The feature extraction for classifying words on social media  with the Naïve Bayes algorithm

Lubis, Arif Ridho; Nasution, Mahyuddin K. M.; Sitompul, Opim Salim; Zamzami, Elviawaty Muisa

doi:10.11591/ijai.v11.i3.pp1041-1048

Cited by 9 publications

(3 citation statements)

References 23 publications

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“…Under the condition that the position  and the space  are certain, the gray value can be simplified to ( , ) P i j and the parameter ( , ) ij takes a range of values related to the number of gray levels L , which is a natural number less than or equal to 1 L  . The grayscale co-occurrence matrix uses the grayscale correlation of each pixel in the space to predict the probability of a certain grayscale value, and ultimately to achieve the description of texture features [19]. However, the computational pressure of the model would be too high if the probability of a gray value is calculated for all locations in the space.…”

Section: Extraction Of Texture Features In Video Imagesmentioning

confidence: 99%

Intelligent Traffic Video Retrieval Model based on Image Processing and Feature Extraction Algorithm

Zhao¹,

Wang²

2023

IJACSA

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Intelligent transportation is a system that combines data-driven information with traffic management to achieve intelligent monitoring and retrieval functions. In order to further improve the retrieval accuracy of the system model, a new retrieval model was designed. The functional requirements of the system were summarized, and the three stages of data preprocessing, feature matching, and feature extraction were analyzed in detail. The study adopted preprocessing measures such as equalization and normalization to minimize the negative effects of noise and brightness. Based on the performance of various algorithms, the distance method was selected as the feature matching method, which has a wider applicability and is better at processing bulk data. Next, the study utilizes Euclidean distance method to extract keyframes and divides the feature extraction into three parts: color, shape, and texture. The methods of color moment, canny operator, and grayscale cooccurrence matrix are used to extract them, and ultimately achieve relevant image retrieval. The research conducted multiple experiments on the retrieval performance of the model, and analyzed the results of retrieving single and mixed features. The experimental results showed that the algorithm performed better in the face of mixed feature extraction. Compared with the average value of a single feature, the recall and precision of the three mixed features increased by 13.78% and 15.64%, respectively. Moreover, in the case of a large number of concurrent features, the algorithm also met the basic requirements. When the concurrent number was 100, the average response time of the algorithm is 4.46 seconds. Therefore, the algorithm proposed by the research institute effectively improves the ability of video retrieval and can meet the requirements of timeliness, which can be widely applied in practical applications.

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Section: Extraction Of Texture Features In Video Imagesmentioning

confidence: 99%

Intelligent Traffic Video Retrieval Model based on Image Processing and Feature Extraction Algorithm

Zhao¹,

Wang²

2023

IJACSA

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“…Bayesian optimization is an approach technique for searching for the optimum value of a function by using the probabilistic of the overall search and evaluating the function [24,25], Bayesian will use the theory of Bayesian probability for an iterative model so that it can have the advantage of updating initial knowledge [26,27]. This research can help in improving the model that ignores text or information that has important value in producing a summary.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Text Summarization with a T5 Model and Bayesian Optimization

Lubis,

Safitri,

Irvan

et al. 2023

RIA

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At present the habits and interests of individuals in obtaining information by reading large amounts of information have changed at the stage of reading information more concisely, but these changes have challenges such as the nature of the data which is still unstructured making it difficult to summarize text. This study applies a data cleaning process with text processing and manually annotates to divide the data into summary data and text data so that it can be used for the process of implementing the T5 model and Bayesian optimization. In the implementation of Bayesian optimization using the prior distribution and likelihood parameters. In implementing the T5 model there will be several stages such as processing training and test data then Decodification and Post-Processing processes. The results of this study were obtained using the ROUGE evaluation technique which resulted in an increased evaluation value. The T5 model produces a ROUGE 1 value with an average value of 0.42, ROUGE-2 has a value of 0.55 and ROUGE-L has a value of 0.46 while applying Bayesian optimization produces a ROUGE-1 evaluation with an average value of 0.53 ROUGE-2 has a value of 0.55 and ROUGE-L has a value of 0.59.

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“…[8][9]. Text Clustering adalah salah satu metode yang bertujuan untuk meng-Volume 4, Nomor 1, Desember 2022, Page 40-47 ISSN ISSN 2808-005X Available Online at http://ejournal.sisfokomtek.org/index.php/jumin Rizky Dea Mustika, Copyright © 2022, JUMIN, Page 41 Submitted: 15/11/2022; Accepted: 25/11/2022; Published: 15/12/2022 cluster data yang berupa dokumen text mejadi lebih terstuktur.…”

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Implementasi Algoritma K-Means Untuk Clustering Judul Skripsi Universitas Harapan Medan

Mustika¹,

Zakir

Rizmi

2022

JUMIN

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Clustering merupakan proses analisa informasi yang mana kerap digunakan sebagai salah satu proses untuk Data Mining yang bertujuan untuk mengumpulkan informasi yang memiliki karakter yang sama pada satu kawasan yang sama dan informasi yang memiliki karakter yang berbeda ke kawasan lain. Pada penelitian yang dilakukan hal yang akan menjadi tujuan atau sasaran dalam penlitian ini adalah mengetahui proses clusterisasi judul skripsi di Universitas Harapan Medan mengetahui cara kerja algoritma K-Means dalam melakukan Clusterisasi judul skripsi, menerapkan algoritma K-Means. Adapun rumusan masalah yang dibangun pada penelitian ini bagaimana proses pengelompokkan judul skripsi yang ada di perpustakaan universitas harapan medan dengan menerapkan algoritma K-Means Clustering sehingga akan dapat memudahkan proses pengelompokkan. Aplikasi yang dibangun menggunakan Bahasa pemrograman PHP dan MySQL segabai databasenya. Metode K-Means merupakan sebuah metode yang melakukan proses clustrering untuk satiap judul skripsi yang ada. Penelitian yang dilakukan menciptakan informasi baru yaitu dengan adanya Clustering data dari judul skripsi berdasrakan bidang dari judul itu sendiri dimana setiap hasilnya dapat dilihat di masing-masing cluster datanya. Dari hasil penelitian yang dilakukan maka dapat disimpulkan bahwa algoritma K-Means merupakan algoritma yang mampu mengelompokan beberapa data dengan cepat dan tepat sehingga data data yang memuat judul skripi tersebut dapat dilihat sesuai dengan kelompoknya datanya masing-masing.

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The feature extraction for classifying words on social media with the Naïve Bayes algorithm

Cited by 9 publications

References 23 publications

Intelligent Traffic Video Retrieval Model based on Image Processing and Feature Extraction Algorithm

Intelligent Traffic Video Retrieval Model based on Image Processing and Feature Extraction Algorithm

Enhancing Text Summarization with a T5 Model and Bayesian Optimization

Implementasi Algoritma K-Means Untuk Clustering Judul Skripsi Universitas Harapan Medan

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