In education field, evaluation is needed to know the extent to which the learning process has been done. The evaluation process can be done through the provision of questions with varying degrees of difficulty. However, making questions with varying degrees of difficulty is not easy. Someone must understand the whole new materials to make the question. If there are a lot of materials, it takes a little time to change them to be a question. Therefore, it is necessary to automate the question generation process, in order to facilitate and accelerate the question generation process. This research introduces a template-based method to generate questions based on New Bloom's Taxonomy. There were 4 stages in this research, dataset collection, pattern identification process, question generating process & classification, and final evaluation process result. The dataset consists of 60 samples of paragraphs that derived from 9 courses of study courses Informatics Engineering. The 60 paragraphs produced 278 sentences and 654 questions. The proposed method is capable of producing an accuracy of 81.65% to generate questions using New Bloom's Taxonomy classification. So it can be concluded that the proposed method can be used to generate questions with varying difficulty levels in accordance with New Bloom's Taxonomy.
The implementation of network security infrastructure has been carried out, including the Intrusion Detection System (IDS). However, in its implementation there are still many who have not combined with Data Technology (Data Science) to get a more comprehensive analysis. This study aims to analyze the types and characteristics of network threats using data science. As a computational method, the results of 3 algorithms in the unsupervised learning category will be implemented and compared, namely K-Means, Meanshift, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). From the experimental results as measured by the Silhouette Index (SI ) the best cluster of each implemented algorithm is DBSCAN which has the best SI value of 0.3424 with an Eps value of 0.2 and a MinPts value of 3. Meanwhile, from the results of clustering using K-Means, The best SI value was obtained by experiment k=4 with a value of 0.4531. The results of clustering using MeanShift, the best SI value was obtained by experiment bandwidth = 1 with a value of 0.5305.
The implementation of eco-friendly technology has been become an interesting field for sustainability. No exception with the implementation of wireless technology that used for developing networks infrastructure, it is necessary for saving the usage of energy. As a data forwarding protocol in a computer network, commonly there are two protocols that used for, which are routing and bridging protocol. Technically routing protocol has been confirmed that it is more effective and efficient than bridging protocol. However bridging protocol still becomes the popular protocol for data forwarding because it is easy to use. This research tried to test the energy consumption of the wireless network device that implementing between routing or bridging protocol. The wireless network device that used for this research was MikroTik router RB 433Ah. The data forwarding protocol that was tested consists of bridging, static routing, and RIP routing. Data traffic scenario that used for this research consisted of two scenarios which were HTML data access with packet size 256B and video streaming data access with packet size 1518B. Measuring the energy consumption referred to three parameters which were power consumption, CPU usage, and processor temperature. The result showed that for HTML data access scenario, the RIP routing protocol become the lowest energy consumption with power consumption reached 7.460 W, CPU usage 4.6 %, and processor temperature 38.133^C. While for video streaming scenario, generally the RIP routing protocol still become the lowest energy consumption with power consumption reached 7.567 W, CPU usage 7.33 %, and processor temperature 36.727^C. IntroductionWireless networks are the most considered choice today to build infrastructure computer networks, especially for wide coverage areas. This is because wireless networks allow users to connect the networks flexibly without being limited by hardware like the limitation of conventional wired computer networks. Finally, wireless networks infrastructure is be able to cover almost every places even remote places. There has been increased the use of mobile devices, hotspot areas, and wireless IoT globally [1]. Even wireless infrastructure like telecom tower (BTS), has grown significantly. In 2018, there are 118 thousand towers that cover mostly 95% area of Indonesia for serving 157 million customers [2]. Actually, mobile device that implements wireless technology infrastructure gives energy efficiency, but nowadays the increasing of data usage makes energy saving in vain [3]. It is no doubt that the increase of internet conection and mobile phones impacts the electricity consumption especially in developing country [4].In other hand, the development of wireless networks as information and communication technology (ICT) certainly raises an effect [5], [6]. Theoretically, information and communication technology has a positive and significant relation with energy consumption [7], no exception with wireless technology. Wireless technology is one of the developed techn...
Skin disease can be suffered by all people and ages as a common disease. In general treatment for people with skin diseases is by consulting to medical experts and asking for a medicine prescription. However, by utilizing current communication technology, sufferers are able to get good information about skin diseases. The expert system for the diagnosis of skin diseases has been done before. However, the expert system is built based on web and desktop, so it is less flexible in use, moreover in supporting self assistance for the skin disease sufferer. Therefore, this study aims to develop an Android-based expert system that can help diagnose skin diseases in humans as well as provide recommendations of generic medicine as first aid. The expert system developed by implementing the Forward Chaining (FC) method as an inference technique. This research was conducted in five phases, namely data collection, analysis system, design system, implementation, and testing. The knowledge base in this expert system includes 39 types of skin diseases, 83 symptoms, 16 types of generic drugs, 150 lines rules of skin disease diagnosis, and 39 lines recommendations of generic drug.
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