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All Journal JAIS (Journal of Applied Intelligent System) Paradigma
Imam Fahrur Rozi
State Polytechnic of Malang
Computer Science & IT

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Cross-Language Text Document Plagiarism Detection System Using Winnowing Method Mustika Mentari; Imam Fahrur Rozi; Maria Puji Rahayu
Journal of Applied Intelligent System Vol 7, No 1 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i1.5950

Abstract

Currently, there are many text documents such as journals scattered on the internet, both Indonesian and English-language journals. With this, it is possible to act plagiarism by copying from foreign journals that are translated into other languages or copying directly without being changed from the original language. One way that can suppress these actions is to build a plagiarism detection system for cross-language text documents. The method that can be used to detect document plagiarism is the Winnowing method. Winnowing method is a method where text input will be processed to produce a hash value called a fingerprint. This study aims to build a system that can detect plagiarism of text documents in different languages using the Winnowing method. Text documents that can be tested are input text and PDF files. Documents used in system testing are journals that have the same topic. The results of the highest level of accuracy produced between the calculation of the Jaccard Coefficient with the Plagiarism Checker X application are in the fourth scenario with an average percentage value of 84.7%.
Support Vector Machine with FastText Word Embedding for Hate Speech Aspect Categorization Aida Milati Mardiana; Imam Fahrur Rozi; Rudy Arianto
Paradigma - Jurnal Komputer dan Informatika Vol. 27 No. 2 (2025): September 2025 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v27i2.5127

Abstract

Freedom of expression on Twitter often leads to issues such as hate speech, which may include provocation, incitement, or insults based on race, religion, gender, and other aspects. To address this issue, machine learning techniques can be applied to automatically classify hate speech. Therefore, this study aims to implement a machine learning–based approach for automatic hate speech aspect classification and to evaluate the accuracy of the obtained results. Support Vector Machine is used as the classifier method, with FastText as the word embedding method in the categorization process of hate speech aspects. The categorized aspects include abusive, individual, group, religion, race, physical, gender and other. The dataset used in this research is a collection of Indonesian tweets from Kaggle, which have been classified into each aspect. This study also tested combinations of preprocessing methods, namely filtering with stemming and the FastText pre-trained model. From the test results of the application of the Support Vector Machine method with FastText word embedding, with parameters C value = 1.0, gamma = 1.0 and RBF kernel and the ratio between training data and testing data is 90:10, the best results were obtained accuracy 98%, precision 98%, recall 98% and F1-Score 97% on Physical and Gender aspects. In addition, this study also tested if it did not use fasttext word embedding and the accuracy results showed 84%, precision 74%, recall 86% and F1 Score 79% in the abbusive aspect.
Co-Authors Aida Milati Mardiana Maria Puji Rahayu Mustika Mentari Rudy Arianto