Decision Support System For Oil Palm Seed Selection Using The Simple Additive Weighting Method - Silvia Wulandari Situngkir; Sanri Yuliana Siallagan; Muhammad Ridho (Universitas Suryakancana Cianjur, 2025)
The selection of superior oil palm seedlings is a crucial factor in improving plantation productivity. However, the selection process, which often relies on farmers’ subjective experience, frequently results in inaccuracies in determining ready-to-plant seedlings. This study aims to develop a Decision Support System (DSS) using the Simple Additive Weighting (SAW) method to assist farmers in selecting high-quality oil palm seedlings based on four main criteria: number of fronds, seedling age, seedling height, and stem diameter. The SAW method was applied to calculate the preference value of each seedling alternative through normalization and weighting of all criteria. The system was developed as a web-based application using the Laravel framework and tested using the Black-Box Testing method to ensure functionality and accuracy. The testing results showed that the system produced recommendations identical to manual calculations (100% accuracy) and completed data processing in less than 2 seconds for 24 seedling alternatives. This decision support system has proven to be efficient, accurate, and stable in supporting the oil palm seedling selection process. The system’s main advantage lies in its ability to automate the evaluation process quickly and objectively, reducing human error and accelerating decision-making for farmers and plantation managers.