Yeremia Tulude Ambat, Jhony Alfian Khusyairi, Diah Ariani Arimbi

Visual Accuracy of the Proclamation of Independence of the Republic of Indonesia in the Eyes of ChatGPT

Introduction

Visual accuracy of the proclamation of independence of the republic of indonesia in the eyes of chatgpt. Analyze ChatGPT's visual accuracy of the Indonesian Independence Proclamation. Study reveals AI's historical inaccuracies in depictions, creating hyperreality. Emphasizes human oversight for factual representation.

Abstract

In today's era, Artificial Intelligence is increasingly entering into the layers of society, including the historical aspect. This study aims to analyze the accuracy of the illustration of the Proclamation of Indonesian Independence 1945 produced by ChatGPT using a qualitative research approach with Ferdinand de Saussure's semiotic analysis research method. This study found that the illustrations produced by AI were historically inaccurate, both in the depiction of the proclaimer, clothing, crowds, and main symbols such as the red and white flag. In addition, although some aspects, such as people's clothing, are close to the facts, most of the illustrations show deficiencies in conveying the true meaning of history. This distortion creates hyperreality, where the resulting visual elements reflect the algorithm's interpretation more than historical facts. This study also highlights that the inaccuracy is due to the limitations of AI in understanding cultural and historical contexts in depth. AI illustrations tend to function as symbolic metaphors rather than factual representations, obscuring the original meaning of events. That makes AI categorized as popular culture, which makes it inauthentic and unoriginal. It shows that AI still requires human intervention to ensure cultural and historical accuracy. This study concludes that the use of AI in representing historical events requires critical oversight to prevent the spread of biased or misleading information. The study emphasizes the need for collaboration between technology and human interpretation to maintain the accuracy of historical information.

Review

This study critically examines the visual accuracy of images generated by ChatGPT regarding the 1945 Proclamation of Indonesian Independence. Employing a qualitative research approach with Ferdinand de Saussure's semiotic analysis, the authors investigate whether AI-produced illustrations faithfully represent historical events. The central finding unequivocally demonstrates that these AI-generated visuals are historically inaccurate, portraying distortions across key elements such as the proclaimer, attire, crowd depiction, and national symbols. This timely research makes a crucial contribution to the ongoing discourse surrounding AI's role in historical representation, underscoring the potential for misinformation when AI operates without nuanced contextual understanding. The paper meticulously details the discrepancies, noting that while some elements like general clothing styles might approximate historical facts, the majority of the illustrations fail to convey the true meaning and specifics of the event. The concept of "hyperreality" is effectively introduced to explain how AI's output reflects algorithmic interpretations rather than factual representations, thus categorizing it as popular culture rather than an authentic historical source. The authors compellingly argue that this inaccuracy stems from AI's inherent limitations in grasping deep cultural and historical contexts, leading its illustrations to function more as symbolic metaphors than accurate records. This highlights a significant vulnerability in relying solely on AI for historical visualization and underscores the imperative for human intervention and critical oversight to ensure factual fidelity. The study's conclusion, emphasizing the necessity of critical oversight and collaboration between technology and human interpretation, is particularly salient. While the abstract provides a strong overview of the findings, a deeper dive into the specific limitations of the AI models (e.g., the nature of their training data, biases, or architectural constraints) that lead to these inaccuracies would further enrich the discussion. Future research could explore mechanisms for integrating expert historical knowledge into AI training, or developing tools that flag potential historical inaccuracies in AI-generated content. Overall, this paper serves as an important cautionary tale, reminding both developers and users of AI that while its capabilities are vast, its application in sensitive domains like historical representation demands rigorous validation and human accountability.

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