Analisis Kinerja IPAL PT X Jawa Timur dalam Menurunkan Parameter Pencemar Menggunakan Pendekatan Water Quality Index (WQI)
Bibliographic record
Abstract
The plastic recycling industry generates wastewater that poses a potential threat to aquatic environments if not managed optimally. Performance evaluation of Wastewater Treatment Plants (WWTP) is typically conducted partially by comparing outlet parameters against effluent standards, often failing to depict water quality conditions holistically. Therefore, this study employs the Water Quality Index (WQI) approach using the Canadian Council of Ministers of the Environment (CCME) method to evaluate the performance of PT X's WWTP in East Java. This research utilized secondary data from inlet and outlet wastewater quality tests over 23 months (January 2024–November 2025), covering 10 parameters. Analysis was conducted by calculating removal efficiency and determining factors F1 (scope), F2 (frequency), and F3 (amplitude) as the basis for the WQI-CCME calculation. The results indicate that the WWTP achieved high efficiency (>80%) in reducing dominant parameters such as TSS, TDS, BOD, and COD. However, violations of effluent standards were still observed in certain parameters. The obtained WQI-CCME value was 69,30, categorized as "Fair" with a moderate level of violation. These findings demonstrate that although the WWTP meets regulatory standards, the WQI-CCME approach provides a more comprehensive assessment of performance. Optimization of advanced treatment units is recommended to improve effluent quality and sustainable WWTP performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".