CLUSTERING SEGMENTASI PASAR PENERBANGAN BANDARA HUSEIN SASTRANEGARA – BANDUNG MENGGUNAKAN INFORMASI PENERBANGAN
Bibliographic record
Abstract
Peralihan fungsi Bandara Husein Sastranegara-Bandung ke Bandara Kertajati-Majalengka diharapkan dapat mengakomodir pergerakan transportasi udara di Provinsi Jawa Barat. Tujuan penelitian untuk mengetahui segmentasi pasar penerbangan di Bandara Husein Sastranegara-Bandung menggunakan informasi penerbangan agar memudahkan calon maskapai dalam perencanaan penerbangannya di Kertajati. Segmentasi pasar menggunakan metode clustering K-Means dengan data informasi penerbangan tahun 2017 – 2021. Data informasi penerbangan yang digunakan berasal dari statistik transportasi udara BPS, merupakan data kuantatif diantaranya data bandara asal dan tujuan yang dari atau menuju Bandara Husein Sastranegara-Bandung, data pergerakan penumpang dan kargo (kg) dari tiap – tiap rute penerbangan tersedia dalam kurun wakti 2017 – 2021. Hasil clustering menunjukan segmentasi pasar penerbangan keberangkatan dari Bandung memiliki 4 cluster segmentasi pasar, dan 3 cluster untuk tujuan Bandung. Terdapat perbedaan destinasi asal dan tujuan keluar atau menuju Bandung pada kasus keberangkatan dan kedatangan. Dominasi daerah asal atau tujuan diminati pada keberangkatan dan kedatanagn ialah Semarang. Cluster keberangkatan terbanyak menuju Kualanamu dengan rata – rata 310.218 penumpang dan 3.022.091,2 kg kargo di rute tersebut. Cluster kedatangan diminati asal Jakarta (Halim Perdanakusuma) dengan rata – rata 238.613 penumpang dan kargo yang diangkut sebanyak 692.964,14 kg terbang di rute ini.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".