Pemetaan Proses Bisnis dengan Pendekatan Klasifikasi Proses CIMOSA: Studi Kasus Perusahaan Pengelola Kawasan Industri
Bibliographic record
Abstract
A business process map is said to be an effective tool to manage a firm value chain.Yet, the absence of business maps in the firms was frequently found.Some of the reasons behind this absence include an overwhelming large number of available languages with their complexity and the large amount of prcess-related data to construct the map while business practitioners have already been burdened by job routines.Offering a simple approach, this paper aims to map business processes using CIMOSA process classification framework.The mapping employed process reasoning and expert judgement from the observed company.An extensive use of the company internal data, interview, and FGD with stakeholders dominated the process mapping.Through iterative verification and validation, a complete business process map has been resulted for the case study.Relationships between process groups within CIMOSA process classification were also proposed.The result suggested CIMOSA process classification was proven to be applicable for nonmanufacturing based company, yet it is very practical for business process mapping at a certain level of detail.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".