K-Nearest Neighbor and Weight K-Nearest Neighbor Classification of Cork Fish Using Gray-Level-Co-Occurrence Matrix Algorithm Approach

Ornamental cork fish is a type of fish that is in great demand among the public as an ornamental fish. Ornamental cork fish have various types and colors; each variation has its own name and is a selling point among ornamental cork fish lovers. With a good motif, ornamental cork fish will have an expensive market value. However, for the most part, there are still many who do not know for sure what type of ornamental cork fish is included in the variation type classification because the colors are varied and seem similar. Because of this, this research created a system that can classify types of ornamental cork fish automatically based on data while still paying attention to the level of accuracy of the classification. The algorithm used for the initial classification process is KNN, which is chosen for its accuracy comparison level value. This algorithm does not consider the weight of each data point to be classified. The data processing process carried out only looks at the highest number of classes, which becomes the benchmark for labels from the classification results. In the classification process method using the KNN algorithm, there are still shortcomings in the classification process, so this research carried out a process of comparing classification accuracy using the Weight-KNN algorithm to increase the classification accuracy value. The process of the Weight-KNN algorithm stages is to carry out classification based on nearest neighbors first but still paying attention to the weight of each data. So that the classification process of determining the type of ornamental cork fish variation will be more accurate. Based on the results of experiments conducted, this research will focus on comparing the classification results between the KNN and Weight-KNN algorithms on ornamental cork fish. The results obtained state that the Weight-KNN algorithm has a higher level of accuracy with a weight of 83.6%, whereas using the KNN algorithm, it is only 80.6%.