Agritech Decision Support System Using the SMART Method for Determining Optimal Oil Palm Seedling Pollination at PT. Bakrie Sawit Unggul
DOI:
https://doi.org/10.35870/ijsecs.v6i3.7924Keywords:
Decision Support System, Oil Palm, Pollination, SMART, Superior SeedsAbstract
The selection of superior oil palm seed pollination methods at PT. Bakrie Sawit Unggul (PT. Bakrie Sumatera Plantations Tbk) has conventionally relied on field experience and intuition, leading to subjective, inconsistent, and less measurable operational decisions. To address this limitation, this study develops a web-based smart agritech Decision Support System using the Simple Multi-Attribute Rating Technique (SMART) method to determine the optimal pollination strategy. The system evaluates ten pollination alternatives across five operational criteria: the number of flowers pollinated, pollination success rate, processing time, labor requirement, and pollination failure rate. Operational data were gathered through direct field observations, structured interviews, production documentation, and literature review. To facilitate multi-criteria evaluation, raw operational indicators were standardized into a 1–5 preference scale and treated as benefit criteria during linear utility calculations. The software was developed using PHP and MySQL, with functional validation conducted via Black Box Testing to ensure computational accuracy and system reliability. The calculation results demonstrate that Intensive Pollination combined with standard operating procedures (Intensive Pollination + SOP, A10) achieved the highest utility score of 0.8500, outperforming Scheduled Pollination + SOP (0.7794) and Standard SOP Pollination (0.7419). Although Alternative A10 requires greater labor allocation, this requirement is heavily compensated by superior biological success and minimized defect rates across production cycles. These findings confirm that the system provides an objective, transparent, and reproducible decision-making framework, standardizing pollination practices, mitigating production risks, and optimizing resource allocation in Seed Garden management.
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