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Plagiarism occurs because of the easy distribution of data. Plagiarism detection of documents such as student assignments and final projects requires a long process, often overlooked. However, to avoid plagiarism, a document must be checked for the level of plagiarism. Plagiarism detection can be done online / offline with the plagiarism checker. However, checking documents with plagiarism checkers such as Turnitin, Dupli Checker, Copyleaks, PaperRater, Grammarly and others requires additional fees. Several studies have been conducted to detect plagiarism. BM25 and Rabin Karp are examples of the Plagiarism Checker method. BM25 is tfidf based, while Rabin Karp is Hashing based. Each method needs to know its performance to detect plagiarism. Based on these problems, a study on the comparison of plagiarism detection with the BM25 algorithm with Rabin-Karp will be conducted. The case study is to use the article in Indonesian. The application of the BM25 and Rabin Karp algorithms goes through the Pre-Processing stage which consists of case folding, cleaning, tokenizing, filtering, and stemming. In this study, Sastrawi stemmer was used in this study. The test was conducted on twenty articles in Indonesian. The test results that are seen are the performance in the form of execution time.

Agriculture is very important for the welfare of the nation. However, accurate and up-to-date agriculture data in the Buleleng regency are difficult to find. This study was aimed at making a developmental design of Web-based Buleleng regency agriculture product information system and implementing it to support the region’s best products-based regional people economy. The development of this system used the Software Development Life Cycle (SDLC) method with Waterfall model that followed five stages of activities, namely (a) requirements definition, (b) system, and software design, (c) implementation and unit testing, (d) integration and testing, and (e) operation and maintenance. The data needed in the system was obtained by working together with the Buleleng regency agricultural office. Later this system will involve some users with different accessing rights, namely field agriculture extension agents who manage product data, the workers of the agriculture office as administrators of the system, the general public, and consumers who probably come from hotels, restaurants, supermarkets, etc that become the markets of the agriculture products in Buleleng regency. Consumers can order the products. However, this system does not handle online payments. It is expected that with this system Buleleng regency will have accurate and up-to-date data related to agriculture products that are available in Buleleng. In addition, this system can be used as a model for other regencies in Bali and other regions in Indonesia. With the availability of this system in the future, the farmers as producers will get good prices for their products and the consumers will get products of good quality at reasonable prices.

The importance of data to help the management of educational institutions in decision making is recognized by all parties to improve the quality and competitiveness of their alumnus in society. However, to process aBig Data, facilities and resource capabilities are needed to convert the data into information that can be easily visualized and consumed by various interested parties. As the inefficiency of the old technology use in helping the businesses’ needs, the infrastructure of the big data is important. Big data presents various challenges in infrastructure provision and resource constraints as well as privacy and security concerns. This paper provides an initial development of how to build Big Data infrastructure in an educational institution that is adaptable to existing conditions which provide considerable performance and capabilities to perform information mining to assist in decision making. The scenarios, in this case, provide valuable additional insights for organizations to apply to their environment and can be leveraged to reach critical decision making through data visualization.

The study period of students who pass the time limit and high numbers of dropout in a college can affect the value of campus accreditation. The anticipation of that possibility, the college must make predictions about potential students don’t graduate on time. This study aims to build a system capable of predicting students who have the potential. If students with unpredictable graduation risks can be identified in the early stages, then the indication of dropout rates may be reduced by providing special appeals to students at risk. Prediction analysis applies the K-Nearest Neighbors method to dig up the trace data stack and look for the proximity of the data with the new data. The test data used student class of 2011 with 100 students as sample data. This method of classification is based on several attributes, namely the evaluation of the 1st semester to semester 6th, the number of GPA, credits that have been taken each semester, number of credits passed, and the number of credits that didn’t pass. The result of classification becomes the output of the system which is then entered into the testing phase. This stage compares the output with the original data with 70, 73% accuration result.