Implementasi Algoritma Fuzzy K-Nearest Neighbor (FK-NN) Untuk Penentuan Kualitas Mutu Air: Implementation Of The Fuzzy K-Nearest Neighbor (FKN-NN) Algorithm For Determining Water Quality - Yose Parman Putra Sinamo; Sutrisno Sutrisno; Sigit Adinugroho (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2021)
Water has an important role in maintaining life. Without water, humans cannot carry out their daily activities, because water is an inseparable part of their daily activities. Water that is good for humans is water that has met the requirements as suitable water for daily use. For this reason, there have been many studies on water classification in determining water quality. However, in determining the results obtained, the accuracy is less than satisfactory and can be improved again. In determining the classification of water, the K-Nearest Neighbor (FK-NN) fuzzy method is used. Some of the attributes or parameters that will be used in this research are the degree of acidity (pH), TDS, NO2, NO3, hardness, chloride, manganese. There are several tests carried out including testing the k value, the distribution of data ratios, and the distribution of data classes. From this test, the accuracy value is 95.52%, with the data ratio level consisting of 70% training data and 30% test data, with a K value of 10.At the level of data class distribution, 80% accuracy is obtained with the distribution of test data classes using 20 data with class 0 is 10 and class 1 is 10. From the accuracy obtained, it is concluded that the accuracy value obtained is much greater than the accuracy of previous research which is only around 78.70% and 85.70% in the Support Vector Machine and Naive Bayes methods..