Data is one of the supporting elements for decision-making in all scientific fields, including in legal dogmatics. As a practical science, legal dogmatics is responsible for responding to legal problems in societies, particularly through
court decisions. In the Indonesian legal tradition, which is heavily influenced by the civil law system, judges in charge of deciding cases do not always use references in the form of precedents drawn from similar case decisions. Situationsnlike this often attract criticism as they are likely to cause inconsistencies in court decision-making and will ultimately reduce public confidence in the courts. This paper offers a way to overcome this tendency and aims to model judgment decisions from cases that are related to cases through data mining with the decision tree method. The methods used
are Decision Tree ID3, with data derived from decisions on corruption cases decided by the Corruption Court in Indonesia over the past ten years. This data-training model has shown variables that can be used as data-training that
really needs consistency in decision making in corruption cases in Indonesia. Considering that the judges' decisions are the result of factual as well as normative reasoning, there are many elements that must be anticipated. The judge's inconsistency in interpreting the facts results in the judge's interpretation of the elements of the crime. This disparity is the source of the weakness of the model that is currently presented in this study. One way to overcome this is to increase the data variance. If the researcher only uses duplicate data from the previous training data, the end result
will not be very different.

Keywords: judge's decision, corruption, decision tree, data-mining, data-training.

Erick Fernando

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Model for Data Training Judge's Decisions in Corruption Cases in Indonesia