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Security becomes one of the major necessities in our lives nowadays however criminal activities are still at large with criminals unable to be persecuted without eligible proofs of their misdeeds. Surveillance Camera is one of the better solutions to these problems in which they can be positioned at every corner of a building even streets and alleys. Their functions can be enhanced by adding algorithms that can identify objects. Frame Differences method is an algorithm to identify an object’s motion. Using this algorithm, we could differentiate an object moving in the environment. Background subtraction is one of the methods suitable to further improve frame differences thus increasing its effectiveness and precision. After implementing the method on a camera, the luminosity was founded to influence the threshold value significantly, the threshold value of 35 is the optimal value.

Abstract. In a large scale network need a routing that can handle a lot number of users, one of the solutions to cope with large scale network is by using a routing protocol, There are 2 types of routing protocol that is static and dynamic, Static routing is manually route input based on network admin, while dynamic routing is automatically route input formed based on existing network. Dynamic routing is efficient used to network extensively because of the input of route automatic formed, Routing Information Protocol (RIP) is one of dynamic routing that uses the bellman-ford algorithm where this algorithm will search for the best path that traversed the network by leveraging the value of each link, so with the bellman-ford algorithm owned by RIP can optimize existing networks.

The success of the work of Generative Adversarial Networks (GAN) has recently achieved great success in many fields, such as stock market prediction, portfolio optimization, financial information processing and trading execution strategies, because the GAN model generates seemingly realistic data with models generator and discriminator .Planning for drug needs that are not optimal will have an impact on hospital services and economics, so it requires a reliable and accurate prediction model with the aim of minimizing the occurrence of shortages and excess stock, In this paper, we propose the GAN architecture to estimate the amount of drug sales in the next one week by using the drug usage data for the last four years (2015-2018) for training, while testing using data running in 2019 year , the classification results will be evaluated by Actual data uses indicators of Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). From the results of the experiment, seen from the value ​​of MAE, RMSE and MAPE, the proposed model has promising performance, but it still needs to be developed to explore ways to extract factors that are more valuable and influential in the trend disease progression, thus helping in the selection of optimal drugs

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The development trend of the coronavirus pandemic (COVID-19) in various countries has become a global threat, including in Southeast Asia, such as Indonesia, the Philippines, Brunei, Malaysia, and Singapore. In this paper, we propose an Exploratory Data Analysis (EDA) model approach and a time series forecasting model using the Prophet method to predict the number of confirmed cases and cases of death in Indonesia in the next thirty days. We apply the EDA model to visualize and provide an understanding of this pandemic outbreak in various countries, especially in Indonesia. We present the trends in the spread of epidemics from the countries of China from which the virus originates, then mark the top ten countries and their development and also present the trends in Asian countries. We present an analytical framework comparing the predicted results with the actual data evaluated using the MAPE and MAE models, where the prophet algorithm produces good performance based on the evaluation results, the relative error rate of our estimate (MAPE) is around 6.52%, and the model average false 52.7% (MAE) for confirmed cases, while case mortality was 1.3% for the MAPE and MAE models around 236.6%. The results of the analysis can be used as a reference for the Indonesian government in making decisions to prevent its spread in order to avoid an increase in the number of deaths

Klasifikasi association rule merupakan salah satu teknik dalam data mining yang digunakan dalam penelitian ini untuk mengolah data pengunjung dalam objek wisata. Pada penelitian ini untuk mendapatkan pola/rule pengunjung wisata aplikasi bantu yang digunakan adalah weka,  Associatiation  rule adalah data mining yang berguna untuk menemukan suatu korelasi atau pola yang terpenting/menarik dari sekumpulan data besar. Algoritma Apriori adalah salah satu algoritma yang melakukan pencarian frequent itemset dengan menggunakan teknik association rule, dengan menggunakan algoritma apriori dapat menghasilkan pola pengunjung dari tanah 2015 dan 2016 pada objek wisata kabupaten karo, dengan algoritma Apriori dapat disimpulkan bahwa pada tahun 2015 jumlah pengunjung lebih sedikit. Pada penelitian ini data yang digunakan sebanyak 122 data jumlah pengunjung bulanan pada pariwisata dari tahun 2015 hingga 2016. Hasil pengujian menunjukkan bahwa nilai confiden yang paling tinggi mencapai 0,92.