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This research is a case study of Search Engine Optimization (SEO) in Palembang Polytechnic of Tourism website. The main objective of this research is to establish a plan for SEO in Palembang Polytechnic of Tourism (http://poltekpar-palembang.ac.id/) and to improve online visibility and ranking position in search engines (Google). It aims to bring in more international traffic and students to visit the website. SEO is a digital marketing technique to increase web accessibility. In the globalization world, people use search engines, such as Google, to know or find out more about various topics quickly and visually. Through a bibliographic review and qualitative analysis, the research focuses on the understanding of what SEO is and its implementation for the Palembang Polytechnic of Tourism website. The results show that the most important thing in making SEO plans is to increase visibility and branding on search engines (Google). SEO is done by developing website content and setting keywords as backlinks.

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Sejarah Artikel:The inability of students to complete their studies on time is faced by most of higher education institution. STMIK Bina Nusantara Jaya Lubuklinggau is one of those which is experienced with this matter. In most cases, the students could complete their studies longer than the expected duration. From 162 students of Sistem Informasi study program in the year 2013 and 2014, there were 117 students completed their studies on time, while 45 students were late. As a result, it could prevent new students from joining the institution since the limited student capacity. This study deploys data mining technique in predicting the graduation status of students on time. First, preprocessing is used to obtain a good dataset. Secondly, the data is processed to obtain a set of prediction. In this step, two mining algorithm were applied -Naive Bayes classifier and C4.5 algorithm to be knowing the performance of the two methods, the method has a greater accuracy value will be recommended to solving the problem of prediction of students graduation at STMIK Bina Nusantara Jaya Lubuklinggau. Thirdly, the result then was validated using K-Fold Cross Validation technique. Finally, the Confusion Matrix is deployed to ensure the accuracy of the prediction. The results indicate that the C4.5 Algorithm method can be used to predict student graduation status with an accuracy rate of 79,08% while the accuracy rate of the Naive Bayes Classifier method is only 78,46%. The dominant factor is IPK-S4 variable.

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