Measuring Plastic Wastewater Quality Using the Internet of Things
Bibliographic record
Abstract
This study evaluates the water quality in the plastic waste management process at CV. AAWW Perdana Usaha, located in Bogor, West Java, Indonesia.The sensors used to measure total dissolved solids (TDS) and turbidity operate using PPM (parts per million) and NTU (nephelometric turbidity units), respectively.To assess water quality during plastic waste processing, a WiFi-enabled Arduino Mega 2560 (ESP8266) microcontroller is connected to the TDS and turbidity sensors.The research methodology follows a structured approach comprising planning, analysis, design, implementation, and testing phases.Utilizing the Internet of Things (IoT), the study presents a system that monitors water quality in real time during the plastic waste treatment process.The results and conclusions of this study indicate that the wastewater quality measurement system has been successfully developed.The system enables classification of wastewater into "clean" and "contaminated" categories following regulation No. 78/M-IND/PER/11/2016 issued by the Indonesian Ministry of Industry.Furthermore, it facilitates easy water quality monitoring through a web-based interface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".