<![CDATA[Tomatoes are a major export commodity in the country's plantation sector. This increases the urgency of efforts to increase tomato productivity, both in terms of quantity and quality. Evaluation of tomato quality currently relies on the degree of ripeness and skin texture. The conventional method currently used involves manual inspection, which can allow for misjudgment and economic loss. This research aims to use a digital image-based approach by utilizing a decision support system that combines the AHP and TOPSIS methods to assess tomato quality based on color and texture criteria. This research evaluates and ranks nine tomato images that have good quality, by giving higher priority to skin texture than skin color. Evaluation results from three tests showed that the system was able to determine the quality of tomatoes with an average kappa value of 0.78, which interpreted the results of good agreement between the system and expert judgments.]]>
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