Adopsi IoT Pada Core Process Trucking di Indonesia Dengan Menggunakan TOGAF Framework
Bibliographic record
Abstract
Biaya logistik di Indonesia masih tergolong mahal yang disebabkan kurangnya infrastruktur, teknologi, kemampuan sumber daya manusia, kebijakan logistik pemerintah, terjadinya bencana alam, serta seringnya pungutan liar. Pelanggan belum menerima informasi secara real time. Hal ini dapat berdampak kepada kepuasan pelanggan serta terlambatnya proses pembayaran dari pelanggan. Untuk menjawab tantangan-tantangan ini, pelaku usaha trucking diharuskan untuk melakukan inovasi serta meningkatkan kinerja dan utilisasi kendaraan yang dimiliki terutama dengan pemanfaatan teknologi internet of things (IoT). Implementasi teknologi IoT pada perusahaan trucking memerlukan perencanaan enterprise architecture, sehingga teknologi yang diimplemntasikan sesuai dengan kebutuhan bisnis. Pada jurnal ini akan membahas bagaimana pemanfaatan teknologi IoT dalam mendukung tujuan bisnis dan proses operasional pada core process perusahaan trucking di Indonesia, serta memberikan rekomendasi enterprise architecture sesuai TOGAF yang dapat diimplementasikan pada core process bisnis trucking di Indonesia. Rekomendasi enterprise architecture divisualisasikan melalui archimate, sehingga dapat dengan mudah dipahami dan diadptasi oleh pelaku usaha bisnis trucking atau pemerintah.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".