Landslide susceptibility mapping in many regions must rely on inventories compiled from community reporting, which provide presence data only. Which modelling approach best suits such data remains unclear. This study compares Maximum Entropy (MaxEnt), Frequency Ratio (FR), and logistic regression (LR) for landslide susceptibility mapping in Semarang Regency, Indonesia, using an identical set of conditioning factors and an identical validation scheme so that differences in performance reflect the methods themselves. An inventory of 445 landslide points, compiled from the records of the Regional Disaster Management Agency and from field survey, was modelled against 13 continuous conditioning factors at 30 m resolution and evaluated through five-fold cross-validation. Discrimination was assessed with AUC, TSS, and partial AUC restricted to the high-specificity range (0.8-1.0), with differences tested using the DeLong test and a bootstrap procedure. MaxEnt recorded the highest values on all three metrics (AUC 0.727; TSS 0.366; pAUC 0.635), followed by FR (0.715; 0.348; 0.597) and LR (0.679; 0.294; 0.567). The difference between MaxEnt and FR on the full AUC was not significant (p = 0.193), and FR was the most stable model across folds (SD 0.009), whereas LR differed significantly from both. MaxEnt outperformed FR only over the high-specificity range (p = 3.3×10⁻⁶) and in the most susceptible class, where it captured 57.5% of events against 53.5% for FR and 45.4% for LR. Slope dominated both models that produce importance scores. Presence-based modelling is therefore adequate for report-based inventories, with FR suited to rapid and transparent mapping and MaxEnt to sharpening the most susceptible zone.