<![CDATA[Banana plants (Musa paradisiaca) are among the leading agricultural commodities in Banyuwangi Regency and have long played an essential role in supporting the local economy. However, in recent years, the productivity of banana plantations has experienced a significant decline. This decrease is closely related to unsuitable soil conditions, including excessive moisture, unstable temperature fluctuations, and extreme soil acidity (pH). Such unfavorable conditions create challenges for farmers, who often find it difficult to evaluate soil characteristics accurately. As a result, cultivation strategies become less effective and crop yields fail to reach their optimal potential. To address this problem, this study developed a web-based Decision Support System (DSS) designed specifically for assessing soil suitability for banana cultivation. The DSS applies the Decision Tree algorithm to classify soil conditions based on three key parameters: moisture, temperature, and pH. The system development process followed the Rapid Application Development (RAD) methodology, which emphasizes iterative prototyping and active participation of farmers, ensuring that the solution is practical and aligned with real-world field needs. Validation of the system was carried out through Black Box Testing and model evaluation, which produced an accuracy rate of 70.9% in classifying soil suitability. The DSS not only passed all functional tests but also generated practical recommendations for soil management strategies aimed at improving crop conditions. Ultimately, this research contributes a reliable, user-friendly, and farmer-oriented tool to support sustainable banana cultivation in Banyuwangi, with the potential to enhance productivity and strengthen decision-making capacity.]]>
