Representasi Kecerdasan Buatan (AI) Pada Iklan Sirup Marjan “Bangkitnya Calon Arang” Episode 1&2 Tahun 2025 Melalui Analisis Semiotika Charles Sanders Peirce
Bibliographic record
Abstract
Iklan not only functions as a marketing strategy, more than that advertising is also a forum for conveying social, cultural, and technological developments. The current issue of technological developments was expressed by Geoffrey Hinton, a computer scientist and artificial intelligence expert from Canada, who used the analogy of "cute tiger cub to describe today's AI. And the concern is the potential dangers of AI in the future as it matures and gets stronger." This research aims to find out how Artificial Intelligence (AI) is represented in the Marjan Syrup advertisement entitled "The Rise of Calon Arang " Episodes 1 & 2 in 2025. This research is a qualitative descriptive research. Data collection techniques through observation, documentation and literature studies. The analysis was carried out using Charles Sanders Peirce's semiotic approach. The author is interested in using this semiotics theory to express the meaning of artificial intelligence representation through the concept of triangle meaning in triadic and trichotomy analysis to answer the formulation of problems in research. In Charles Sanders Peirce's semiotics, the concept of triadics contains semiotic relationships consisting of Representation as a sign, Object as a marker of a sign, and Interpretant is the meaning that arises from the existence of a sign and an object. This research was carried out on the Marjan syrup advertisement "The Rise of Calon Arang" Episode 1 &2 in 2025 which then produced a representation of artificial intelligence what is in the advertisement and the meaning contained in it. From the results of this research, there are 4 forms of representation of artificial intelligence, including, artificial intelligence as a tool or system, artificial intelligence as an autonomous entity, artificial intelligence as a threat or potential enemy, and artificial intelligence with pseudo-power.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".