On December 11, 2019, the Minister of Education and Culture of the Republic of Indonesia Nadiem Anwar Makarim issued a policy of "Merdeka Belajar". Netizens on Twitter have debated this Merdeka Belajar and became a trending topic. This study tries to analyze the sentiment of tweets about opinions on this policy by classifying whether it is a positive opinion or a negative opinion. The classification method applied is the K-Nearest Neighbor algorithm. In this study, four main processes were carried out, namely text-preprocessing, word-weighting (TF-IDF), classification and validation using k-fold cross validation. Tests were carried out with a dataset of 700 data, training was carried out using 630 training data and 70 testing data. In testing, the highest accuracy of the K-Nearest Neighbor algorithm was obtained at the k-8 value, namely 84.28%. Furthermore, validation is carried out using k-fold cross validation with a value of fold = 10 to get an accuracy of 84.42%.

Pada web berita yang jadi suatu kabar terpercaya dalam mengenali suatu data, namun terdapat sebagian kekurangan pada berita berbasis website khususnya pada pencarian. Perihal tersebut beberapa kendala yang dihadapi yaitu  lambat sistem dalam membaca dari tiap- tiap kata kunci yang kita cari pada database yang terdapat dalam sistem tersebut. Penelitian ini bertujuan untuk mengimplementasi  Algoritma Brute Force  Pada Pencarian Berita Berbasis Web. Algoritma Brute Force bertujuan pencarian seluruh kemunculan string pendek yaitu pattern di string yang lebih panjang yang di inginkan. Hasil dari penelitian ini implementasi algoitma Brute Force pada website berita bisa menuntaskan permasalahan dalam melaksanakan pencarian informasi berita, sebab algoritma ini menciptakan informasi yang dicari.

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The rapid development of the internet of things (IoT) has taken an important role in daily activities. As it develops, IoT is very vulnerable to attacks and creates IoT for users. Intrusion detection system (IDS) can work efficiently and look for activity in the network. Many data sets have already been collected, however, when dealing with problems involving big data and hight data imbalances. This article proposes, using the dataset used by BotIoT to evaluate the system framework to be created, the XGBoost model to improve the detection performance of all types of attacks, to control unbalanced data using the imbalance ratio of each class weight (CW). The experimental results show that the proposed approach greatly increases the detection rate for infrequent disturbances.

Phobia is a condition in which a person experiences excessive fear of a particular object, giving rise to irrational fears that can threaten personal safety. Based on the data obtained from the Siak District Health Office in conducting immunizations at SDN 003 Benteng Hilir, out of 25 class 1 (one) elementary school students who were immunized, 18 of them suffered from needle phobia (Trypanophobia). Therefore, to overcome this problem, it is necessary to build an application as a medium for the therapy of needle phobia. The application that will be built in this study applies augmented reality technology as a mobile-based therapeutic medium. The approach used is systematic desentisiasi to the stage of flooding, where the medical team will guide patients in conducting therapy which begins with providing information about phobias through the application then directs the mobile to the marker bracelet that has been installed in the patient's hand so that the patient can interact with the syringe object directly. Based on the results of tests conducted in blackbox, it can be concluded that the application of trypanophobia can provide sufficiently clear information to patients and can help the medical team quickly control the patient's fear before injection