Ektraksi fitur dan algoritma klasifikasi teks merupakan bagian penting dari pekerjaan teks, yang memiliki dampak langsung pada efek teks. Algoritma machine learning tradisional seperti Na¨ıve Bayes, Support Vector Machines, Decision Tree, K-Nearest Neighbors, Random Forest, Logistic Regression telah berhasil dalam melakukan dengan ektraksi i.e. Bag ofWord (BoW), Term Frequency-Inverse Document ...

Information retrieval is one of the major research areas due to accumulation of huge information in digital form. Various techniques of Information retrieval are based on the fact that various terms present in a document along with their frequency of occurrence signify the semantics of the document. Recent attempts to find the relevant document for a context represents documents in a Latent Sem...

Although the current transcription systems could achieve high recognition performance, they still have a lot of difficulties to transcribe speech in very noisy environments. The transcription quality has a direct impact on classification tasks using text features. In this paper, we propose to identify themes of telephone conversation services with the classical Term Frequency-Inverse Document F...

This study is an exploratory analysis of applying natural language processing techniques such as Term Frequency-Inverse Document Frequency and Sentiment Analysis on Twitter data. The uniqueness this work established by determining the overall sentiment a politician’s tweets based TF-IDF values terms used in their published tweets. By calculating value from corpus, displays correlation between s...

This paper proposes a graph-based readability assessment method using word coupling. Compared to the state-of-theart methods such as the readability formulae, the word-based and feature-based methods, our method develops a coupled bag-of-words model which combines the merits of word frequencies and text features. Unlike the general bag-of-words model which assumes words are independent, our mod...

Two new methods for retrieving mathematical expressions using conventional keyword search and expression images are presented. An expression-level TF-IDF (term frequency-inverse document frequency) approach is used for keyword search, where queries and indexed expressions are represented by keywords taken from LATEX strings. TF-IDF is computed at the level of individual expressions rather than ...

Categorization of text plays an important role in the text mining field. Text categorization is the process in which documents are categorized into its predefined category. Automatic text categorization is an important task due to large amount of electronic documents. This paper presents a survey of Text categorization of Indian and non-Indian languages. There is very less work done in text cat...

The classification of content on the deep and dark web has been a topic interest for researchers. Researchers focus adopting more efficient effective methods as data available platforms continues to grow. Multi-label is approach simultaneously categorizing into multiple classes. To address this, hybrid combining Term Frequency-Inverse Document Frequency (TF-IDF) Recurrent Neural Network (RNN) p...

In many natural language processing (NLP) tasks, a document is commonly modeled as a bag of words using the term frequencyinverse document frequency (TF-IDF) vector. One major shortcoming of the frequencybased TF-IDF feature vector is that it ignores word orders that carry syntactic and semantic relationships among the words in a document, and they can be important in some NLP tasks such as gen...

Detection of metamorphic malware is a challenging problem as a result of high diversity in the internal code structure between generations. Code morphing/obfuscation when applied, reshapes malware code without compromising the maliciousness. As a result, signature based scanners fail to detect metamorphic malware. Prior research in the domain of metamorphic malware detection utilizes similarity...