Pemanfaatan Teknologi Artificial Intelligence untuk Penguatan Kesehatan dan Pemulihan Ekonomi Nasional
Bibliographic record
Abstract
Technological developments have been used to support health and economic systems in various countries. Especially during the COVID-19 pandemic, in the health sector, for example, starting from the process of identifying positive cases with chatbots, contact tracing, monitoring independent isolation, even monitoring social media for mental health can be done with the help of technology. This can help the government make policies and keep health workers in direct contact with patients, especially patients with mild symptoms, for patients with severe symptoms can be prioritized to be assisted by health workers. In the economic field, this pandemic has caused economic growth to decline, even in the third quarter of 2020 Indonesia experienced negative economic growth. The largest proportion of economic growth in Indonesia is household consumption, which is closely related to people's purchasing power. Artificial intelligence technology can be used to examine the level of public consumption. So that it helps the government in making policies on how to increase people's purchasing power. The use of this technology involves a variety of devices, online datasets, devices connected to the internet (IoT), and advances in the fields of machine learning, computer vision and natural language processing. This study aims to provide an overview of how artificial intelligence technology has great potential in strengthening the health system and restoring the national economy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".