PREDIKSI CEPAT KUALITAS AIR MENGGUNAKAN LPAS (LASER PHOTO-ACOUSTIC SPECTROSCOPY) DENGAN MENERAPKAN METODE KOREKSI CUTTING EDGE FILTERING

Autor: Ichwana Ramli, Agus Arip Munawar, Khairul Abdi Ruslana
Jazyk: indonéština
Rok vydání: 2021
Předmět:
Zdroj: Jurnal Teknik Pertanian Lampung, Vol 10, Iss 2, Pp 220-227 (2021)
Druh dokumentu: article
ISSN: 2302-559X
2549-0818
DOI: 10.23960/jtep-l.v10.i2.220-227
Popis: Laboratory analysis needs a long time and expensive to determine water quality. A tool that can predict water quality by capturing sound vibrations generated by collisions between water samples and light is LPAS (Laser Photo-Acoustic Spectroscopy). To process the data acquired by LPAS, spectrum correction is needed to eliminate data errors when making acquisitions on water samples. The correction method used in this research is the cutting edge filtering correction method. The regression model that can be used is the PLSR (Partial Least Square Regression) regression model. This research was conducted in the Instrumentation and Energy Laboratory, Agricultural Engineering Study Program, Faculty of Agriculture, Syiah Kuala University. Water sample analysis was carried out at the Laboratory of the Industrial Research and Standardization Center (BARISTAND) Banda Aceh. This study used 4 monitoring wells within the TPA (Final Disposal Site) and 4 samples were taken from community wells outside the scope of TPA Gampong Jawa, Banda Aceh City. The results of this study indicate the parameters (temperature, turbidity, Ph, TSS, DO, BOD and Nitrate) are predicted to be in the frequency range 4000 - 10,000 cm-1. Raw spectrum data for pH and Nitrate (NO3-) parameters produce better data than the spectrum data for cutting edge filtering correction methods while cutting edge filtering spectrum data for temperature, turbidity, TSS, DO and BOD-5 parameters are better than spectrum raw data. This study also shows that the cutting edge filtering correction method is able to cut boundaries and compress the spectrum so that it can provide data limits on the spectrum so that the PLSR method can be applied to predict water quality.
Databáze: Directory of Open Access Journals
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