PENERAPAN NETWORK PLANNING PADA PROYEK PEMBANGUNAN PERUMAHAN MUTIARA RESIDENCE DI DESA PENGAMBENGAN KABUPATEN JEMBRANA
Bibliographic record
Abstract
ABSTRACT The increasing number of construction companies in Indonesia poses a challenge for companies to improve the effectiveness and efficiency of resource management in order to excel among competitors. The purpose of this study is to determine whether the implementation of Network Planning can improve efficiency and effectiveness in the allocation of time and cost on the construction project. The research method is a case study to address the problem at Putra Dewata PT Karya Tama concerning network planning of Mutiara Residence Housing project. Data analysis technique used is Critical Path Method. The results show that the project completion time is 142.5 days, 14.5 days faster than the calculation result by the company with Gantt Chart method. This will provide benefits in terms of cost of completion of the project, where cost efficiencies that can be gained is Rp. 20,807,500. Keywords: network planning, critical path method, critical path.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".