Implementation of Military Incident Management System in Disaster Management in Indonesia
Bibliographic record
Abstract
Indonesia’s success in disaster management cannot be separated from the military’s role. The military plays a strategic role by mobilizing military resources on a massive scale through the military command system. However, the ability of Indonesian Army (TNI AD) soldiers and organizations, in general, is considered to have limited capabilities specifically for personnel handling natural disasters. This research aims to map the disaster management implemented by the Indonesian Army in disaster response through the Incident Management System. Data collection was conducted interactively through qualitative methods with in-depth interviews with the Indonesian Army’s Supply and Transportation Unit (Pusbekangad). The research results show that the Indonesian Army (TNI AD) has competent resources in disaster response, involving the Indonesian Army’s Supply and Transportation Unit, which has primary skills and capabilities in logistics and transportation. These capabilities are facilitated by the Incident Management System, which is structured, systematic, and well-organized. The Incident Management System built by the Indonesian Army involves an incident commander, operation section, planning section, logistics section, finance/administration section, driver section, and the cooking team as a trained, capable, experienced, and ready-to-deploy ad-hoc organization in all operational areas. Indonesian Army uses the Incident Management System to respond to disasters such as earthquakes in Cianjur, South Kalimantan floods, and West Sulawesi floods. The Incident Management System serves
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".