This study employed a descriptive qualitative method with an error analysis approach to examine language inaccuracies found in online news titles and texts as well as their impact on readers’ comprehension. Employing a descriptive qualitative method with an error analysis approach, data were sourced from five articles on Detik.com (May 2026) across various fields (economy, environment, culinary, health) and analyzed based on EYD and KBBI. The results revealed 42 errors across eight categories: non-standard language (21.43%), ineffective sentences (16.67%), diction errors (11.90%), morphological errors (11.90%), syntactic errors (11.90%), spelling errors (9.52%), redundancy (9.52%), and language logic errors (7.14%). Non-standard language and ineffective sentences were the most dominant findings. These linguistic errors result in semantic ambiguity, information distortion, and decreased media credibility. This study recommends stricter language copyediting standards in online newsrooms.