Selektivitas Pemilihan Komputer dengan Pendekatan Pohon Keputusan
Keywords:
c4.5 algorithm, decision tree approach, selectiveAbstract
The important role of a computer in work activities is solving cases that require fast and precise computing processes. However, the limitations of the required computer specifications must sometimes be taken into account according to the specified needs. Cases that are commonly found in terms of aspects of the availability of computer specifications are related to multimedia, and one that is quite prominent in the current 4G technology trend is e-sport activities, namely online games. The main requirement for running games is video-memory capacity, namely image requirements. The image is based on a dictionary-color data that can be saved, so for a display with fidelity characteristics it certainly requires a large video-memory capacity, so that when the game is running it doesn't get stuttering. A fairly simple method with a decision-tree approach by applying certain techniques or algorithms, in order to obtain the specifications of the computer; based on the component attributes it has (such as processor, main-memory, and video-memory), and the operating system (such as software) is whether it is compatible or vice versa. There are two operating systems involved in the testing process, namely: Windows 10 and Windows 11; the Windows family was chosen for the reason that the majority of users use it and is adapted to games that are commonly run on this variant of the operating system. The decision tree approach is based on the C4.5 algorithm. And in this research a series of tests have been carried out on a number of computers with various characteristics and specifications; with the field of objects tested being games, without specifically limiting whether they run on-line or off-line; but on this research used on-line . There were three sample games involved in the testing with the number of computers processor being selected using seven different specifications. The results show that from a number of computer specifications involved in testing the three game samples, some are compatible. And the results obtained showed that the machine learning model designed was optimal.
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