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Relative abundance indices as calculated based on commercial catches are the input data to run stock assessment models to gather useful information for decision making in fishery management. In this paper a Generalized Linear Model (GLM) was used to calculate relative abundance indices and effect of longline fishing gear configuration. Data were collected by a scientific observer program from 2006 to 2017. Most of the boats monitored were based in Benoa Port, Bali. Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to select the best models among all those evaluated. Zero-inflated negative binomial (ZINB) model and simple negative binomial (NB) model had the lowest AIC and BIC value, respectively. Time trends of standardized CPUE as calculated using Poisson (P) and Zero Inflated Poisson (ZIP) models were fluctuated from 2006 to 2009. The trends showed differently from 2010 where P model increased and reached the peak in 2015 while ZIP model decreased gradually to 2017. Catches are often equal to zero because silky shark is a bycatch for Indonesian longline fleets. Therefore, a hurdle model was used. The low proportional decrease of deviance indicates that most of the variability of catch rates of silky shark caught by Indonesian longline boats are not related to year, quarter, number of hooks between floats and the length of branch lines. Other variables and information, like the daytime when the longlines are deployed in the water (day or night), type of bait, size and type of hooks, are necessary to better understand the catch rate, and improve the estimations of the relative abundance indices.
