the global level. Comments for the local level are ineffective as a guide in the revision process. Therefore, this study aims to build machine learning-based software with the ANN method to classify global or local comments. This study uses a design research methodology with the SDLC model of prototyping. The results show that the RSYS software was successfully built with machine learning accuracy in classifying 19 comments, obtained from 2 documents, with a 95: 0.5 ratio, 94.74%. Whereas in alpha testing, it was stated that the functionality of the RSYS system and machine learning was considered to be functioning correctly. For beta testing, the largest percentage was 85% for ease of operation and convenience in using the application, 75% for website display and navigation availability

Crowdsourcing geospatial data for Earth and human observations: a review

The transformation from authoritative to user-generated data landscapes has garnered considerable attention, notably with the proliferation of crowdsourced geospatial data. Facilitated by advancements in digital technology and high-speed communication, this paradigm shift has democratized data collection, obliterating traditional barriers between data producers and users. While previous literature has compartmentalized this subject into distinct platforms and application domains, this review offers a holistic examination of crowdsourced geospatial data. Employing a narrative review approach due to the interdisciplinary nature of the topic, we investigate both human and Earth observations through crowdsourced initiatives. This review categorizes the diverse applications of these data and rigorously examines specific platforms and paradigms pertinent to data collection. Furthermore, it addresses salient challenges, encompassing data quality, inherent biases, and ethical dimensions. We contend that this thorough analysis will serve as an invaluable scholarly resource, encapsulating the current state-of-the-art in crowdsourced geospatial data, and offering strategic directions for future interdisciplinary research and applications across various sectors