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ASSESSMENT OF THE OPERATOR'S ABILITY TO PRODUCE QUALITY DRINKING WATER

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International Journal of Civil Engineering and Technology (IJCIET)
Volume 10, Issue 04, April 2019, pp. 1863-1869, Article ID: IJCIET_10_04_195
Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=10&IType=04
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication
Scopus Indexed
ASSESSMENT OF THE OPERATOR'S ABILITY
TO PRODUCE QUALITY DRINKING WATER
Nieke Karnaningroem
Department of Environmental Engineering, Faculty of Civil, Environmental, and Geo
Engineering, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia
Geodita Woro Bramanti
Department of Business Management, Faculty of Business Management and Technology,
Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia
Nur Wakhidah Mayang Sari, Rosalina Eka Praptiwi and Sarwoko Mangkoedihardjo
Department of Environmental Engineering, Faculty of Civil, Environmental, and Geo
Engineering, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia
ABSTRACT
Refill drinking water depot (DAMIU) is a business that aims to middle and lower
class because of its affordable price. The laboratory analysis of refill drinking water
quality showed that the total coliform had exceeded the threshold based on the Ministry
of Health Regulation No. 492/2010. Direct observation in the depots resulted in a
general conclusion that poor water quality was produced due to incapability the
operators in operating water treatment units. The capability of DAMIU operator was
divided into 3 latent variables, which are knowledge, attitude, and behavior. Those
variables observed by 17 indicator variables described operator’s understanding about
refill drinking water regulations (knowledge), depots operation and maintenance
(behavior), and operator’s willingness to improve their depots performance (attitude).
Structural Equation Modeling conducted through validity test and correlation test.
Validity test result showed only 9 indicators were valid. Correlation test carried with
the valid indicators identified that correlation value of knowledge, behavior, and
attitude towards coliform contamination were 0,165; 0,151; and 0,374; respectively.
The priority of the improvement was determined by fishbone analysis which made based
on the valid indicators and adjusted by the correlation value between latent variables
from correlation test. The first problem to overcome was operator’s attitude, followed
by their knowledge, and last was their behavior.
Keywords: drinking water, partial least square, structural equation modeling, total
coliform
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1863
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Nieke Karnaningroem, Geodita Woro Bramanti, Nur Wakhidah Mayang Sari, Rosalina Eka
Praptiwi and Sarwoko Mangkoedihardjo
Cite this Article: Nieke Karnaningroem, Geodita Woro Bramanti, Nur Wakhidah
Mayang Sari, Rosalina Eka Praptiwi and Sarwoko Mangkoedihardjo, Assessment of
the Operator's Ability to Produce Quality Drinking Water. International Journal of
Civil Engineering and Technology, 10(04), 2019, pp. 1863-1869
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=04
1. INTRODUCTION
In Indonesia, there are two types of drinking water, the one produced from credible drinking
water company and small business that called refill drinking water depot (DAMIU). DAMIU
is a business that intended to people with low and middle class because of its affordable price,
although its quality has does not confirmed yet (Karnaningroem et al., 2017). It used spring
water as source of raw water and deliver it with water tank. The water treatment is divided into
two steps, which are filtration process and disinfection process.
Water
Storage
Sand
Filter
Activated
Carbon
Cartridge
Filter
Desinfection
Unit
Figure 1. Process flow diagram for DAMIU
The methods for disinfection commonly used were chlorination, ozonation, UV ray,
membrane filter, etc. (Xi et al., 2017). Drinking water quality has to fulfill the threshold stated
in regulation by Ministry of Health of Indonesia No. 492/2010. Good quality disinfection for
drinking water meant that the coliform bacteria was not exceeding standard, because it could
cause disease such as gastroenteritis, dysentery, diarrhea, and hepatitis (Khan et al., 2013).
The aim of this research was to analyze the effects of operator capability on drinking water
quality produced in DAMIU. The procedure was carried out with Structural Equation
Modeling-Partial Least Square (SEM-PLS). Structural equation models are a family of
multivariate statistical models that allow the analyst to estimate the effect and relationship
between multiple variables. They could be thought of as a variety of factorial analysis models
that allow the consideration of direct and indirect affects between factors (Dell’Olion et al.,
2018). The indicators were determined through validity test where its loading factor should be
≥ 0.5 to be valid. The relation of those valid indicators and the priority of control measures was
calculated on correlation test.
2. MATERIALS AND METHODS
The data derived from questionnaires with drinking water operator as the respondent (Samudro
and Mangkoedihardjo, 2006). The object of this research was represented by 30 DAMIU that
had been clustered.
2.1. Water Sampling
Water sample was collected from water storage and outlet of water treatment. The water quality
was determined by analyzing Total Dissolved Solid (TDS), turbidity, pH, color, and total
coliform in laboratory. The test result was used to confirm which parameter that exceed the
threshold based on the regulation.
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Assessment of the Operator's Ability to Produce Quality Drinking Water
2.2. Quantitative Data Analysis
Questionnaires data was analyzed using Likert scale. Likert scale items are created by
calculating a composite score (sum or mean) that analyzed at the interval measurement scale.
Descriptive statistics recommended for interval scale items include the mean for central
tendency and standard deviations for variability (Boone and Boone, 2012). In this research, the
scale range was created in 1-5, where 5 represented the best condition. The calculated data
would be used in validity and correlation test.
2.3. Structural Equation Modeling
The validity and correlation test was calculated using SEM with Smart-PLS software. There
were two types of variables, latent variables and observed variables. Latent variables
(constructs or factors) are variables that are not directly observable or measured. Latent
variables are indirectly observed or measured, and hence are inferred from a set of observed
variables that actually measured using tests, surveys, and so on (Schumacker and Lomax,
2010). Latent variables are distinguished into exogenous and endogenous. Exogenous latent
variables (independent variables): they “cause” fluctuations in the values if other latent
variables in the model. Endogenous latent variables (dependent variables) are influenced by
the exogenous variables in the model, either directly or indirectly (Byrne, 2016).
The operator capability was drawn by 3 latent variables, which were knowledge, attitude,
and behavior. Those variables measured by 17 observed variables that described operator
understanding about drinking water regulations (knowledge), how the operator did their job
(behavior), and operator willingness to improve their DAMIU performance (attitude). At the
same time, the water quality was described by water quality parameters that did not meet the
standard.
Figure 2. Measurement and Structural Model
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Nieke Karnaningroem, Geodita Woro Bramanti, Nur Wakhidah Mayang Sari, Rosalina Eka
Praptiwi and Sarwoko Mangkoedihardjo
3. RESULTS AND DISCUSSION
3.1. Drinking Water Quality
Water samples collected was analyzed in Environmental Quality Management Laboratory,
Department of Environmental Engineering, Institut Teknologi Sepuluh Nopember. The test
results shown below.
Figure 3. TDS
Figure 4. Turbidity
Figure 5. pH
Figure 6. Color
Figure 7. Total Coliform
Figures above illustrated the concentration of each parameters towards its standard.
Overall, 4 out of 5 parameters was within acceptable range. However, the total coliform was
founded in water sample which indicated that the water produced from DAMIU was not good
for consumption.
3.2. Validity Test
The questionnaire data is calculated according to Figure 2 to determine valid and invalid
indicator. An indicator was categorized as valid indicator if it had value ൒ 0,5 [6]. Invalid
indicator should be taken out of the model. Valid indicators that remain in the model is
recalculated to produced final loading factor value that be used in correlation test.
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Assessment of the Operator's Ability to Produce Quality Drinking Water
Table 1. Latent Variable and SEM Indicator
Variables
K1
K2
K3
K4
K5
K6
B1
B2
B3
B4
B5
B6
A1
A2
A3
A4
A5
Knowledge
Decree of the Ministry of Industry and
Trade No. 651/2004
Ministry of Health Regulation No. 43/ 2014
Ministry of Health Regulation No.
492/2010
Ministry of Health Regulation No.
736/2010
Treatment units in production process
Standard Operational Procedure (SOP)
Behavior
Participating in DAMIU socialization
Operating water treatment based on SOP
Controlled or monitored by the Health
Centers
Cleaning raw water reservoir
Operators hygiene
Microorganism remnants, insects, and rats
extermination with disinfectant
Attitude
Improve the water quality to meet the
standard
Participate in DAMIU management training
Establish and apply DAMIU SOP
Replace unsuitable components
Manage sanitation hygiene in DAMIU
location
Loading
Factor (Initial)
Decision
Loading Factor
(Recalculation)
0,690
Valid
0,829
0,754
Valid
0,970
0,670
Valid
0,930
0,788
Valid
0,837
-0,367
-0,345
Invalid
Invalid
0,526
-0,489
Valid
Invalid
-0,084
Invalid
0,657
0,543
Valid
Valid
0,028
Invalid
0,130
Invalid
0,521
-0,414
0,140
Valid
Invalid
Invalid
0,545
0,909
Valid
0,937
0,674
0,892
0,857
3.3. Correlation Test
The valid variables were calculated in correlation test and produced the correlation coefficient
which shows the relations between the latent variables in Table 2.
Table 2. Correlation Test Result
Variable
Attitude
Behavior
Knowledge
Attitude
Behavior
Knowledge
Refill Drinking Water
Quality
1,000
0,315
0,325
1,000
0,667
1,000
0,374
0,151
0,165
Refill Drinking Water
Quality
1,000
Sources: Analysis Results, 2018
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Nieke Karnaningroem, Geodita Woro Bramanti, Nur Wakhidah Mayang Sari, Rosalina Eka
Praptiwi and Sarwoko Mangkoedihardjo
Variable “attitude” is the most important variable for the “refill drinking water quality” (the
correlation coefficient is 0,374) and the less important is the “behavior” (the correlation
coefficient is 0,151). Therefore, in order to improve the refill drinking water quality as required,
the control measurement should focus on “attitude” first, and then “knowledge”, and
“behavior”. The highest correlation coefficient observed was between “behavior” and
“knowledge”. The procedure for improving “behavior” could concentrated on “knowledge”
(0,667).
Figure 8. Illustration of how latent variables and indicators affect refill drinking water quality
4. CONCLUSIONS
A hierarchy of the influence of the latent variables on refill drinking water quality can be
established using the structural equation modeling. The priority of the improvement was
determined based on the valid indicators and adjusted by the correlation value between latent
variables from correlation test. The first problem to overcome was operator’s attitude, followed
by their knowledge, and last was their behavior. The present data article overs implication for
the authorities who legalized and monitored DAMIU operation. Regulation that exist can be
modified to ensure that the DAMIU owner had been participated in DAMIU management
training. Furthermore, the monitoring frequencies of refill drinking water quality can be done
regularly according to the regulation.
ACKNOWLEDGMENT
The authors would like to acknowledge and express their sincere gratitude to the Ministry of
Research Technology and Higher Education, Indonesia for providing the financial support for
this study.
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REFERENCES
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Application, and Programing. Third Edition. New York: Routledge Taylor and Francis
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Dell’Olion, L., Ibeas, A., de Ona, J., de Ona, R. (2018). Public Transportation of Service
1st Edition. Elsevier, pp. 141.
Karnaningroem, N., Muzakky, A. Bramanti, G. W. (2017). Identifying the Risk of
Drinking Water Refill Stations in Surabaya Using Fishbone Method, Case Study in
Sukolilo, Gubeng, Rungkut, and Wonocolo Subdistrict. Journal of Applied Environmental
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Quality and Human Health Risk in Charsadda District, Pakistan. Spec. Vol. Water Women
Waste Wisdom Wealth, 93–101.
Samudro, G. and Mangkoedihardjo, S. (2006). Water equivalent method for city
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Schumacker, R. E., Lomax, R. G. (2010). A Beginner’s Guide to Structural Equation
Modeling: Third Edition. New York: Routledge Taylor and Francis Group, pp 2-3 and 170.
Xi, J., Zhang, F., Lu, Y., Hu, H.Y. (2017). A Novel Model Simulating Reclaimed Water
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