Honey yield prediction using Tsukamoto fuzzy inference system

• Published in: 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) pp. 1 - 6
• Main Authors: Hastono, Tri, Santoso, Albertus Joko, Pranowo
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-09-2017
• Subjects: Agriculture; Apis Cerana; beekeeper; beekeeping; Business; Fuzzy logic; Fuzzy sets; Honey yield; Insects; Mathematical model; prediction; Three-dimensional displays; Tsukamoto Fuzzy Inference System
• DOI: 10.1109/EECSI.2017.8239150

Honey is a natural product of bee. Since ancient times, honey has been known by humans as a source of natural food and also for traditional medicine. There are so many beneficial of honey, make people trying to do honeybee cultivate as a business solution to increase their income. However, to cultivate honey bees is not easy. Special knowledge is required on honey bee cultivation and capital is fairly large. In order for beekeepers not to lose from honey sales business, beekeepers should be able to estimate the honey yield accurately. Predicted yield of honey is used as a material consideration and help determine the decision in honey bee cultivation. This study provides a solution for prediction of honey yield type Apis Cerana with the main food of Calliandra flowers accurately. The method used in this research is Tsukamoto's fuzzy inference system (FIS) method. There are 3 input fuzzy used in this study, namely : Rainfall, number of box, and number of flower trees. The three fuzzy inputs are the determinants of the honey yield. The representation model used in the research is Trapezoid with fuzzy rules of 125 rules. While the test data in this research are rainfall and honey yield data for 21 years. The results of this study showed that the prediction of honey yield using FIS Tsukamoto closed the real honey yield with RMSE value of 9.44933860119277.