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Many people choose to trade binary options because it is not difficult to understand. They need to predict whether the price will be higher or lower than their open position in a specific timeframe. The most popular timeframe for binary options traders is the M5 (5-minutes). Many traders prefer to use a candlestick chart model to predict. We use SVM (Support Vector Machine) to indicate the next-candlestick based on the previous-candlestick features in this research. This paper uses six features such as Open Value, Close Value, High Value, Low Value, Volume Value, and previous Candlestick Color on each candlestick. The result of the prediction accuracy using SVM is only 56%.

Bicycle is one of the most popular transportation  and in Indonesia there are still many people used it. Static bike or stationary bicycle is one of many kind of bicycle and people used it in indoor room. Static bike has an electronic panel that could help people who used it to know how far the distance, what abaout their speed and how many calories they have burned. Many people said that static bike is more fun and more interesting than outdoor bicycle especially from mountain bike because the static bike has the electronic panel. The electronic panel from static bike will be used on mountain bike so it can be more interresting as static bike. Microcontroller will used as minicomputer to processing all data from magnetic sensor that show the rotation of bike’s tires and then the microcontroller will show the result on seven segment display about the distances, the speed, and the calories burned.

Many researcher said that emotion or mood can be detected from physiological changes like the heartbeat. In order to measure human heart activity, we were using tools like Electrocardiograph. The changes on human emotion or mood affect physiology. This paper is part of our research that talked about Analysis of Mood State from Heart Signal during Playing Flappy Bird. In this paper, we will explain how we design ECG tools that could measure or detect the human heartbeat especially that affect by mood changes. E-Health Sensor Platform v2.0 and Arduino Uno were used to build this system. E-Health Sensor Platform v2.0 contains several sensors that can measure the biological state of humans such as heart rate, breathing, skin conductance and many others. This device can operate when connected with Arduino Uno as a microcontroller. Arduino uno role as a liaison between the Platform and the PC via a serial port. We were using C programming languange on Arduino Uno. 100 Hz were sett in programming code so we can read all data from ECG sensor clearly. To visualize the heartbeat signals we were using KSTPlot. This system has successfully read the heart signals of 20 participants. Although there is signal noise but it does not affect the data. The noise can also be removed with a filter.In our next study, the raw data will be analyzed using the HRV method, but this will be discussed in another paper.

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