Kehadiran siswa di kelas merupakan hal penting saat kegiatan belajar mengajar di laksanakan. Sistem presensi yang masih menggunakan cara manual dengan memakai kertas. Memiliki permasalahan yang sering muncul antara lain: terjadinya manipulasi data kehadiran, hilangnya buku presensi, sulit dalam merekapitulasi data kehadiran. Sistem presensi dengan pengenalan wajah digunakan karena hanya memerlukan kamera untuk mengambil citra gambar. Dengan mengotomatiskan proses kehadiran akan lebih meningkatkan produktivitas guru kepada siswanya. Metode haar cascade classifier digunakan karena memiliki komputasi yang sangat cepat tergantung pada jumlah piksel dalam persegi bukan nilai piksel dari sebuah gambar. Dari hasil pendeksian wajah menggunakan metode haar cascade classifier prosentase yang telah dicapai sebesar 75%. Seluruh sistem terbukti dapat berjalan dengan baik dalam mendeteksi seluruh objek yang ada secara tepat. Sistem memudahkan dalam memantau kehadiran siswadi kelas secara akurat, efisien serta menghemat waktu serta tenaga

<p>Information books in the library will provide a special identity on any book title. Identity of the books stored in the library will allow the borrower book knows the book title, author, ISBN number, number of shelves where the books are stored and the number of books available in the library. Identity can be summarized in a QR Code.</p><p>With the QR Code on the books, the user can obtain information about a book without having to connect to the database, simply scanning the QR Code Reader. To create a QR Code requires an application generator. In this study, the authors will develop case studies QR Code Generator library FTIK University of Semarang. QR Code Generator will convert alphanumeric data from a book into a two-dimensional image. Which will be attached to each book in the library.<em></em></p>

The Hiposentrum or epicentre is the source of an earthquake which is at a certain depth on earth. The classification of earthquake powers based on the depth of Hiposentrum needed to examine the potential earthquake powers spread in Indonesian territory. The results of the classification process often experience problems, namely inaccuracy in classification. To solve that problem, then algorithms optimising classification must be increased. This research uses the Naïve Bayes algorithm, which is optimized using the Adaboost algorithm. Evaluation of the results of the optimized classification algorithm is needed to determine the level of accuracy using prescriptions and recall. In this study, the object of research is earthquake data in Indonesia which will be used as training data and testing data. The average accuracy of the Naïve Bayes algorithm is 72.3%, and the Naïve Bayes and Adaboost algorithm is 85.3%.

Pemakaian internet merupakan kebutuhan yang penting yang mendukung kinerja dan aktivitas di kampus. Bagian yang terpenting dari infrastrutur internet yang difasilitasi oleh kampus adalah tersedianya bandwidth yang cukup untuk kelancaran trafik data melalui internet. Metode klasifikasi menggunakan Bayes Network ini memanfaatkan metode klasifikasi yang dimiliki oleh data mining untuk diterapkan pada data trafik jaringan internet. Penelitian ini bertujuan untuk mengklasifikasi data pemakaian internet sehingga dari klasifikasi tersebut dapat diketahui destination network, protocol dan lebar bandwidth yang banyak diakses pada waktu tertentu. Data trafik internet diambil melalui software Wireshark. Sedangkan pengolahan data dan proses pengklasifikasian data trafik internet diolah dengan Weka.

Logo is a graphical symbol that is the identity of an organization, institution, or company. Logo is generally used to introduce to the public the existence of an organization, institution, or company. Through the existence of an agency logo can be seen by the public. Feature recognition is one of the processes that exist within an augmented reality system. One of uses augmented reality is able to recognize the identity of the logo through a camera.The first step to make a process of feature recognition is through the corner detection. Incorporation of several method such as FAST, SURF, and FLANN TREE for the feature detection process based corner detection feature matching up process, will have the better ability to detect the presence of a logo. Additionally when running the feature extraction process there are several issues that arise as scale invariant feature and rotation invariant feature. In this study the research object in the form of logo to the priority to make the process of feature recognition. FAST, SURF, and FLANN TREE method will detection logo with scale invariant feature and rotation invariant feature conditions. Obtained from this study will demonstration the accuracy from FAST, SURF, and FLANN TREE methods to solve the scale invariant and rotation invariant feature problems.