Most source code plagiarism detection tools only rely on source code similarity to indicate plagiarism. This can be an issue since not all source code pairs with high similarity are plagiarism. Moreover, the culprits (i.e., the ones who plagiarise) cannot be differentiated from the victims even though they need to be educated further on different ways. This paper proposes a mechanism to generate hints for investigating source code plagiarism and identifying the culprits on in-class individual programming assessment. The hints are collected from the culprits’ copying behaviour during the assessment. According to our evaluation, the hints from source code creation process and seating position are 76.88% and at least 80.87% accurate for indicating plagiarism. Further, the hints from source code creation process can be helpful for indicating the culprits as the culprits’ codes have at least one of our predefined conditions for the copying behaviour.

Final Project Report at a university has the potential for plagiarism. To detect possible plagiarism, String Similarity can be used. Text preprocessing is needed to process words which can make String Similarity results inaccurate. The value of the distribution of the results of the similarity that is getting higher shows the level of accuracy is also getting higher. Reports that contain many words can make it difficult to find plagiarism recommendations. In this study, we try to divide the report into each chapter to provide more detailed recommendation material. By using text preprocessing and comparison methods in the same chapter, can determine the characteristics of each chapter. The discovery of the characteristics of each chapter can be used as plagiarism recommendation material in more detail than a full text report. The experiment was a comparison of the results of cosine similarity between the same chapters and full text, then combined with preprocessing stopword removal and stemming. The experimental results show that the use of preprocessing stopword removal and stemming can produce the highest distribution value and the similarity ratio in each chapter can show its characteristics. Words that represent the characteristics of a chapter can potentially become a stopword.

Along with the advancement in technology, todays community begins to abandon conventional shopping methods where buyers must come to the seller's shop. Nowadays community mostly doing online shopping because the process is considered more convenience. Because of this, there are more and more online marketplace users. Much more data can be retrieved with the increasing number of online marketplace users. Because of the large amount of data the process for extracting the data so that it can be seen and utilized becomes possible. The purpose of this journal is to show data and extraction method from an online marketplace system so that the results can be visualized and users can analyze the data. The data extraction method that will be used is the web crawling method and web scraping where after the data is successfully extracted and cleaned it will be visualized with the power BI application. The experiments show that the method is useful to conduct analysis.