Strategic Planning for Rice Seed Productivity Using Integration of modified TF-IDF and SWOT-QSPM

Abstract

The agricultural sector of Indonesia is dependent on the availability of highquality rice seeds for its functionality. The effective management of these seeds is therefore of paramount importance to ensure the continuity of productivity and the security of food supplies. However, the aspirations of farmers, who are the primary actors, are often ineffective and only available in an unstructured narrative form. This complicates the process of strategic decision-making. The objective of this study is to enhance rice seed productivity by developing a strategy that employs an integrative informatics approach, integrating text mining, SWOT analysis, and the QSPM method. The data was collected via 100 open-endedinterviews with farmers and processed through text cleansing, modified TF-IDF weighting, and token classification into SWOT factors. The classification results were then employed to construct IFAS and EFAS matrices, which were used to determine strategic positioning. The utilization of the QSPM matrix facilitated the identification of priority strategies. The analysis indicated that the seed aspect falls into quadrant IV, suggesting a predominance of weaknesses and threats, necessitating a defensive (WT) strategy. The primary strategy identified was the provision of superior seeds that are resistant to extreme weather; this strategy achieved the highest score in the QSPM analysis. The strategy’s feasibility level, as validated by three experts, exceeded 83%, thus categorizing it as "highly feasible." The present study concludes that integrating text mining techniques with SWOT-QSPM transforms opinion data into an objective, adaptable, and applicable decision-making strategy based on local data.