Libraries have the main task in the processing of library materials by classifying books according to certain ways. Dewey Decimal Classification (DDC) is the method most commonly used in the world to determine book classification (labeling) in libraries. The advantages of this DDC method are universal and more systematic. However, this method is less efficient considering the large number of books that must be classified in a library, as well as labeling that must follow label updates on the DDC. An automatic classification system will be the perfect solution to this problem. Automatic classification can be done by applying the text mining method. In this study, searching for words in the book title was carried out with N-Gram (Unigram, Bigram, Trigram) as a feature generation. The features that have been raised are then selected for features. The process of book title classification is carried out using the Naïve Bayes Multinomial algorithm. This study examines the effect of Unigram, Bigram, Trigram on the classification of book titles using the feature extraction and selection feature on Multinomial Naïve Bayes algorithm. The test results show Unigram has the highest accuracy value of 74.4%.

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Tertutupnya akan informasi mengenai pencak silat akhirnya membuat pencak silat sebagai warisan budaya bangsa Indonesia hilang tergerus perkembangan jaman dengan adanya teknologi yang semakin modern seperti saat ini. Biasanya pendaftaran pada perguruan pencak silat Budi Asih masih menggunakan cara konvensional, yaitu anggota baru harus memperoleh formulir pendaftaran, mengisi data, dan menyerahkannya ke Guru kolatnya atau Sekretariat. Masalah pendaftaran dan edukasi bisa dilakukan dengan solusi aplikasi pencak silat Budi Asih berbasis website sehingga prosesnya menjadi lebih cepat jika dibandingkan dengan cara konvensional. Hasil dari pengujian yang dilakukan oleh pengguna untuk mencoba aplikasi ini menunjukkan bahwa dapat mempercepat kerja pengurus secretariat dalam mengolah data secara terkomputerisas

<p class="Abstrak"><em>Web server</em> bertugas menjalankan aplikasi <em>web</em> untuk melayani <em>request</em> dari klien. Setiap interaksi yang dilakukan klien terhadap aplikasi <em>web</em>, tercatat pada catatan <em>log server</em>. Dari <em>log</em> tersebut, terdapat data detail tentang alamat IP, perangkat dan sumber klien, <em>request</em> pengguna, respon <em>server</em>, dan keterangan lainnya. Dari informasi pada <em>log</em>, dapat dipakai untuk keperluan pengamanan sistem, salah satunya dengan cara melakukan analisis menggunakan <em>data mining</em> terhadap aktifitas klien yang tercatat pada <em>log server</em>. Selain itu, jika terdapat <em>file</em> yang diunggah pengguna, dapat juga dikaitkan dalam analisis <em>log server</em> dalam mengenali pola aktifitas dan <em>malicious file</em>. Dataset log yang telah didapat, diolah dengan menggunakan pelabelan <em>rule-based </em>yang nantinya diuji dengan pemodelan <em>Support Vector Machine </em>berbasis <em>Linear Discriminant Analysis</em>. Proses mengklasifikasikan data <em>log server</em> dapat dilakukan untuk mengenali aktifitas yang termasuk serangan, usaha paksa untuk masuk sistem terhadap server atau bukan. Dari pemodelan yang dilakukan, didapat hasil bahwa algoritma SVM berbasis LDA memiliki tingkat akurasi <em>training </em>90,2%<em> </em>dan <em>testing </em>89,9% dalam melakukan pengujian <em>rule-based </em>untuk pelabelan aktifitas pada <em>web server</em>.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p><em>The web server is in charge of running web applications to serve requests from clients. Every interaction the client makes to the web application is logged in server logs. From these logs, there are detailed data about IP addresses, client devices and sources, user requests, server responses, and other information. From the information in the logs, it can be used for system security purposes, one of which is by performing analysis using data mining of client activities recorded on the server log. In addition, if there is a file uploaded by a user, it can also be linked in server log analysis in recognizing activity patterns and malicious files. The log dataset that has been obtained is processed using rule-based labeling which will later be tested with a Linear Discriminant Analysis-based Support Vector Machine modeling. The process of classifying server log data can be done to identify activities that include attacks, forced attempts to enter the system against the server or not. From the modeling, the results show that the LDA-based SVM algorithm has a training accuracy rate of 90,2% and testing 89,9% in performing rule-based testing for activity labeling on the web server.</em></p><p><em> </em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

Nowadays, real-time communication (RTC) to become the trend for smart technology systems in some fields. The connection between hosts by RTC has a peer-to-peer (p2p) model, it's not the client-server model, so p2p needs connectivity establishment on every peer. In this paper, we will examine how the effect of the Interactive Connectivity Establishment (ICE) Protocol to build Web Real-time Communication (WebRTC) systems by NAT Traversal, such as Session Traversal Utility for Network Address Translation (STUN). In this study, we use manual signaling to interact and build connections with two peers, using the Whatsapp application to exchange session descriptions. The results of implementation in our method are that using RTC needs STUN as a Traversal of NAT for building p2p or real-time communication. Our recommendation is that actually establishing real-time communication using STUN is not always successful, so we need to test with other protocols such as Traversal Using Relay for NAT (TURN). a https://orcid.