EFFECTIVENESS OF AIRPORT FUNCTION SEPARATION IN INDONESIA: A CASE STUDY OF SOEKARNO-HATTA AND HALIM PERDANAKUSUMA AIRPORTS, WITH A PROPOSAL FOR UTILIZING KERTAJATI AIRPORT FOR PRIVATE AVIATION
Bibliographic record
Abstract
This study analyzes the effectiveness of airport function separation in Indonesia as a strategic response to airspace congestion, operational inefficiencies, and overlapping civil-military airport usage. Focusing on Soekarno-Hatta (CGK), Halim Perdanakusuma (HLP), and Kertajati (KJT) Airports, the study adopts a qualitative-descriptive method supported by in-depth interviews with stakeholders from the Ministry of Transportation, Indonesian Air Force (TNI-AU), airport operators, airlines, and aviation experts. The findings reveal that overlapping functions at Halim generate flight delays, coordination difficulties, and safety risks, while Soekarno-Hatta faces high traffic volume with limited relief options. Meanwhile, Kertajati remains underutilized despite having modern infrastructure and a strategic location. Drawing on international best practices, such as Japan’s Narita-Haneda-Chōfu model and similar systems in the US and UK, the study proposes a five-function airport classification: international commercial, domestic commercial, state/VVIP, military, and private/general aviation. It recommends formalizing a national regulation on function separation, reclassifying Halim for exclusive state and military use, and optimizing Kertajati through Public-Private Partnerships (PPPs) supported by regulatory incentives. Establishing a civil-military coordination body is also urged to enhance airspace governance. The novelty of this study lies in its integrated policy framework, which combines regulatory, institutional, and operational dimensions. Unlike previous studies that focus only on Jakarta’s traffic management or civil-military coordination, this research introduces Kertajati as a national hub for private and general aviation, an approach that remains underexplored in Indonesian aviation discourse and presents a viable solution to redistribute traffic and improve national airspace efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".