The Study of Liquid Fertilizer from Ground Coffee

<span lang="EN-US">Nowadays, coffee is one of needs among metropolitan people. This lifestyle impact to increasing waste along coffee production, one of them is coffee ground. Coffee ground </span><span lang="EN-GB">has toxic properties to the environment such as caffeine, tannins, and polyphenols. While other chemicals contain on coffee ground are 2.28% nitrogen, 0.06% phosphorus, 0.6% potassium, which means has a good impact to the soil. In existing condition, coffee gr<span lang="EN-US">Nowadays, coffee is one of needs among metropolitan people. This lifestyle impact to increasing waste along coffee production, one of them is coffee ground. Coffee ground </span><span lang="EN-GB">has toxic properties to the environment such as caffeine, tannins, and polyphenols. While other chemicals contain on coffee ground are 2.28% nitrogen, 0.06% phosphorus, 0.6% potassium, which means has a good impact to the soil. In existing condition, coffee ground is commonly used to, biodiesel and bioethanol or by direct used to the soil. The processing of coffee ground to be liquid organic fertilizer by using bio-activator are considered to substitute the direct used, it will improve the quality of soil. </span><strong><span lang="IN">Objectives:</span><span lang="IN"> </span></strong><span lang="EN-US">The objective of this research is to study of liquid fertilizer from coffee ground, whether comply or not to the standard regulation </span><span lang="EN-GB">Ministerial Decree of Agriculture of the Republic of Indonesia Number 261/2019</span><span lang="EN-US">.</span><span lang="EN-US"> </span><strong><span lang="IN">Method and results:</span></strong><span lang="IN"> </span><span lang="EN-US">This research use a coffee ground from <em>arabica</em> and <em>robusta</em> coffee and applied 2(two) different dilution by repeated 2x2 on one time </span><span lang="EN-US">by using EM4 as bio-activator. The pH and temperature for 4 experiment measured daily and has an average on 4.8 and 31-degree celcius for 10 days. Nitrogen, Phosphorus, and phosphor measured in the end of experiment has average results on 0.18, 0.17, 0.04, sequentially. </span><strong><span lang="IN">Conclusion:</span></strong><span lang="IN"> </span><span lang="EN-US">The result for chemical parameter; Nitrogen, Phosphorus, and phosphor has not comply to the standard of liquid organic fertilizer by Indonesia Government.</span>ound is commonly used to, biodiesel and bioethanol or by direct used to the soil. The processing of coffee ground to be liquid organic fertilizer by using bio-activator are considered to substitute the direct used, it will improve the quality of soil. </span><strong><span lang="IN">Objectives:</span><span lang="IN"> </span></strong><span lang="EN-US">The objective of this research is to study of liquid fertilizer from coffee ground, whether comply or not to the standard regulation </span><span lang="EN-GB">Ministerial Decree of Agriculture of the Republic of Indonesia Number 261/2019</span><span lang="EN-US">.</span><span lang="EN-US"> </span><strong><span lang="IN">Method and results:</span></strong><span lang="IN"> </span><span lang="EN-US">This research use a coffee ground from <em>arabica</em> and <em>robusta</em> coffee and applied 2(two) different dilution by repeated 2x2 on one time </span><span lang="EN-US">by using EM4 as bio-activator. The pH and temperature for 4 experiment measured daily and has an average on 4.8 and 31-degree celcius for 10 days. Nitrogen, Phosphorus, and phosphor measured in the end of experiment has average results on 0.18, 0.17, 0.04, sequentially. </span><strong><span lang="IN">Conclusion:</span></strong><span lang="IN"> </span><span lang="EN-US">The result for chemical parameter; Nitrogen, Phosphorus, and phosphor has not comply to the standard of liquid organic fertilizer by Indonesia Government.</span>

The Utilization OpenCV to Measure the Water Pollutants Concentration

<strong>Abstract. </strong>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. <strong>Objectives:</strong> This study identify the potential of colorimetry analysis method by using OpenCV as an alternative method for pollutant concentration measurement<strong>. Method and results:</strong> 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. <strong>Conclusion:</strong> 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

The Study of Tong Composter in Produced Liquid Organic Fertilizer

<strong>Abstract. </strong>Inadequate processing of waste in household areas has resulted in many people throwing the waste anywhere. Waste generation can be minimized by using the composting method. There are many methods of composting organic waste; one of them is the <em>Tong</em> Composter. <em>Tong</em> Composter is a composting method which in the process uses an EM4 bio-activator. <strong>Objectives:</strong> This research objective is to determine whether the liquid organic fertilizer parameters produced comply with the standard of Ministerial Decree of Agriculture No. 261 of 2019. <strong>Method and results:</strong> This research using the experimental method with two treatments, EM4 dosage (60 ml and 70 ml) and fermentation time (10 days and 20 days). A dose of EM4 in treatment 1 is 60 ml, and treatment 2 is 70 ml; each treatment was analyzed on the 10^th day. For treatment 3 and 4, a dose of EM4 in treatment 3 is 60 ml, and in treatment 4 is 70 ml; each treatment was analyzed on the 20^th day. Then from the liquid organic fertilizer produced, several parameters were analyzed at PT. Sucofindo Cibitung, such as Nitrogen, P₂O₅, and K₂O parameter. Meanwhile, the pH will be measured in the Environmental Engineering Laboratory of President University. The laboratory experiment results from pH, Nitrogen, P­₂O₅, and K₂O will be tested using the t-test. With the hypothesis (H_O: µ sample ≥ minimum value of macro-nutrient and pH parameter from Ministerial Decree of Agriculture of the Republic of Indonesia Number 261 of 2019). According to the t-test result, the null hypothesis is rejected because Nitrogen, P₂O₅, and K₂O in all parameters are less than 2%. The null hypothesis is accepted for pH because in all parameters is higher than or equal to 4. <strong>Conclusion:</strong> The t-test result showed that only the pH was complied with the standard, while the Nitrogen, P­₂O₅, and K₂O parameters have not complied with the standard of Ministerial Decree of Agriculture No. 261 of 2019.

Optimization of Coagulation and Flocculation in Concrete Wastewater of Precast Industry

<p><strong>Abstract.</strong> The population increase until the end of 2019 reached 267 million. This is in line with developments that support activities. This is inseparable from the actions of one of the precast concrete companies PT.WB Precast Plant Karawang in supporting development that produces waste in the form of wastewater. The parameter measured as a reference in carrying out processing is the Minister of Health Regulation Number 32 of 2017. This study's method refers to turbidity as a physical parameter, namely 25NTU and hardness total as a chemical parameter, which is 500 mg / l. CaCO3. <strong>Objectives:</strong><strong> </strong>The purpose of this study was to determine the optimization of the use of PAC as a coagulant and flocculant to reduce the turbidity and total hardness and determine the optimum detention time of flocculation.<strong> </strong><strong>Method and results:</strong><strong> </strong>The sampling method in this study is observation. The population in this study is concrete wastewater with parameter values that are not in accordance with the standard.<strong> Conclusion:</strong> By using PAC 10% the optimum doses is 140 ppm, and the optimum detention time of flocculation is 5 minute that can reduce the turbidity from 275 NTU to be 11.615 NTU or 95.7% and can reduce total hardness from 948.75 mg/l CaCO3 to be 491.25 mg/l CaCO3 or 48.2%.</p>

The evaluation of hydraulic parameters in water distribution network using Epanet 2.0 (Case study: Comoro, Zone I, Timor-Leste)

<strong>Abstract. </strong>In Timor-Leste, supplying clean water is the responsibility of Serviço de Água e Saneamento (SAS). The study area is in Zone I, locates in Comoro, Dili. The service area uses groundwater as the water source with two types of reservoirs; rectangular and cylinder. <strong>Objectives</strong>: This study aims to evaluate the existing condition of the water distribution network (WDN) in Zone I by using the Epanet 2.0 software. The development of the WDN begins with projecting the population with 10-year planning using the Geometric, Arithmetic, and Exponential methods. <strong>Method and results</strong>: The collection data is through observations in the study area as primary data. Secondary data is collecting SAS data, such as the map of water pipelines, piping data, pump data, reservoir data, and consumers' numbers. Processing data is the use of MS. Excel to calculate the population size, water demand, and identifying the pipelines of Zone I through QGIS. Followed by Epanet 2.0, with WDN data, the result is then evaluated with the Regulation of the Minister of Public Works No. 18 Year 2007 (No.18/PRT/M/2007). <strong>Conclusion</strong>: The results show that the WDN in Zone I have met the pressure requirement from No.18/PRT/M/2007 with an average flow of 19.57 litres/second. However, velocity still needs improvements since it did not meet the criteria; it suggests adding pump stations and reservoirs where the velocity did not meet the criteria. For the projection year in 2030, the estimated population is 26,057, with an average daily total water requirement of 48.46 litres/second.

Development of Multiple Linear Regression Model to Predict COD Concentration based on West Tarum Canal Surface Water Quality Data

<strong><span style="font-size: 10.0pt; font-family: 'Calibri Light',sans-serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Arial; mso-ansi-language: EN-US; mso-fareast-language: AR-SA; mso-bidi-language: AR-SA;" lang="EN-US">Abstract. </span></strong><span style="font-size: 10.0pt; font-family: 'Calibri Light',sans-serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Arial; mso-ansi-language: EN-US; mso-fareast-language: AR-SA; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold;" lang="EN-US">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. <strong>Objectives:</strong></span><span style="font-size: 10.0pt; font-family: 'Calibri Light',sans-serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Arial; mso-ansi-language: EN-US; mso-fareast-language: AR-SA; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold;" lang="EN-US">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. <strong>Method and results:</strong> 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. <strong>Conclusion:</strong> The Multiple Linear Regression is not a fit tool for predicting the COD in the West Tarum Canal surface water. </span><a style="mso-comment-reference: rh_1; mso-comment-date: 20201202T0302; mso-comment-done: yes;"><strong style="mso-bidi-font-weight: normal;"><span style="font-size: 10.0pt; font-family: 'Calibri Light',sans-serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Arial; mso-ansi-language: EN-US; mso-fareast-language: AR-SA; mso-bidi-language: AR-SA;" lang="EN-US">Abstract</span></strong></a><span class="MsoCommentReference"><span style="font-size: 8.0pt; font-family: 'New York',serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: 'New York'; mso-ansi-language: EN-US; mso-fareast-language: AR-SA; mso-bidi-language: AR-SA;" lang="EN-US"><!--[if !supportAnnotations]--><a id="_anchor_1" class="msocomanchor" name="_msoanchor_1" href="file:///J:/THESIS/THESIS%20DOCUMENTS/JOURNAL%20SEMINAR/SUBMIT%20JENV/Julio%20Putra%20David_Development%20of%20Multiple%20Linear%20Regression%20Model%20to%20Predict%20COD%20Concentration%20based%20on%20West%20Tarum%20Canal%20Surface%20Water%20Quality%20Data.docx#_msocom_1"></a>[rh1]<!--[endif]--><span style="mso-special-character: comment;"> </span></span></span><strong style="mso-bidi-font-weight: normal;"><span style="font-size: 10.0pt; font-family: 'Calibri Light',sans-serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Arial; mso-ansi-language: EN-US; mso-fareast-language: AR-SA; mso-bidi-language: AR-SA;" lang="EN-US">. </span></strong><span style="font-size: 10.0pt; font-family: 'Calibri Light',sans-serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Arial; mso-ansi-language: EN-US; mso-fareast-language: AR-SA; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold;" lang="EN-US">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. <span style="mso-spacerun: yes;"> </span><a style="mso-comment-reference: rh_2; mso-comment-date: 20201202T0249; mso-comment-done: yes;"><strong>Objectives</strong></a></span><span class="MsoCommentReference"><span style="font-size: 8.0pt; font-family: 'New York',serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: 'New York'; mso-ansi-language: EN-US; mso-fareast-language: AR-SA; mso-bidi-language: AR-SA;" lang="EN-US"><!--[if !supportAnnotations]--><a id="_anchor_2" class="msocomanchor" name="_msoanchor_2" href="file:///J:/THESIS/THESIS%20DOCUMENTS/JOURNAL%20SEMINAR/SUBMIT%20JENV/Julio%20Putra%20David_Development%20of%20Multiple%20Linear%20Regression%20Model%20to%20Predict%20COD%20Concentration%20based%20on%20West%20Tarum%20Canal%20Surface%20Water%20Quality%20Data.docx#_msocom_2"></a>[rh2]<!--[endif]--><span style="mso-special-character: comment;"> </span></span></span><strong><span style="font-size: 10.0pt; font-family: 'Calibri Light',sans-serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Arial; mso-ansi-language: EN-US; mso-fareast-language: AR-SA; mso-bidi-language: AR-SA;" lang="EN-US">:</span></strong><span style="font-size: 10.0pt; font-family: 'Calibri Light',sans-serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Arial; mso-ansi-language: EN-US; mso-fareast-language: AR-SA; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold;" lang="EN-US">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. <a style="mso-comment-reference: rh_3; mso-comment-date: 20201202T0301; mso-comment-done: yes;"><strong>Method and results</strong></a></span><span class="MsoCommentReference"><span style="font-size: 8.0pt; font-family: 'New York',serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: 'New York'; mso-ansi-language: EN-US; mso-fareast-language: AR-SA; mso-bidi-language: AR-SA;" lang="EN-US"><!--[if !supportAnnotations]--><a id="_anchor_3" class="msocomanchor" name="_msoanchor_3" href="file:///J:/THESIS/THESIS%20DOCUMENTS/JOURNAL%20SEMINAR/SUBMIT%20JENV/Julio%20Putra%20David_Development%20of%20Multiple%20Linear%20Regression%20Model%20to%20Predict%20COD%20Concentration%20based%20on%20West%20Tarum%20Canal%20Surface%20Water%20Quality%20Data.docx#_msocom_3"></a>[rh3]<!--[endif]--><span style="mso-special-character: comment;"> </span></span></span><strong><span style="font-size: 10.0pt; font-family: 'Calibri Light',sans-serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Arial; mso-ansi-language: EN-US; mso-fareast-language: AR-SA; mso-bidi-language: AR-SA;" lang="EN-US">:</span></strong><span style="font-size: 10.0pt; font-family: 'Calibri Light',sans-serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Arial; mso-ansi-language: EN-US; mso-fareast-language: AR-SA; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold;" lang="EN-US"> 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. <a style="mso-comment-reference: rh_4; mso-comment-date: 20201202T0301; mso-comment-done: yes;"><strong>Conclusion</strong></a></span><span class="MsoCommentReference"><span style="font-size: 8.0pt; font-family: 'New York',serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: 'New York'; mso-ansi-language: EN-US; mso-fareast-language: AR-SA; mso-bidi-language: AR-SA;" lang="EN-US"><!--[if !supportAnnotations]--><a id="_anchor_4" class="msocomanchor" name="_msoanchor_4" href="file:///J:/THESIS/THESIS%20DOCUMENTS/JOURNAL%20SEMINAR/SUBMIT%20JENV/Julio%20Putra%20David_Development%20of%20Multiple%20Linear%20Regression%20Model%20to%20Predict%20COD%20Concentration%20based%20on%20West%20Tarum%20Canal%20Surface%20Water%20Quality%20Data.docx#_msocom_4"></a>[rh4]<!--[endif]--><span style="mso-special-character: comment;"> </span></span></span><strong><span style="font-size: 10.0pt; font-family: 'Calibri Light',sans-serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Arial; mso-ansi-language: EN-US; mso-fareast-language: AR-SA; mso-bidi-language: AR-SA;" lang="EN-US">:</span></strong><span style="font-size: 10.0pt; font-family: 'Calibri Light',sans-serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Arial; mso-ansi-language: EN-US; mso-fareast-language: AR-SA; mso-bidi-language: AR-SA; mso-bidi-font-weight: bold;" lang="EN-US"> The Multiple Linear Regression is not a fit tool for predicting the COD in the West Tarum Canal surface water. </span><div style="mso-element: comment-list;"><!--[if !supportAnnotations]--><hr class="msocomoff" align="left" size="1" width="33%" /><!--[endif]--><div style="mso-element: comment;"><!--[if !supportAnnotations]--><div id="_com_1" class="msocomtxt"><!--[endif]--><span style="mso-comment-author: rhakiki;"><!--[if !supportAnnotations]--></span><p class="MsoCommentText"> </p></div></div><div style="mso-element: comment;"><div id="_com_4" class="msocomtxt"><!--[if !supportAnnotations]--></div><!--[endif]--></div></div>

GOING PAPERLESS CONCEPT IMPLEMENTATION AT SENIOR HIGH SCHOOL IN BEKASI, INDONESIA

An important reason for the rise of the paperless learning environment is that society is moving towards a green direction. At the same time, the advancement of technology and how people’s consumption of scarce resources affects digital generation and the understanding of the environment in the future have attracted more and more attention. The objective of this study is to investigate how capable, and available the students are at implementing paperless classrooms with substitutes such as the digital mode in learning activities. This quantitative descriptive research uses 108 participants as a sample through a questionnaire to collect the data. The student from Computer Network Engineering major is considered as the most suitable and related to one of the variables, namely digital literacy, where students more often use technology as learning media and solutions for reducing paper usage. The data were analysed using SPSS v 22. The results of this study found that there are significant influences from Environmental Awareness, digital literacy, and habits as factors in students’ readiness to apply this concept. The coefficient of determination shows that the Environmental Awareness (X1), Digital Literacy (X2), and Habit (X3) effected the Student’s Readiness (Y) to Implement Paperless Concept of 41.3%. The rest, 58.7% will be explained by other factors that will not be discussed in this study.

Waste Reduction Potential in SAMTAMA Program Implementation (Study Case: RT 10/RW 03 Cempaka Putih Timur)

<p>Waste is one of the big problems often faced by metropolitan cities like DKI Jakarta. The Jakarta Environment Agency's research stated that DKI Jakarta produces 7500-tons of waste per day with 60.5% coming from residential. Therefore, the DKI Jakarta government initiated the Sampah Tanggung Jawab Bersama (SAMTAMA) to mobilize residential people to manage waste from its source at certain locations as a pilot, one of locations is RT 10 / RW 03 Cempaka Putih Timur. RT 10 / RW 03 Cempaka Putih Timur is the RT that is considered optimal in implement the SAMTAMA program. Based on this, the objectives of this study are: (1) to find the existing condition of SAMTAMA program; (2) to measure the waste generation, composition, and reduction potential in the implementation of SAMTAMA program. The method implemented in measuring using SNI 19-3964-1994 which is carried out for 8 consecutive days. Waste composition is classified based on SNI 19-3964-1995, supplemented with components of B3 waste and diapers. The results of the analysis show that the average of waste generation is 14.99 kg/day where the treated waste is 11.36 kg/day and the untreated or residual waste is 3.63 kg/day with the largest composition is food waste with 10.66 kg/day (71%). With SAMTAMA Program implementation, the waste potential reduction can reach 0.019 kg/day/person.</p>