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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika
Taqa , Alaa Yaseen
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Computer Science & IT

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A proposed approach for plagiarism detection in Article documents Saeed , Ayoub Ali M.; Taqa , Alaa Yaseen
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11381

Abstract

According to the scientific institutes, Plagiarism is defined as claiming someone else's ideas or efforts as one's own without citing the sources. Systems of plagiarism detection typically use a text similarity algorithm in a text document to look for common sentences between source and suspicious documents, either by directly matching the sentences or by embedding the sentences into a vector using TFIDF-like or other methods and then calculating the distance or the similarity between the source and suspect sentence vectors. The cosine similarity method is one of the methods for determining that distance. To cluster the documents and choose only related documents for detection, an unsupervised Machine learning technique such as K-means could be utilized. In this paper, a plagiarism detecting application was created and tested on many text document types, including doc, Docx, and pdf of research papers that were collected from the web to build the source corpus. To calculate the level of similarity between the suspicious article and the corpus of source articles, the TFIDF text encoding approach is used with NLP, K-means clustering, and cosine similarity algorithms. The proposed application was carried out with five different documents and resulted in different ratios of plagiarism, the first document has a 0.27 ratio, the second document has a 0.15 ratio, the third document has 0.19 ratio while document 4 has a 0.42 ratio, and finally, document 5 has 0.37 ratio of plagiarism. The generated detailed plagiarism ratio report presents the percentage of plagiarism in the suspicious article document. Depending on the threshold value, the application will decide if the suspicious document is acceptable or not.
A proposed User-Based Approach for eBooks Recommendation Using a Weighted Nearest Neighbor Technique Saleh, Abdullah Mohammed; Taqa , Alaa Yaseen
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12441

Abstract

Large book data stores were beneficial for our support systems but posed significant challenges for useful information retrieval. This issue was resolved by collaboratively filtering data depending on user needs. This study suggested a user-based methodology for recommending eBooks. The selected dataset was pre-processed, and Cross-validation was used to build a user-user similarity matrix. Three nearest neighbor algorithms (KNN Basic, KNN with Means and KNN with ZScore) were used, and weighted KNN was proposed for rating prediction. In this technique, the weight of each user was calculated based on its distance from the intended user. The evaluation process depends on the user-item matrix and user-user matrix for prediction. The proposed recommendation system was tested on the book-crossing dataset, and the results were evaluated using the root mean square error and the mean absolute value of error. The results show that the error rate of the proposed model is the lowest compared to the other methods used, specifically when using the Pearson-Baseline technique. Since the root mean square error is 1.647 and the mean absolute value of errors is 1.253. When using the cosine technique, the root mean square error is 1.742, and the mean absolute value of errors is 1.328.
Co-Authors Saeed , Ayoub Ali M. Saleh, Abdullah Mohammed