International Journal of Eco-Innovation in Science and Engineering (IJEISE) Vol.
, 2025 .
https://ijeise.
id/ E-ISSN: 2721-8775 Article Integration of Waste Management and Environmental Impact Assessment for Sustainable Manufacturing in Sukolego Tofu Production Mega Cattleya P.
Islami1,a*.
Rizky Novera Harnaningrum2,b Department of Industrial Engineering.
Faculty of Engineering and Science.
Universitas Pembangunan Nasional Veteran Jawa Timur.
Indonesia 2 Department of Industrial Engineering.
Universitas Insan Cita Indonesia.
Indonesia E-mail: amega.
ti@upnjatim.
id, brizkynovera@uici.
*Corresponding author: .
ti@upnjatim.
|Phone number: 6281775433488 Received: 07th May 2025.
Revised: 24th June 2021.
Accepted: 26th August 2025.
Available online: 30th November 2025.
Published regularly: May and November Abstract The tofu production process, particularly in small- to medium-scale industries like Sukolego Tofu Production, generates various types of waste that pose environmental challenges.
This study aims to integrate waste management practices and environmental impact analysis within a sustainable manufacturing framework.
This study uses a quantitative method with a causal associative approach to measure Waste Management (X.
Environmental Impact Assessment (X.
, and Sustainable Manufacturing (Y).
Data was collected through questionnaires distributed to 50 people living around the Sukolego Tofu Factory.
Data that meets the validity, reliability, and classical assumption tests is processed to produce a linear regression equation.
The analysis results indicate that the Environmental Impact Assessment (EIA) variable has a positive and significant effect on Sustainable Manufacturing, both partially and simultaneously.
However, the Waste Management variable does not exhibit a significant This is supported by the significance (Sig.
) values, where the EIA variable yields a value of 0.
< 0.
, indicating statistical significance, whereas the Waste Management variable yields a value of 702, which is not statistically significant.
Keywords: Waste Management.
Environmental Impact Assessment.
Sustainable Manufacturing.
Tofu Production.
Introduction The growing demand for environmentally responsible production has urged industries of all scales, including traditional food manufacturers, to adopt sustainable practices .
One such industry is tofu production, which is known for generating considerable amounts of solid and liquid waste .
When not properly managed, this waste contributes to environmental degradation, such as water and soil pollution, and can negatively affect the health and well-being of surrounding communities .
Sukolego Tofu Production, a small-to-medium-sized enterprise located in Indonesia, represents a typical tofu manufacturing unit facing the challenge of balancing productivity and environmental The PLS-SEM based empirical model shows that Green Supply Chain Management (GSCM) practices enhance Zero Waste Management and green innovation .
coAcinnovatio.
, which significantly reduces environmental impact and improves corporate environmental performance .
Although tofu is considered a sustainable food product due to its plant-based origin, the production process often lacks proper waste management systems and ecological assessment DOI:10.
International Journal of Eco-Innovation in Science and Engineering (IJEISE) Vol.
, 2025
While tofu contributes to a lower environmental footprint than animal-based protein sources, the sustainability of the final product is significantly influenced by how it is manufactured .
In many small- to medium-scale tofu industries, especially in developing regions, waste such as soybean residue .
, wastewater, and by-products are frequently disposed of without adequate treatment .
This can result in environmental issues, including water pollution, foul odors, and increased organic load in surrounding ecosystems .
Furthermore, the absence of structured environmental impact assessments means these negative externalities often go unmonitored and unmitigated.
Without proper intervention, the tofu production process can contradict the principles of sustainability that the product represents.
Therefore, it is essential to integrate waste management improvements and environmental monitoring into the production framework to align the value chain with the Sustainable Development Goals .
This gap underscores the need for a structured approach that integrates waste handling and environmental impact analysis within a broader sustainable manufacturing framework.
Sustainable manufacturing creates products through economically sound processes that minimize negative environmental impacts while conserving energy and natural resources .
, .
This approach maximizes production efficiency and profitability, emphasizing longterm ecological stewardship and social By integrating environmental considerations into each stage of the manufacturing lifecycleAifrom raw material selection, production, distribution, to waste disposalAisustainable manufacturing seeks to reduce carbon emissions, prevent pollution, and limit the depletion of finite resources .
Moreover, it encourages the use of renewable energy sources, cleaner technologies, and closedloop systems to enhance sustainability.
In doing so, sustainable manufacturing contributes to achieving broader goals such as climate change mitigation, sustainable economic growth, and compliance with environmental regulations .
For industries such as tofu production, where traditional processes may lack environmental safeguards, adopting sustainable manufacturing practices is crucial to reducing their ecological footprint while maintaining productivity and competitiveness .
Applying this concept to tofu production involves enhancing operational efficiency and minimizing environmental impact through improved waste management and process This study examines the integration of waste management practices and environmental impact analysis in supporting sustainable manufacturing at the Sukolego Tofu Production facility.
achieve this, the research applies a multiple linear regression method, with Waste Management and Environmental Impact Analysis as exogenous variables and Sustainable Manufacturing as the endogenous variable.
The aim is to quantitatively assess the influence of these two factors on the implementation of sustainable manufacturing The results will provide practical insights and strategic recommendations for enhancing environmental performance in smallscale tofu production industries.
Materials and Methods This research was conducted at the Sukolego Tofu Production Factory in Sidoarjo.
East Java.
Indonesia.
This site was selected because it was relevant to the study's focus on small-scale tofu manufacturing and its associated environmental management challenges.
The data collection process employed both interview methods and the distribution of structured questionnaires to gather comprehensive information related to waste management practices, perceived environmental impacts, and sustainability awareness.
The questionnaires were designed using a Likert scale, allowing respondents to express the degree of their agreement or disagreement with various statements related to the research A total of 50 respondents were selected from the residential community surrounding the Sukolego Tofu Factory.
The sample was determined based on purposive sampling, targeting individuals who were directly or indirectly affected by the factory's operations and thus had relevant insight into the area's environmental conditions.
Purposive sampling was employed to ensure that respondents had relevant, experiential knowledge of the environmental effects of the Sukolego Tofu Factory.
This method enabled the study to gather targeted, context-rich insights that might not have been captured through a purely random sample.
DOI:10.
International Journal of Eco-Innovation in Science and Engineering (IJEISE) Vol.
, 2025
Data was processed using the SPSS (Statistical Package for the Social Science.
After confirming that the data met the necessary assumptions, the study applied multiple linear regression analysis to examine the relationship between the independent variables.
Waste Management (X.
and Environmental Impact Analysis (X.
, and the dependent variable.
Sustainable Manufacturing (Y).
Lastly, hypothesis testing was conducted using the t-test .
o assess the significance of individual predictor.
, the Ftest .
o evaluate the overall model significanc.
Table 2.
Waste Management Indicators Item
WM 1
WM 2
WM 3
WM 4
WM 5
WM 6
EIA 1
EIA 2
EIA 3
EIA 4
EIA 5
EIA 6
SM 1
SM 2
SM 3
SM 4
SM 5
SM 6
Score Community awareness of waste separation practices Perceived cleanliness of surrounding Observed waste disposal methods Reported environmental issues related to waste Community perception of waste reduction efforts Access to information on waste Public awareness of EIA documents .
AMDAL.
UKL-UPL)
Perception degradation .
ir, water, lan.
Community environmental consultations Visibility of mitigation infrastructure .
, filtration, treatmen.
Responsiveness Perceived commitment to sustainability and environmental improvement Perceived reduction in environmental Awareness of eco-friendly production Community perception of resource efficiency .
, water, energ.
Perceived social responsibility of the Community satisfaction with factory communication and transparency Community participation opportunities in environmental programs and the coefficient of determination (RA) to measure the proportion of variance .
Results and Discussion The output of the normality test was obtained Table 1.
Normality Test.
Item KolmogorovSmirnov Asymp.
Sig .
-taile.
Score using the Kolmogorov-Smirnov method, a statistical technique commonly employed to assess whether a dataset follows a normal distribution.
The normality test using the KolmogorovSmirnov (K-S) method is conducted to determine whether the residuals .
r dat.
follow a normal distribution, which is one of the key assumptions in linear regression analysis .
In the test output, the Asymp.
Sig.
-taile.
The value is 0.
This p-value is then compared to the commonly used significance level of 0.
Since the value of 0.
is greater than 0.
05, we fail to reject the null hypothesis, which states that the data follow a normal distribution.
In other words, no statistically significant deviation from normality is detected in the data.
The output of the heteroscedasticity test using the Glejser method provides insight into whether the variance of the residuals .
in a regression model is constant, a key assumption of the classical linear regression model .
Heteroscedasticity violates this assumption, potentially leading to inefficient estimates and misleading statistical inferences.
Table 3.
Heteroscedasticity Test.
Variables Waste Management Environmental Impact Score From the output, the significance values for the Environmental Impact variable and the Waste Management variable are 0.
383 and 0.
Since the values are greater than 0.
there are no indications of heteroscedasticity.
The output of the multicollinearity test indicates that DOI:10.
International Journal of Eco-Innovation in Science and Engineering (IJEISE) Vol.
, 2025
the Tolerance value for the Environmental Impact variable and the Waste Management variable is 000, which is greater than 0.
10, indicating no Table 4.
Multicollinearity Test.
Variables Waste Management Environmental Impact Score multicollinearity in the regression model.
Meanwhile, the VIF (Variance Inflation Facto.
values for the Environmental Impact variable and the Waste Management variable are this value is less than 10.
00, indicating no multicollinearity in the regression model.
Variance Inflation Factor (VIF) value of 1.
000 is theoretically ideal and suggests no linear correlation between the predictor variables.
However, in real-world datasets, such a perfectly uncorrelated relationship is extremely rare, as even slight overlaps in predictor variables typically result in VIF values greater than 1.
In this study, the VIF values of 1.
000 for both Waste Management and Environmental Impact variables are still statistically plausible, but likely influenced by the small number of predictor variables .
nly tw.
, reducing the chance for inter-variable The Adjusted R-Square is a modified version of RSquare (RA) that adjusts for the number of explanatory variables in a regression model .
reflects how well the independent variables explain the variation in the dependent variable while penalizing the addition of irrelevant Unlike RA, the Adjusted RA can Table 5.
Coefficient of Determination Test.
Item Waste Management Environmental Impact Score decrease if a new variable doesnAot significantly improve the model .
While the model demonstrates a statistically significant relationship, its explanatory power is modest (Adjusted RA = 0.
, indicating that additional factors beyond waste management and Table 6.
Coefficient of Determination Test.
Item Waste Management Environmental Impact Score environmental impact likely contribute to sustainable manufacturing at the Sukolego Tofu Factory.
Based on the F-test results, the significance value is 0.
016, which is less than the threshold value of 0.
This indicates that the regression model is statistically fit, and it can be concluded that the variables Environmental Impact and Waste Management have a significant simultaneous effect on the Sustainable Manufacturing Approach.
The partial significance test results show that the Environmental Impact variable has a p-value 004 (<0.
, indicating a statistically significant influence on the Sustainable Manufacturing Approach.
In contrast, the Waste Management variable has a p-value of 0.
702 (>
, suggesting that it does not have a significant individual effect.
Table 7.
Coefficient of Determination Test.
Item Constant Waste Management -0.
Environmental Impact Sig However, based on the F-test, the simultaneous significance value is 0.
024 (< 0.
confirming that the regression model is Thus, it can be concluded that the Environmental Impact variable plays an important role, while Waste Management does not, although their combination still provides a meaningful explanation of the dependent variable.
The regression coefficient for Waste Management (XCA) is -0.
702, which means that for each one-unit increase in Waste Management, the value of the Sustainable Manufacturing Approach is predicted to decrease by 0.
702 units, assuming the Environmental Impact variable remains This negative relationship suggests that, in the context of this model, higher waste DOI:10.
International Journal of Eco-Innovation in Science and Engineering (IJEISE) Vol.
, 2025
management is associated with a reduction in the sustainable manufacturing index, possibly due to other underlying trade-offs or operational challenges .
On the other hand, the coefficient for Environmental Impact (XCC) is 0.
004, indicating that for every additional unit increase in the Environmental Impact score, the Sustainable Manufacturing Approach is predicted to increase 004 units, holding Waste Management Although this coefficient is relatively small, its positive value suggests a direct considerations and sustainable manufacturing.
summary, the regression equation shows contrasting influences of the two independent Environmental Impact contributes positively .
lbeit slightl.
to sustainable manufacturing practices.
Waste Management appears to have a negative association within the scope of this model.
Based on the findings, local tofu producers are encouraged to go beyond technical compliance in waste management by actively engaging with community perceptions and improving the visibility of sustainability efforts.
While environmental impact assessments were positively associated with sustainable manufacturing, waste management did not significantly contribute Ai a potential result of misperceptions or inadequate To address this, producers operational practices, and collaborate with stakeholders in participatory waste initiatives.
Additionally, adopting simple EIA tools, promoting cleaner production, and aligning sustainability actions with local concerns can lead to more meaningful and perceived improvements in environmental responsibility.
Conclusions emissions, pollution, resource depletion, and waste generation, play a critical role in shaping sustainable manufacturing practices .
This significance implies that organizations are more likely to adopt strategies and technologies aligned with sustainability principles when they emphasize minimizing their environmental footprint .
the other hand, waste management does not show a significant effect, suggesting that its influence on sustainable manufacturing is not strong enough to be statistically confirmed in this model or dataset.
One possible explanation for why Waste Management does not demonstrate a statistically Sustainable Manufacturing Approach is that its influence may be indirectly mediated through other variables not captured in the current model, such as technological capabilities, regulatory compliance, organizational culture, and employee awareness.
However, when both variables are tested using the F-test, the overall regression model is significant, indicating that at least one of the variables .
n this case.
Environmental Impac.
explains a portion of the variance in the Sustainable Manufacturing approach .
contrast, the insignificant effect of Waste Management highlights a potential disconnect between existing waste-handling practices and their perceived impact on sustainability.
This could be due to several reasons: the waste practices may not yet meet community their benefits may not be clearly or their influence may be mediated through unobserved variables such as technology adoption, operational scale, or management commitment.
This calls for more integrated waste strategies that are not only technically effective but also socially visible and References