READABILITY ASSESSMENT OF AI-DRIVEN INDONESIAN JOURNALISTIC TRANSLATIONS: COMPARING CHATGPT AND DEEPL PERFORMANCE - Farizka Humolungo; Fanny Puji Rakhmi; Ina Sukaesih; Ince Dian Aprilyani Azir; Talenta Gloria Napitupulu (LLDIKTI Wilayah X, 2026)
This study investigates the readability of Indonesian to English translations of journalistic texts produced by ChatGPT and DeepL across the domains of law, politics, and environment using articles from Tempo and Kompas. A qualitative descriptive comparative design was employed. Six source texts were translated under controlled procedures, with standardized prompts for ChatGPT and standard input for DeepL. Two expert raters evaluated segment level readability using a three point rubric adapted from prior work. Before scoring, the raters reviewed and aligned category descriptors to support shared interpretation. Readability scores were summarized by outlet and topic, and rater comments from a focused discussion were used to contextualize patterns. The findings show small and patterned differences between systems. One rater tended to rate ChatGPT as more readable, while the other found broad parity. Readability was highest for environmental reporting and most challenging for legal texts that contain dense institutional references. Kompas items were generally read as more accessible than Tempo items. The results point to domain sensitive strengths for each system and suggest targeted post editing as a practical lever to support clarity for public facing news.