Penerapan Data Mining Dengan Metode K-Nearest Neighbor Terhadap Klasifikasi Sarang Walet - Muhammad Ismail; Renaldi Yulvianda (Universitas Budi Darma, 2023)
Sungai Benuh Village is one of the areas where many swallow houses are made because it is able to produce a large number of swallow nests so that the purpose of this study was to make a classification by applying data mining to see the quality level of swallow nests, so that later it will become a reference in helping buyers and the seller obtains appropriate results and maintains the selling power of the swallow's nest. This is also based on a problem that is often encountered, namely sellers and buyers do not have a fixed standard of evaluation when a transaction takes place, so that unilateral judgments appear. In addition, there are differences in quality and quantity in different seasons. During the rainy season, swallow nests are larger, white, clean and numerous, while during the dry season, the opposite results are obtained. Classification results using the k-nearest neighbor method with Weka software show 90% accuracy for 45 out of 50 data samples, including comparative data samples or new data samples using a value of k = 7 with categorized attributes of cleanliness, color, size, shape and harvest time “Good” or “Bad”. Evaluation of the results with the confusion matrix results obtained accuracy of 80%, precision 80.49, recall 94.29% and F1 score 86.84%. So, this research was successfully carried out with high classification results so that it can be a reference to help buyers and sellers obtain a mutual agreement during transactions and maintain the selling power of the swallow's nest.