<![CDATA[The growth of electric motorcycles in Indonesia, which is projected to reach more than 196,000 units by mid-2025, has created complexity in consumers’ purchasing decision-making processes due to the wide variety of technical specifications across brands. This study designs and develops a user-driven Android-based Decision Support System by integrating the Fuzzy Analytical Hierarchy Process (Fuzzy AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to generate personalized recommendations for selecting electric motorcycles. The system evaluates 23 alternatives from seven brands with official dealers in Pekanbaru based on seven technical criteria: price, range, charging time, maximum speed, motor power, load capacity, and battery capacity. Criterion weights are determined dynamically through a 1–5 scale slider interface mapped to Triangular Fuzzy Numbers (TFN) and processed using Chang’s Extent Analysis Method (1996), while ranking is performed using TOPSIS. This study applies the Fuzzy AHP method to address the ambiguity in users’ subjective assessments, which are often not well accommodated by single crisp values in conventional AHP. The main contribution of this study lies in the simplification of the weight elicitation mechanism, which reduces 21 conventional pairwise comparisons to just seven direct slider inputs mapped into TFN form. Furthermore, this study successfully implemented this user-driven, slider-based mechanism into an Android-based decision support system (DSS) for selecting electric motorcycles that is directly accessible to end consumers. For system testing, all scenarios in the Black Box Testing (33 scenarios) were successfully executed without errors. Furthermore, an evaluation via User Acceptance Testing (UAT) using the USE Questionnaire framework on 10 respondents yielded an acceptability score of 83.2%, which falls into the “Highly Acceptable” category. Based on the Performance preference profile, the United RX6000 was determined to be the best alternative with a Closeness Coefficient value of 0.9377.]]>
