Quality of Service (QoS) control is an important concept in computer networking, as it is related to end-user experience. While providing QoS guarantees over the Internet has long been deemed too complicated, the emergence of Software-Defined Networking (SDN), and OpenFlow as its most popular standard, may facilitate QoS control.In this paper, we consider how to enable bandwidth guarantees with OpenFlow. Our design allows QoS flows to send more than their guaranteed rates, as long as they do not hinder other guaranteed and/or best-effort flows.Furthermore, our design uses OpenFlow's meter table to aggregate traffic. Our traffic aggregation functionality only adds overhead to the first switch, but no other complexity is incurred at the subsequent switches.
To invest or buy and sell on the stock exchange requires understanding in the field of data analysis. The movement of the curve in the stock market is very dynamic, so it requires data modeling to predict stock prices in order to get prices with a high degree of accuracy. Machine Learning currently has a good level of accuracy in processing and predicting data. In this study, we modeled data using the Long-Short Term Memory (LSTM) algorithm to predict the stock price of a company called Japfa Comfeed. The main objective of this journal is to analyze the level of accuracy of Machine Learning algorithms in predicting stock price data and to analyze the number of epochs in forming an optimal model. The results of our research show that the LSTM algorithm has a good level of accurate prediction shown in mape values and the data model obtained on variations in epochs values. All optimization models show that the higher the epoch value, the lower the loss value. Adam's Optimization Model is the model with the highest accuracy value of 98.44%.
Perkembangkan teknologi terutama di bidang energi sangat pesat, Energi meter digital yang dikembangkan adalah smart meter. Smart meter mempunyai informasi tambahan seperti besar energi yang terpakai dalam kWh dan harga energi tersebut. Penelitian ini bertujuan untuk merancang jaringan LoRaWAN di sisi coverage untuk mengetahui berapa jumlah gateway yang dibutuhkan dan untuk merancang smart meter berbasis IoT untuk memaksimalkan dan memudahkan masyarakat terutama pada kebutuhan listrik untuk menunjang aktivitas sehari hari. Metode yang digunakan berupa simulasi menggunakan software Atoll versi 3.40 dan beberapa tahapan perhitungan untuk memprediksi kekuatan dan kualitas sinyal di daerah Kabupaten Gresik. Penelitian ini menggunakan fekuensi 920 MHz dengan bandwidth 125 khz dan Spreading factor 1 sampai 12. Hasil yang didapat berupa perbandingan jumlah gateway, kekuatan sinyal dan kualitas sinyal berdasarkan variasi spreading factor. SF 7 menghasikan 77 gateway dengan kekuatan sinyal -69,29 dBm dan kualitas sinyal 8.43 dBm. spreading factor. SF 12 menghasikan 35 gateway dengan kekuatan sinyal -86,08 dBm dan kualitas sinyal 9,04 dBm. Semakin besar SF yang digunakan akan meningkatkan kualitas sinyal tetapi mengurangi kekuatan sinyal dan juga gateway yang lebih sedikit.
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