Decision Support System at PDAM Tirta Sari City of Binjai Using the Decision Tree Method in Determining Maintenance Actions for Clean Water Distribution Networks
Bibliographic record
Abstract
Water is a very important need for human survival, without water there would be no life on earth. Every region should have clean water service management for community needs, especially in Binjai City which is managed by PDAM Tirta Sari, a company owned by the Binjai City government. Problems that often occur during this time are that water distributed to residents sometimes experiences problems such as difficulty in flowing water to residents' homes, leaks in distribution pipes, dirty water or smelly water if there is heavy rain. Based on the problems above, it is necessary to observe the causes of problems that occur in the distribution of clean water in the city of Binjai. As the community grows and the increasing number of people requesting the installation of new drinking water meters causes pressure on water distribution in the Binjai area, there is a problem of not being able to distribute water normally, therefore it is necessary to examine which areas need to be improved so that water distribution can be even by applying algorithms. DecisionTree. Based on the problem of maintaining the clean water distribution network, it requires a decision-making method that is able to accommodate complex problems, which provides a value to support a decision. One method that can be used is a decision tree. This method is a method that tries to find discrete approximation functions, and was built using the ID3 algorithm (Interative Dychotomizer Version 3), and for ranking using risk analysis. The system is designed using the PHP programming language with a MySQL database. From the results of the decision tree above, it is known that node 1.1 is routine inspection, routine maintenance is carried out to check problems that occur in the field with the aim of ensuring that water distribution can flow normally to residents' homes, node 1.2 monitors water quality, node 1.3 pipe maintenance, node 1.4 changes equipment and nodes 1.5 checking water pressure, where the PDAM will check the water pressure if it is known that the water in the reservoir has decreased, then it is ensured that the distributed water pressure reaches the minimum standard so that the water can reach residents' homes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".