ANALISIS ISI PADA KOLOM KOMENTAR YOUTUBE MUSIK VIDEO ZIVA MAGNOLYA YANG BERJUDUL CUKUP
Bibliographic record
Abstract
The comment column on platforms such as YouTube creates an MV space as a medium of communication where viewers can actively participate. This content analysis includes discussions, praise, criticism, and sharing personal stories related to the song. Music video. Want to know how netizens respond to the Music Video "Ziva Magnolya - Cukup. To find out how netizens respond to the Music Video "Ziva Magnolya - Cukup, the research used in this study is a descriptive qualitative approach. In this study, content analysis was used. The time of data collection in this study was November 24, 2023. The comment data used was obtained using the scraping technique from the "Google Apps Script" website. dominant group who are proud of the song entitled "cukup" sung by Ziva Magnolya. Netizens in this category show their admiration for the character, lyrics, music quality, and the way Ziva Magnolya performs the song which ultimately makes the listeners feel as if they are drowning and feel a very deep feeling for the song sung by Ziva Magnolya entitled cukup. Research on Ziva Magnolya's music video entitled "Cukup" on the YouTube platform found various responses from the audience. However, most netizens gave comments that they liked the lyrics of the song and liked Ziva Magnolya. Netizens also gave comments that showed emotional levels in the form of happiness and enthusiasm after hearing Ziva Magnolya's song entitled "Cukup" through the Youtube account.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".