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.

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.

STMIK Bina Nusantara Jaya Lubuklinggau menawarkan beasiswa Penelusuran Minat dan Kemampuan (PMDK) kepada calon mahasiswa baru, dalam hal ini untuk melakukan proses seleksi penentuan penerima beasiswa PMDK pengolahan data masih dilakukan secara manual dengan melakukan tes wawancara dan melakukan perhitungan hasil dari wawancara. Proses seleksi yang dilakukan secara manual memiliki beberapa kelemahan sehingga besar kemungkinan akan terjadinya kesalahan dalam pengolahan data. Untuk Itu diperlukannya suatu Sistem Pendukung Keputusan yang dapat mempermudah dalam penentuan penerima beasiswa PMDK. Pada penelitian ini menggunakan analisa perhitungan komparasi metode WP, SAW dan WASPAS dalam penentuan penerima beasiswa penelusuran minat dan kemampuan (PMDK). Sistem dibuat menggunakan metode SAW karena memberikan nilai Alternatif tertinggi dan memberikan hasil perangkingan yang terbaik.

STMIK Bina Nusantara Jaya Lubuklinggau menawarkan beasiswa Penelusuran Minat dan Kemampuan (PMDK) kepada calon mahasiswa baru, dalam hal  ini  untuk melakukan proses seleksi penentuan penerima beasiswa PMDK pengolahan data masih dilakukan secara manual dengan  melakukan  tes  wawancara  dan  melakukan  perhitungan hasil  dari  wawancara.  Proses seleksi yang dilakukan secara manual memiliki beberapa kelemahan sehingga besar kemungkinan akan terjadinya kesalahan dalam pengolahan data. Untuk Itu diperlukannya suatu Sistem Pendukung Keputusan yang dapat mempermudah dalam penentuan penerima beasiswa PMDK. Pada penelitian ini menggunakan analisa perhitungan komparasi metode WP, SAW dan WASPAS dalam penentuan penerima beasiswa penelusuran minat dan kemampuan (PMDK). Sistem dibuat menggunakan metode SAW karena memberikan nilai Alternatif tertinggi dan memberikan hasil perangkingan yang terbaik.

Information technology has become an essential part of human life so as to facilitate a business activity. However, the use of information technology is not separated from the risks that can affect the process of the activity. As for the purpose of this study was to conduct an assessment of risk against potential vulnerabilities and threats that can attack the academic information system E-University all at once mempersiapan action anticipation towards things that can interfere with the the system. To do the assessment, this study uses the framework NIST SP 800-30r-1 consisting of nine stages to risk assessment i.e. in the characteristics of the system are used, identify the threats that attack system, identification of vulnerability, control systems, determine the likelihood of occurring (likelihood), determine the impact (impact), the determination of risks, control recommendations and documentation of results. The results of the risk assessment against the academic information system E-University is there are three risks disrupting existing activities in the system. Then from the results of the assessment of risks in the form of recommendations are used to minimize the risks that occur on the system

The face or the front of the head consists of the eyes, nose and mouth. Each face has its uniqueness from the many human faces. The face is used to show happy and sad expressions and feelings. Smiling also includes self-expression from others. The analysis of facial expressions plays a key role in analyzing human emotions and behavior. Smile detection is a specialized task in facial expression analysis with a variety of potential applications such as photo selection, user experience analysis, smile payments, and patient monitoring. Conventional approaches often extract low-level face descriptors and smile detection based on strong binary classifiers. In this paper, we propose an effective Histogram of Oriented method supporting vector engines for smile detection. The experimental results show that our proposed network outperforms the current state of the method. The test results using the Histogram of Oriented Gradient and Support Vector Machine method for smile detection were 87% for a precision value of 88% and a recall value of 83% and accuracy. In the future, we want to exploit some of the latest effective designs. we will try to update our mouth model so we can support a bigger head turn and face size scale.

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