Critical Path Analysis Using Microsoft Project 2016 on the Implementation Schedule of Residential House Construction Projects
Bibliographic record
Abstract
A project requires good planning, careful execution and effective and efficient use of resources. The implementation of the development of a construction project consists of a series of activities that are interrelated with one another. This is where the importance of planning and scheduling projects properly to facilitate implementation in the field and development can be completed on time according to schedule. The existence of obstacles or obstacles that will occur in the process of implementing a construction project can be seen and predicted from the level of urgency of the work items to be carried out, the analysis that will be used to predict the constraints or obstacles that may occur is by analyzing the critical path of the project implementation schedule construction. The use of Microsoft Project is very effective in analyzing data and determining critical paths. Based on the critical path analysis that has been carried out on the construction project schedule for the construction of residential houses, it is found that several work sub-items are on the critical path, especially in preparatory work and structural work, which means that the work sub-item in this work must pay close attention to the implementation process both in terms of resource readiness. human and equipment.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".