Implementation Of The Support Vector Machine Method In Determining The Best Quality Of Sap - Azhari Putra Sayani; Safwandi; Fajriana (Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN), 2024)
Rubber trees (Hevea brasiliensis) are the main source of natural rubber and play an important role in Indonesia's industry. Determining the quality of rubber sap is a challenge for companies, as traditional manual processes are time-consuming and prone to human error. PT Poly Kencana Raya, a company in Besitang, North Sumatra, currently still uses conventional methods in determining the quality of rubber latex it produces. This research aims to design a web-based system with the application of the Support Vector Machine (SVM) method to facilitate the determination of rubber latex quality. SVM was chosen as a classification method because of its ability to determine the optimal hyperplane that can separate data from two different classes, namely feasible and unfit. The built system utilizes the main criteria such as tree age, tapping time, moisture content, color, and texture in determining the quality of the sap. Implementation. This study used 120 samples of test data, with accurate prediction results on 111 data, resulting in an accuracy rate of 92.5%. This decision support system is expected to increase efficiency and accuracy in rubber sap selection and support the development of rubber production quality in Indonesia. This research also opens up opportunities for further development by adding other classification methods for accuracy comparison or adding training data to optimize prediction results. Keywords: Rubber Trees, Support Vector Machine, Data Mining