Weather Prediction for Strawberry Cultivation Using Double Exponential Smoothing and Golden Section Optimization Methods - Herlinah, Herlinah; Asrul, Billy Eden William; HS, Hafsah; Faisal, Muhammad; Lee, Swa Lee; Gani, Hamdan; Feng, Zhipeng (ILKOM Jurnal Ilmiah, 2024)
Strawberry is one of the fruit commodities that has a high demand so that it is widely cultivated by most people in Bantaeng Regency to meet with the market needs. The high intensity of weather changes is the main challenge in the strawberry production, which is influenced by climate dynamics and the start season time changes. Climate change does not only affect the amount of rainfall, but also causes a shift in the rainy season and dry season start. As a result, in the cultivation of plants such as strawberries, there are often difficulties in adjusting or slow anticipation in the extreme changes of rainfall. This research began with the data collection stage through field observations, interviews, and literature studies. The design tool used a systematically organized UML, which included a use case diagram, then an activity diagram, as well as an elaboration into sequence diagrams, and class diagrams. The system was developed by implementing the PHP programming language on the interface design as well as MySQL as a database processing. The algorithm used to predict the air temperature feature, wind speed feature, and rainfall feature was Double Exponential Smoothing, followed by the optimization of the Golden Section method to select the right smoothing value. Referring to the results of this study, the system can provide planting time recommendations based on prediction of rainfall, air temperature, and wind speed parameters through a web-based platform. Based on the calculation of the accuracy value of the prediction results using the Mean Absolute Percentage Error (MAPE), the obtained forecast error value was of 5.89% for wind speed, 0.63% for air temperature, and 0.69% for rainfall. The Golden Section Optimization in Double Exponential Smoothing provided the best smoothing for prediction.