Dampak Perubahan Kebijakan Pengadaan Secara Elektronik melalui E-Katalog V5 Menjadi V6 bagi Penyedia di Kecamatan Balongbendo
Bibliographic record
Abstract
This study aims to analyze the implementation of procurement of goods and services through e-Catalog version 6 in Balongbendo District after the enactment of policy changes from the previous version (V5). In addition, this study also identifies impacts, obstacles, and strategies to increase system effectiveness. This study uses a qualitative descriptive method with a document study approach based on the Electronic Procurement of Goods/Services Implementation Report for the third quarter of 2025 as a secondary data source. The results of this study show that the implementation of the V6 e-Catalog has been relatively effective with a completion rate of 87.18% of a total of 39 procurement packages. However, obstacles were found in the form of human error (75%) and system bugs (25%) that caused some packages to be unable to be processed. The transition from version 5 to version 6 brings a positive impact in terms of increased transparency and efficiency, but it also poses new challenges in adapting to the latest version. This study confirms that the success of digital transformation of public procurement does not only depend on technological aspects, but also on human readiness and adaptive institutional governance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".