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The Repository Mahasiswa (RAMA) is a national repository of research reports in the form of final assignments, student projects, theses, dissertations, and research reports of lecturers or researchers that have not yet been published in journals, conferences, or integrated books from the scientific repository of universities and research institutes in Indonesia. The increasing popularity of the RAMA Repository leads to security issues, including the two most widespread, vulnerable attacks i.e., Structured Query Language (SQL) injection and cross-site scripting (XSS) attacks. An attacker gaining access to data and performing unauthorized data modifications is extremely dangerous. This paper aims to provide an attack detection system for securing the repository portal from the abovementioned attacks. The proposed system combines a Long Short-Term Memory and Principal Component Analysis (LSTM-PCA) model as a classifier. This model can effectively solve the vanishing gradient problem caused by excessive positive samples. The experiment results show that the proposed system achieves an accuracy of 96.85% using an 80%:20% ratio of training data and testing data. The rationale for this best achievement is that the LSTM's Forget Gate works very well as the PCA supplies only selected features that are significantly relevant to the attacks' patterns. The Forget Gate in LSTM is responsible for deciding which information should be kept for computing the cell state and which one is not relevant and can be discarded. In addition, the LSTM's Input Gate assists in finding out crucial information and stores specific relevant data in the memory. [ABSTRACT FROM AUTHOR]

There are many free instant messengers available now which allow people to communicate with friends with texts, phone calls, videos, by sharing files, in group or not, and to maintain contact with them even internationally. But only very few of instant messengers have gained popularity and recognition. Recent studies have shown that the most popular instant messengers are WhatsApp, Viber and Telegram. Facebook acquired WhatsApp due to its huge number of users. Viber is another messenger with many integrated features that allow for phone calls and send text messages for free, and like WhatsApp there is no subscription, while Telegram offers users a completely free open-source platform without any ads, a clean interface, and (the biggest selling point) security. So, which instant messenger is best? Currently Telegram is the most downloaded messaging apps in the Google Play Store. But at the moment WhatsApp is still the winner!. [ABSTRACT FROM AUTHOR]

Heart diseases are among the nation's leading couse of mortality and moribidity. Data mining teqniques can predict the likelihood of patients getting a heart disease. The purpose of this study is comparison of different data mining algorithm on prediction of heart diseases. This work applied and compared data mining techniques to predict the risk of heart diseases. After feature analysis, models by five algorithms including C5.0, Neural Network, Support Vector Machine (SVM), K-Nearest Neighborhood (KNN) and Logistic Regression, developed and validated. C5.0 Decision tree has been able to build a model with greatest accuracy 93.02%, KNN, SVM, Neural Network have been 88.37%, 86.05% and 80.23% respectively. Produced results of decision tree can be simply interpretable and applicable; their rules can be understood easily by different clinical practitioner. [ABSTRACT FROM AUTHOR]