<![CDATA[Central obesity was a major risk factor for noncommunicable diseases (NCDs), with an increasing trend in Denpasar City, highlighting the need for an effective surveillance system. This study evaluated the performance of the ASIK-based central obesity surveillance system using a logic model framework (input, process, and output) and surveillance attributes. A mixed-methods descriptive evaluative study was conducted among NCD program officers at the Denpasar City Health Office and community health centers. The quantitative component used a cross-sectional approach, while qualitative data were collected through in-depth interviews, involving 12 respondents selected through total sampling. Data were collected using structured Likert-scale questionnaires and in-depth interviews and were analyzed descriptively based on CDC surveillance system evaluation guidelines. The results showed that input components were relatively adequate; however, challenges included dual workloads, limited technical training, and insufficient operational support. At the process level, the system operated as a non-integrated manual-digital hybrid, characterized by delayed data entry, duplication of recording, and lack of routine data verification. At the output level, data utilization remained limited to administrative reporting and was not optimally used for program planning or decision-making. In terms of surveillance attributes, simplicity (83.3%), flexibility (100%), and acceptability (91.7%) were categorized as good, while data quality (16.7%), representativeness (25.0%), stability (8.4%), and timeliness (50.0%) were low. In conclusion, the ASIK-based central obesity surveillance system was operational but not yet optimal. Strengthening system integration, improving data quality assurance mechanisms, and enhancing data utilization were essential to improve surveillance effectiveness and support evidence-based decision-making. Keywords: ASIK application, central obesity, logic model, surveillance system evaluation.]]>
