ANALISA INTERFERENSI ELEKTROMAGNETIK PADA PROPAGASI WI-FI INDOOR

Information and communication technologies evolve rapidly. One is with Wi-Fi technology, particularly Internet technology developments that are accessed Wireless (Wireless network). Designed for IEEE 802.11b to cover large area, up to a diameter of 100 meters, and connect 100 of computers that operate on four data rate Different ie 1, 2; 5.5; and 11 Mbps. This condition is risky interference when Wi-Fi technology and Bluetooth are used concurrently. This is because both technologies are equally operate in 2.4 GHz frequency band. At the end of this project has been carried out measurements to see the influence of interference Bluetooth against several parameters Wi-Fi IEEE 802.11b which is indicated by throughput based on the changes data rate. Based on the throughput can be obtained value delay and jitter. The results obtained were the presence of Bluetooth Wi-fi influence is expressed by throughput which influence the throughput is the throughput value decreases with increasing distance between the transmitter (Tx) and receiver (Rx) is the smallest value of throughput on the conditions Non Line Of Sight (NLOS) with Bluetooth 0.5 meters presence of interference against receiver laptop is 0.262 Mbps, and the biggest throughput value when the condition Line Of Sight (LOS) without the bluetooth is 6.107 Mbps. Throughput values obtained from the jitter and delay and delay jitter is the largest in NLOS conditions when there is interference at a distance of 0.5 meters bluetooth to laptop recipients are 3.0534 s and 2.7481 s. Keyword: wireless, QCheck, throughput, Bluetooth, jitter, dela

Student Behavior Analysis to Predict Learning Styles Based Felder Silverman Model Using Ensemble Tree Method

Learning styles are very important to know so that students can learn effectively. By understanding the learning style, students will learn about their needs in the learning process. One of the famous learning management systems is called Moodle. Moodle can catch student experiences and behaviors while learning and store all student activities in the Moodle Log. There is a fundamental issue in e-learning where not all students have the same degree of comprehension. Therefore, in some cases of learning in E-Learning, students tend to leave the classroom and lack activeness in the classroom. In order to solve these problems, we have to know students' preferences in the learning process by understanding each student's learning style. To find out the appropriate student learning style, it is necessary to analyze student behavior based on the frequency of visits when accessing Moodle E-learning and fill out the Index Learning Style (ILS) questionnaire. The Felder Silverman model's learning style classifies it into four dimensions: Input, Processing, Perception, and Understanding. We propose a learning style prediction model using the Ensemble Tree method, namely Bagging and Boosting-Gradient Boosted Tree. Afterwards, we evaluate the classification results using Stratified Cross Validation and measure the performance using accuracy. The results showed that the Ensemble Tree method's classification efficiency has higher accuracy than a single tree classification model