Comparison of the K-Nearest Neighbor algorithm and the decision tree on moisture classification
Year: 2021
Type: conference-paper
Abstract: Soil moisture is a parameter needed by plants in terms of plant growth. In determining the appropriate soil moisture for plants requires a control system. This study uses a comparison of KNN and decision tree algorithms with the aim of being able to determine soil calcification with yield parameters namely moist and dry, so that it has good accuracy compared to the accuracy of the Decision Tree algorithm with an accuracy of 55.77% with dry class recall of 62.69% moist 51.92% dry precision class 58.33% humid 47.37% and K-Nearest Neighbor with 72.69% accuracy dry class recall 80.60% humid 63.16% dry precision class 72.00% humid 73.47%. The results of the above model testing can be concluded that the K-Nearest Neighbor is the most accurate algorithm for classification of moist soil
Authors: Odi Nurdiawan, D. A Kurnia, Dodi Solihudin, Tuti Hartati, Tati Suprapti
Institution: STMIK IKMI Cirebon
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W3134845183