C4.5 Algorithm to Predict the Impact of the Earthquake

One of the impacts of the quake was heavily damaged, the even tsunami killed at no less. One cause many deaths is because many can not predict the impact of earthquakes. Data earthquakes that occurred earlier can be used to predict the incidence of the quake will probably happen someday. One algorithm that can be used to predict is the algorithm C4.5. The results of the algorithm C4.5 decision tree form, decision trees characteristic or condition of the earthquake and the decision, where the decision is a fruit of the earthquake that occurred modeling Keywords—Earthquake; Impact; The Algorithm C4.5


INTRODUCTION
Earthquakes often cause massive damage, and human casualties are not small, one reason is that many people can not predict the incidence of the quake which occurred mainly in the earthquake-ravaged region.
The earthquake can not predict when it would happen, but the expected impact of the quake based on seismic data that never happened before [1] [2]. One of the methods used to dig or search for information on old data is data mining algorithm C4.5. The output of the algorithm C4.5 in predicting the impact of the quake is divided into three parts [3] [4]. Namely, there is no impact / minor damage, severe damage, and the damage and tsunami. With predictions of the implications of the earthquake is expected to be minimized as a result of the quake victims.

A. Earthquake
An earthquake is a vibration or shock caused by the release of energy from the earth suddenly and creates seismic waves. Usually, earthquakes caused by the movement of the earth's crust or plates. Several theories have been making the quake is the collapse of caverns below the surface of the Earth, meteor impact on Earth's surface, due to the volcanic eruption magma activity that occurred before the volcanoes and tectonic activity. Damage caused by earthquakes is death and disability living beings, and the environmental damage and the collapse of the construction of buildings and tsunami waves [5].
B. Algorithms C4. 5 The c4.5 algorithm is one of the data mining algorithms that included in the classification groups. C4.5 algorithms are used to form a decision tree. The resulting decision tree is the result of the algorithm C4.5 and can represent and model the results of the exploration of significant data, so the knowledge or information from these data more easily identified [6] [7].

III. ANALYSIS AND DISCUSSION
To predict the impact of earthquakes with C4.5 algorithm then takes the old data of the earthquake never happened before. Below are the seismic data that never happened [9]  Attributes distance from shore, depth, scale and duration molded into the form of the categories of data, based on the value of each attribute.     The next step is to calculate the number of cases(S), the number of declared cases of non-effect(S1), the number of cases for decision broken(S2) and the number of cases reported broken and tsunami(S3). After that calculating the gain for each attribute. The results show in the following table. From the Table VII, the calculation could see that the highest attribute is a scale that is equal to 0.892271866. Thus the scale can be the root node. There is three attributes value, low, medium and high. Due to Low Entropy value of 0 means, the case has classified into (S1) indicates the decision to no effect. While Medium and High does not have decision-making needs to be calculated again.  Table VIII is the highest gain value with the value 0.918295834 duration, the duration becomes a branch node of the medium. The duration has two branches, namely short and long, the two branches already have a decision for entropy value of 0, as shown below  From Table IX, the highest value gain distance from the beach is 0.610073065, the distance from the coast to the high scale branch node. Distance from the beach is owned by the three branches namely No. and very much with entropy values 0, during the length because the decision did not have entropy value is not 0, then continued the following process. To search for a branch node of the from calculation table X, like the following. The decision tree above is the product of the algorithm C4.5. A decision tree can be used to predict the impact of the earthquake based on the characteristics and condition of the quake. The explanation of the decision tree above are as follows: a. If the scale is low, does not cause any effect b. If the scale of medium and short duration then no effect c. If the scale of medium and long duration then cause broken d. If the scale height and distance from the coast 0 / happened on land, it causes broken e. If the scale height and distance from the coast very far then cause broken and tsunami f. If the scale of height and distance from the coast far and The epicenter sea it causes broken and tsunami

IV. CONCLUSION
Based on the description above can be summarized as follows: a. The data of earthquakes that has ever happened can provide useful information or knowledge b. Data mining algorithms can be used to predict C4.5 c. Algorithms C4.5 can predict the impact of the quake based on seismic data that has ever happened which modeled in the form of a decision tree d.
e. An impact of earthquake affected by some characteristics or conditions of an earthquake that is the scale, duration, distance from the beach and The epicenter.