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Model kebijakan strategis pengelolaan lingkungan kawasan industri (Studi Kasus Kawasan Industri Jababeka dan EJIP di Kabupaten Bekasi) Temmy Wikaningrum; Rijal Hakiki
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol. 9 No. 3 (2019): Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan
Publisher : Graduate School Bogor Agricultural University (SPs IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpsl.9.3.802-817

The model of key factors contributing to support sustainability of the environmental management in the industrial estate were studied. These factors were the result of the prior research of prospective analysis that classified as the leverage factors and the driving factors. These factors were studied by the Analytical Hierarchy Process (AHP) as the criteria and alternatives input to elaborate the priority in the model of environmental management. AHP matrices were analyzed by purposive expert in the depth interview procedure. The analysis result showed that the industrial estate manager is the most important as the firts actor in the model (63.7%), the second actor is the industrial company as the tenant of the first actor (25.8%), and third actor is the local government (10.5 %). The priority of the dimension of the model in a row were economic (38.1%), social (19.4%), institutional management (19.3%), ecology (13.8%) and technology (9.4%). For developing the model there were five driving factors that develop the alternatives of the policy for achieving the goal. The priority of the alternatives in a row were improving the quality of industrial wastewater (29.5%), improving wastewater quality of integrated wastewater treatment plant outlet of the estate manager facility (21.5%), obtaining the industrial water demand (19.1%), conducting 3R (reduce, reuse and recycle) program of hazardous waste, and maintaining and supporting employed people in condusive work environment. By AHP result which resulting the priority of actors, dimensions, and alternatives, the model was developed effectively.

The Utilization OpenCV to Measure the Water Pollutants Concentration Riri Asyahira Sariati Syah; Rijal Hakiki
Journal of Environmental Engineering and Waste Management Vol 6, No 2 (2021)
Publisher : President University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jenv.v6i2.1475

Abstract. Intensive water quality determination needs to be adjusted with technological developments to meet today's society's needs and increased water pollution due to urbanization. Therefore, early detection is essential for in site water quality determination and as a critical consideration in making health and environmental decisions. OpenCV is a library programming feature for Computer Vision which focuses on extracting information from images in real-time, this can be considered to be potential to measure the pollutant concentration. Objectives: This study identify the potential of colorimetry analysis method by using OpenCV as an alternative method for pollutant concentration measurement. Method and results: First stage, this study collecting the data of NH3 phenate and Pt-Co CU from the spectrophotometer. The first stage also was including the development of an OpenCV code. Then, the data was collected were processed to get the concentration of NH3 and Pt-Co both using OpenCV and spectrophotometer; factors that influence the Pt-Co sample image measurement process by using OpenCV-Python was analyzed too. Then in the analysis stage, the result of the two measurement method was tested by statistic determine its significant difference. The conclusion found whether OpenCV could be potential to measure the pollutant concentration or not. Conclusion: the OpenCV has potential to be use as alternative colorimetry measurement method to determine water pollutant as there is no significant difference in the spectrophotometric method results and the results from OpenCV for Pt-Co sample. Meanwhile, in this study found that the result of NH3 from spectrophotometer is nonlinear different with from OpenCV that is linear. Thus, further research is needed to test the validity of OpenCV method. The factor influence of measurement using OpenCV code is when determining the Region of Interest (ROI) and determining the pixel values for the normalized box filter

Development of Multiple Linear Regression Model to Predict COD Concentration based on West Tarum Canal Surface Water Quality Data Julio Putra David; Rijal Hakiki
Journal of Environmental Engineering and Waste Management Vol 6, No 1 (2021)
Publisher : President University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33021/jenv.v6i1.1416

Abstract. COD level indicates the organic matter pollution in water. A predictive analysis, such as Multiple Linear Regression, could be an option to make the COD measurement more effective. Objectives:This research aims to determine the parameter that can predict COD concentration using correlation analysis and develop a Multiple Linear Regression Model for predictive analysis on COD level in the West Tarum Canal surface water. Method and results: The correlation analysis is done in Microsoft Excel using the Pearson Product Moment Correlation Analysis. The water quality dataset is inputted to the R Studio and made the MLR model. The model is validated using t-Test. The result showed that all models in all intake points are not showing good prediction results, and the predictors showed no effect on the COD level. Conclusion: The Multiple Linear Regression is not a fit tool for predicting the COD in the West Tarum Canal surface water. Abstract[rh1] . COD level indicates the organic matter pollution in water. A predictive analysis, such as Multiple Linear Regression, could be an option to make the COD measurement more effective. Objectives[rh2] :This research aims to determine the parameter that can predict COD concentration using correlation analysis and develop a Multiple Linear Regression Model for predictive analysis on COD level in the West Tarum Canal surface water. Method and results[rh3] : The correlation analysis is done in Microsoft Excel using the Pearson Product Moment Correlation Analysis. The water quality dataset is inputted to the R Studio and made the MLR model. The model is validated using t-Test. The result showed that all models in all intake points are not showing good prediction results, and the predictors showed no effect on the COD level. Conclusion[rh4] : The Multiple Linear Regression is not a fit tool for predicting the COD in the West Tarum Canal surface water.

MIKROALGA SEBAGAI BIOINDIKATOR KUALITAS AIR PERMUKAAN Studi Awal : Hubungan Antara Konsentrasi Pigmen dan Berat Kering dalam Penentuan Kandungan Mikroalga Pada Sampel Air Artifisial Rijal Hakiki
Journal of Environmental Engineering and Waste Management Vol 1, No 1 (2016)
Publisher : President University

Abstract: Microalgae is the one of aquatic organism wich can be a bioindicators for surface water quality. Chlorophyll contain in a microalgae cell can be measured to know the abundance of microalga in a water body. Dry-weight biomass measurements is another method that can be used to know the abundance of microalga in a water body. both method have advantages and disadvantanges of each. In chlorophyll concentration measurements, the present of other compounds that can absorb light spectrum at measurements wavelength (Strickland and Parsons use 665 nm, 645 nm and 630 nm) result absorbance value higher than it should be. Turbidity level result by suspended particle content being a problem for dry-weight biomass measurements. Dry-weight biomass determination based on the approximation of chlorophyll content measurements was studied in this research. The Results of simple regression analysis showed that there is a fairly strong positive correlation between chlorophyll content and dry-weight biomass (Ra = 0.870), which has the tendency to follow the linear regression equation y = 302,35x + 17,121. Dry-weight determination based on approximation of clorophyll content can be applied to the sample of water that has suspended particle content tend to be constant and inert (did not produce subtances that can react with organic solvent when chlorophyll extraction process occurred). Based on the processed datas, it can be conclude that the influence of another suspended particle content in a sample of water is not statistically significant.

Turbidity Level Prediction Based on Suspended Particle Counting Through Image Processing Approach Temmy Wikaningrum; M. Galang Alvasa; Yandes Panelin; Rijal Hakiki
Jurnal Serambi Engineering Vol 6, No 1: Januari 2021
Publisher : Fakultas Teknik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jse.v6i1.2593

Monitoring of pollutant concentrations in surface water becomes a concern, considering the utilization of surface water as the raw water for drinking water treatment plants (WTP). The fluctuation of pollutant concentrations in surface water can affect the performance of WTP. This research was conducted to assess the potential for turbidity level prediction based on the calculation of the number and surface area of suspended particles through a digital image processing approach. Measurements of the amount and surface area were carried out in the form of laboratory-scale experiments using the open source software ImageJ 1.46r. The algorithm in ImageJ can convert pixels into a number “value” and surface area through a series of digital image processing steps, henceforth compared with the existing measurement method. The results showed that there was a strong correlation between the number of particles and the concentration of formazine suspension (r = 0.9821), but does not apply to the surface area. Referring to the results of laboratory experiments, it can be concluded that the approach to measure the number of suspended particles can be the basis for predicting the turbidity level in the turbidity range 100-800 NTU, but does not apply to the turbidity range 0.02-20 NTU.

Turbidity Level Prediction Based on Suspended Particle Counting Through Image Processing Approach Temmy Wikaningrum; M. Galang Alvasa; Yandes Panelin; Rijal Hakiki
Jurnal Serambi Engineering Vol 6, No 1: Januari 2021
Publisher : Fakultas Teknik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jse.v6i1.2593

Monitoring of pollutant concentrations in surface water becomes a concern, considering the utilization of surface water as the raw water for drinking water treatment plants (WTP). The fluctuation of pollutant concentrations in surface water can affect the performance of WTP. This research was conducted to assess the potential for turbidity level prediction based on the calculation of the number and surface area of suspended particles through a digital image processing approach. Measurements of the amount and surface area were carried out in the form of laboratory-scale experiments using the open source software ImageJ 1.46r. The algorithm in ImageJ can convert pixels into a number “value” and surface area through a series of digital image processing steps, henceforth compared with the existing measurement method. The results showed that there was a strong correlation between the number of particles and the concentration of formazine suspension (r = 0.9821), but does not apply to the surface area. Referring to the results of laboratory experiments, it can be concluded that the approach to measure the number of suspended particles can be the basis for predicting the turbidity level in the turbidity range 100-800 NTU, but does not apply to the turbidity range 0.02-20 NTU.

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