DESAIN SISTEM MONITORING CONTROL AND SURVEILLANCE NASIONAL DALAM RANGKA PEMBANGUNAN KELAUTAN INDONESIA (National Monitoring, Control and Surveillance Design System for Marine Development in Indonesia)
Bibliographic record
Abstract
Indonesia, an archipelago of 17,508 islands has an abundant of marine resources and also strategic position in international sea-traffic. MCS should be applied to protect Indonesia from illegal activities at the sea. The purpose of this research is to design a national monitoring control and surveillance system in developing Indonesian maritime. Benchmarking analysis was chosen as the preferred analysing method to compare the Indonesian MCS activities with 24 other countries. In order to determine the key factors of Indonesian MCS system, an expert survey was performed. The analysis result shows that the Indonesian MCS activities still operates in a low level compared to the MCS activities of many countries, such as Canada, Australia, and America. Thus, in order to reach a more preferred level, lndonesia has to improve their MCS operation base and furthermore also improve their MCS performance. In order to improve the Indonesian MCS system, factors such as legislation and permission should be more heavily considered, while the performance level of other factors relating to MCS should also be increased. Further research conducted through the SWOT and statistical analysis is still needed in order to determine the system development model of Indonesian MCS.Key words: MCS, benchmarking analysis, design system, prospective analysis.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".