Talent Readiness of PT KAI to Face the Era of Change
Bibliographic record
Abstract
ABSTRACT: PT KAI is the only rail-based transportation company in Indonesia managed by SOEs. The main services offered by PT KAI are transportation for passengers and products, while its non-core business is real estate, and its subsidiaries include six companies offering a wide range of services. PT KAI is currently undergoing changes that can be seen with the change in partnership PT KAI is moving towards Global Partnership. This can be seen by the cooperation carried out by PT KAI with other countries in carrying out the Indonesian Fast Train project. One of PT KAI's missions is to become digital-based railway transportation. This mission is a business response to changes moving into the digital era. This research was conducted using quantitative methods. Data collection was carried out by researchers by visiting PT KAI Head Office located on Jl. Perintis Kemerdekaan No.1, Babakan Ciamis, Kec. Sumur Bandung, Bandung City, West Java, and PT KAI Training Center located in two locations, namely Jl. Kacapiring, Batununggal District, Bandung City, West Java, Indonesia, and Jl. Ir. H. Juanda No.215, Dago, Coblong District, Bandung City, West Java, Indonesian. The respondents of this study were active employees of PT KAI totaling 168 respondents. The method of data collection is carried out by du event, namely Quick Count or Pooling Priority, and also through Questionnaires. The data processed then was from 140 respondents. The data reduction was carried out because it was found that there were outliers of 28 respondents or 16.67% obtained through examination with the SPSS 26 tool with the Casewise Diagnostic method. Researchers also provide solutions to change issues so that PT KAI is better prepared to face the era of change in the future, consist of PT KAI Goes International through English Habits, and Work Abroad Opportunities for PT KAI's Top Talent, Transforming Service Quality and Security by maximizing the use of AI and Renewable Technology in all Business processes, Creating Synergies (Including SOEs) through harmonious and collaborative strategic alliances with other state-owned companies, Global Partnership as the best transportation ecosystem solution for Indonesia by opening up to the existence of a Global partnership business scheme.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".