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TEKNOSAINS: Jurnal Sains.
Teknologi dan Informatika Vol.
No.
2, 2025, page.
http://jurnal.
id/index.
php/tekno https://doi.
org/10.
37373/tekno.
Decision support system for determining the best tofu industry location using a simple multi-attribute rating technique Julianto Lemantara Dinamika University.
Indonesia.
Jl.
Raya Kedung Baruk No.
Rungkut.
Surabaya.
Indonesia, 60298 Corresponding Author: julianto@dinamika.
Submitted: 27/04/2025 Revised: 15/05/2025 Accepted: 20/06/2025 ABSTRACT The tofu industry has significant potential in Indonesia, particularly as a staple food rich in protein.
However, many entrepreneurs face challenges in determining the optimal location to build a tofu industry.
Suboptimal locations often cause various problems, including high operational costs, difficult access to raw materials, and negative environmental impacts due to production waste.
Factors such as raw material availability, waste management, distance to residential area, land cost, and accessibility are the main challenges that must be considered in determining the optimal location of the tofu industry.
This study used the Simple Multi-Attribute Rating Technique (SMART) method to support objective decision-making in determining the best location for the tofu industry.
Using this method, the determination of the best alternative location for the tofu industry is evaluated based on the weight of the specified criteria.
The research results indicated that the Decision Support System (DSS) application that implemented the SMART method could provide the best location recommendations.
The accuracy of the SMART method calculation on the DSS application is 100% correct and can be trusted.
In addition, the DSS application was also able to accommodate criteria modeling with dynamic assessment scales according to user Keywords: Decision support system.
SMART method.
industry location.
tofu industry INTRODUCTION The tofu industry has very good prospects in Indonesia, considering that tofu is a popular food among the people.
As one of the providers of soy-based foods that are rich in protein and affordable, tofu has become a staple food, especially for people with middle to low purchasing power.
In addition to its high nutritional value, tofu also has an economical price, making it the main choice for daily consumption .
However, despite the great opportunities, the tofu industry faces complex challenges, especially in terms of choosing the right location.
Factors such as the availability of raw materials, waste management, distance to residential areas, land cost, and accessibility are crucial elements that must be Suboptimal locations often cause various problems, including high operational costs, difficult access to raw materials, and negative environmental impacts due to production waste .
In Surabaya, there are at least 10 tofu industries with varying production capacities, ranging from 40 kg to 880 kg per day.
Most of these industries are classified as small to medium enterprises with limited capital and technology .
Unfortunately, many business actors have not made the location a strategic element in their business planning.
As a result, the great potential of the tofu industry often cannot be maximized.
In the business world, choosing a business location has a significant impact on the sustainability and success of a business.
A strategic location can increase operational efficiency, reduce distribution costs, and facilitate access to raw materials and customers .
The tofu industry.
TEKNOSAINS: Jurnal Sains.
Teknologi & Informatika is licensed under a Creative Commons Attribution-NonCommercial 4.
0 International License.
ISSN 2087-3336
(Prin.
| 2721-4729 (Onlin.
ISSN 2087-3336 (Prin.
| 2721-4729 (Onlin.
DOI 10.
37373/tekno.
choosing the right location is very important to ensure the availability of high-quality soybean raw materials, accessibility to the market, and good waste management.
One of the main challenges in the tofu industry is high waste production.
The tofu production process produces organic waste, both liquid and solid, with a Chemical Oxygen Demand (COD) level reaching 14,000 mg/L and Biological Oxygen Demand (BOD) up to 8,000 mg/L.
This waste has the potential to pollute the environment if not managed properly.
Locations that do not support waste treatment installations can worsen environmental impacts and hinder business sustainability.
In addition, locations that are far from traditional markets or main customers can increase transportation costs and distribution times, which ultimately reduce the competitiveness of the tofu industry in meeting operational cost efficiency needs .
Decision Support System (DSS) is a system that has the capacity to solve semi-structured problems .
Previous research has shown that DSS can help business actors determine business locations based on predetermined criteria.
Common methods used in DSS for business location selection include Weighted Product (WP) and Simple Multi-Attribute Rating Technique (SMART), which allow alternative rankings based on the weight given to each criteria.
However, most previous researchers have only applied the SMART theory to solve certain problems, not to the point of creating a websitebased application for a computerized DSS.
There was research that only applied the SMART theory to tourist destinations in North Sumatra .
There is also research that only applied the SMART theory to determine the location of a bakery .
These researchers had not produced a computerized DSS.
There was previous research that has produced a DSS with SMART method but has not modeled the value of each criteria with a flexible and dynamic scale.
This study has modeled the assessment of criteria with a scale that can be adjusted to user needs flexibly and dynamically.
In the context of the tofu industry, location selection must also consider environmental aspects, especially in waste management.
A study emphasized that selecting the right raw material supplier and effective waste management greatly affect production quality .
Therefore, a DSS based on the SMART method can be used to evaluate factors such as distance to suppliers, operational costs, and the environmental impact of a business location.
Given these problems, this study aims to develop a DSS based on the SMART method that can help tofu entrepreneurs in determining the optimal industrial location in Surabaya.
By adopting a multi-criteria approach, this system will provide location recommendations based on main criteria.
Based on these problems, a DSS is needed that can help tofu industry business actors in determining the optimal business location.
This system must be able to identify and evaluate a number of criteria in a structured manner, so that the resulting decisions are objective and accountable.
The criteria used in this DSS cover various important aspects such as: Availability of raw materials.
Waste management.
Distance to residential area.
Land cost, and Accessibility.
This DSS has modeled the assessment of criteria with a scale that can be adjusted to user needs flexibly and dynamically so this has added value compared to previous research.
This DSS application was built with the PHP programming language and MySQL database because this application was website-based and was still being tested locally.
METHOD
Phases of Decision Support System (DSS) This study uses 4 phases in the decision-making system, namely intelligence, design, choice, and For more details, each phase can be explained as follows:
Intelligence phase As the first phase in decision-making, the intelligence phase is the stage of identifying problems and collecting information related to what will be decided .
In this phase, problem identification is carried out, namely determining the optimal location for the tofu industry.
Data related to potential locations will be collected, in the context of the tofu industry, choosing a strategic location is a crucial factor in determining business sustainability .
Inappropriate decisions in determining the location can cause various problems, such as limited access to raw materials, increased distribution costs, and environmental problems due to suboptimal waste management .
Therefore, this study attempts to identify the main factors that need to be considered in selecting a tofu industry location using the Simple Multi-Attribute Rating Technique (SMART) method.
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Design phase This phase focuses on designing an assessment model to evaluate and compare each alternative location of the tofu industry based on predetermined criteria .
With the SMART method, each criteria is given a weight according to its level of importance.
The weight of each criteria is determined in Table 1.
Table 1.
List of criteria Criteria Type Availability of Raw Materials (K.
Benefit Waste Management (K.
Benefit Distance to Residential Areas (K.
Benefit Land Costs (K.
Cost Accessibility (K.
Benefit Weight Calculation and comparison of alternatives are done using the SMART method to determine the best location.
The assessment for each criteria uses a Likert scale with a value range of 1 .
ery ba.
to 5 .
ery goo.
More details, the criteria assessment scale can be seen in Table 2.
Table 2.
Criteria assessment scale Criteria Assessment Scale Very Good Good Good Enough Bad Very Bad The results of the criteria modeling in the DSS for determining the location of the tofu industry can be seen in Table 3.
Table 3.
Criteria modeling Description Supply capacity > 200 kg/hari Supply capacity 150 - 200 kg/day Supply capacity 100 - 149 kg/day Supply capacity 50 - 99 kg/day Supply capacity 0 - 49 kg/day Waste Wastewater Treatment Plant system (WTPS) capacity > 10.
000 L/hari Management WTPS capacity 5.
000 - 10.
000 L/hari (K.
WTPS capacity 2.
000 - 5.
000 L/hari WTPS capacity < 2.
000 L/hari There is no WTPS Distance to > 5 km Residential 3 Ae 5 km Areas 2 Ae 3 km (K.
1 Ae 2 km < 1 km Land Costs Land purchase price < Rp 7.
000,00/mA (K.
Land purchase price: Rp 7.
000,00 - Rp 7.
000,00/mA Land purchase price: Rp 7.
000,00 - Rp 7.
999,99/mA Land purchase price: Rp 8.
000,00 - Rp 8.
999,99/mA Land purchase price: Rp 8.
000,00 - Rp 9.
000,00/mA Accessibility The asphalt road condition is very good, 2 lanes, road width > 8 meters (K.
The asphalt road condition is good, 2 lanes, road width > 6-8 meters The asphalt road condition is quite good, 1 lane, road width > 4-6 meters The road condition is not good, the road width is 3-4 meters Criteria Availability Raw Materials (K.
Scale ISSN 2087-3336 (Prin.
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Criteria Description The road condition is poor, road width < 3 meters Scale Choice Phase In the choice phase, calculations will be carried out on each alternative using the SMART method, producing a score for each location.
Then it will sort the location alternatives from the highest to the lowest score, and users can choose the best location based on the recommendations from the Implementation Phase After the decision is made, the implementation steps are carried out and will produce a complete report or recommendation regarding the best location .
This information can then be submitted to related parties, such as the industrial development team or management, for the realization steps of selecting the tofu industry location.
Simple Multi-Attribute Rating Technique (SMART) Method In this study, the method used to create a DSS in selecting the best tofu industry location is SMART.
The SMART method is one of the techniques in DSS that is used to assist the decision-making process by considering various criteria.
This multi-criteria decision-making technique is based on the theory that each alternative consists of several criteria that have values and each criteria has a weight that describes how important it is compared to other criteria .
This method is widely used in selecting business locations because of its ability to optimize various factors that influence business decisions.
The model used in SMART has several stages that must be carried out, as follows: .
perform normalization of criteria weight with formula .
perform normalization of criteria value, where the benefit criteria use formula .
and the cost criteria use formula .
the result of the criteria normalization is multiplied by the weight normalization.
and finally .
Sort the best alternatives based on the largest sum of the multiplication of the normalized criteria weights by the normalized criteria values.
Note:
out = criteria value for a particular alternative min = highest value on the criteria
max= highest value on the criteria
= ! # !"
! #
= ! # !"
RESULT AND DISCUSSION
Results of problem analysis Based on the research problems, the solution offered to tofu industry players is the creation of a DSS application equipped with the functions of sign up, login, alternative data maintenance, criteria data maintenance, criteria assessment scale data maintenance, and alternative ranking as a recommendation for determining the best tofu industry location.
To use the application, hardware is required to be able to run the operating system and web browser smoothly.
In this research, several alternative data were used as samples in determining the Best Tofu Industry Location in Surabaya Using the SMART Method.
The alternative assessment data for each criteria in this study can be seen in Table There are 5 criteria used in this study, namely: Availability of Raw Materials (K.
Availability of Raw Materials (K.
Distance to Residential Areas (K.
Land Costs (K.
, and Accessibility (K.
A more detailed explanation of the criteria can be seen in Table 4.
Table 4.
Alternative assessment of each criteria Alternative K01 K02 K03 K04 Jl.
Kalilom Lor Indah (A.
Jl.
Mulyosari Surabaya (A.
Jl.
Gunung Anyar (A.
Jl.
Medokan Ayu Utara (A.
K05 ISSN 2087-3336 (Prin.
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Alternative Jl.
Medokan Ayu.
Tambak (A.
K01
K02
K03
K04
K05
Implementation of SPK Components Using SMART Method Data management This component stores all data related to alternative locations, assessment criteria, and the weight of each criteria .
Data related to potential locations will be collected, such as raw material availability, waste management, distance to residential area, land costs, and location and accessibility.
The system also supports structured data storage to facilitate decision-making and further analysis.
For database design, the tool used was Power Designer.
Figure 1 is a design of the Physical Data Model, where there are 5 tables.
The first table is an alternative with the attributes id_alternatif as the primary key, alternative_name, alternative_address, alternative_district, and alternative_city.
The second table is a criteria table with the attributes id_criteria as the primary key, criteria_name, criteria_type, and criteria_weight.
Then there is a criteria_scale table with the attributes id_slala as the primary key, definition, scale, and criteria_id as the foreign key that refers to the criteria table.
There is an assessment table with the attributes id_penilaian as the primary key, id_alternatif as the foreign key that refers to the alternative table, id_scale as the foreign key that refers to the criteria_scale table, and id_criteria as the foreign key that refers to the criteria table.
Finally, there is a user table with the id attribute as the primary key, email, and password.
These tables are related to each other, the assessment table is related to the alternative, criteria, and criteria_scale tables.
While the scale_criteria table is related to the criteria table.
Figure 1.
Physical data model Model management This component manages the SMART calculation model.
There is a module that functions to calculate the value of each alternative based on the weight and criteria that have been set .
This model also allows users to adjust the criteria weights if necessary, so that they can be adjusted to user preferences or needs.
SMART method calculation steps: Weight Normalization.
Criteria Normalization.
Criteria normalization results multiplied by weight normalization, and Ranking Knowledge management This component stores information about the effective decision-making process, including knowledge of the SMART method and guidelines for determining criteria weights.
Knowledge Management can help users understand how the system works and the basis for the decisions made, so that users can make relevant adjustments .
The following are the results of implementing the SMART method: The results of the normalization of the criteria weights can be seen in Table 5.
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Table 5.
Weight Normalization Weight of Interest
Criteria
Criteria
%,'(
%,'(
= * = 0,25
%,'()%,'%)%,'%)%,*()%,'%
%,'%
%,'%
= * = 0,2
%,'()%,'%)%,'%)%,*()%,'%
%,'%
%,'%
= * = 0,2
%,'()%,'%)%,'%)%,*()%,'%
K01
K02
K03
K04
K05
Total Weight of Interest
Weight of Interest
%,*(
%,*(
= * = 0,15
%,'()%,'%)%,'%)%,*()%,'%
%,'%
%,'%
= * = 0,2
%,'()%,'%)%,'%)%,*()%,'%
The results of the criteria normalization with formula .
can be described in more detail in the paragraph below.
Meanwhile, the results of the multiplication between the normalization of the criteria values and the normalization of the criteria weights can be seen in Table 6.
K01:
A01:
Benefit = * * K02:
A01:
A02:
Benefit = , , ( .
A03:
, *
= 0,67
A04:
A05:
* *
A03:
, ,
( ,
A04:
( ,
( ,
A05:
= 0,5
( ,
A03:
A04:
A05:
! # !"
A02:
, ,
( ,
Benefit = ! # ! " ( ( K04 :
Cost A02:
! #
! # !"
A01: * = 0,67
K05:
, *
= 0,67
K03:
A01:
! # !"
A02: * = 0,67
A03: * = 0,33
A04: * = 1
A05: * = 0
Benefit = ! # ! " , ' A01: ' = 0,5 Criteria
K01
K02
K03
K04
K05
' '
A02: ' = 0
A03: ' = 1
, '
A04: ' = 0,5
' '
A05: ' = 0
Table 6.
Weight normalization x criteria normalization Calculation Results A01: .
x 0,.
= 0,25 A04: .
x 0,.
= 0,25 A02: .
,67 x 0,.
= 0,1675 A05: .
x 0,.
= 0 A03: .
,67 x 0,.
= 0,1675 A01: .
x 0,.
= 0 A04: .
x 0,.
= 0,2 A02: .
x 0,.
= 0 A05: .
,5 x 0,.
= 0,1 A03: 0 x 0,.
= 0 A01: .
x 0,.
= 0,2 A04: .
x 0,.
= 0 A02: .
x 0,.
= 0 A05: .
x 0,.
= 0 A03: .
x 0,.
= 0 A01: .
,67 x 0,.
= 0,1005 A04: .
x 0,.
= 0,15 A02: .
,67 x 0,.
= 0,1005 A05: .
x 0,.
= 0 A03: .
,33 x 0,.
= 0,0495 A01: .
,5 x 0,.
= 0,1 A04: .
,5 x 0,.
= 0,1 A02: .
x 0,.
= 0 A05: .
x 0,.
= 0 A03: .
x 0,.
= 0,2 ISSN 2087-3336 (Prin.
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The results of the ranking of the best alternative tofu industry locations can be seen in Table 7.
The final result is obtained by adding up all the values obtained by each alternative on all criteria.
For example, the final value for alternative A04 is obtained with the following details: 0,25 0,2 0 0,15 0,1 = 0,7.
Table 7.
Ranking results Alternative Final Result Jl.
KaliLom Lor Surabaya (A.
0,6505 Jl.
Mulyosari surabaya (A.
0,268 Jl.
Gunung Anyar (A.
0,417 Jl.
Medokan Ayu Utara (A.
Jl.
Medokan Ayu.
Tambak (A.
Ranking Communication This component is responsible for conveying the analysis results information to the user, including visualization of results and easy-to-understand reports .
For example, the system can present the calculation results in the form of graphs or comparison tables to facilitate location selection.
This feature ensures that the results of the system are easily accessible and understood by those who need them.
Figure 2 is a display of the Alternative Table which functions to view alternative data, starting from Alternative ID (Id Alternati.
Name (Nam.
Address (Alama.
District (Kecamata.
City (Kot.
, and Action (Aks.
In the action column, there are edit and delete buttons that function to replace and remove If the user wants to add alternative data, press the Add Alternative ( Tambah Alternati.
Figure 2.
Display of the alternative table Figure 3 is a display of the Criteria Data Addition Form which functions to input the required criteria data, namely Criteria ID (ID Kriteri.
Criteria Name (Nama Kriteri.
Criteria Type (Jenis Kriteri.
, and Criteria Weight (Bobot Kriteri.
The criteria weight is filled in on a scale of 0 to 1, where 0 is equivalent to 0% and 1 is equivalent to 100%.
If all the data has been filled in, the data can be saved with the InsertData button and there is a Reset button if you want to repeat filling the data.
There is also a Criteria Type Dropdown which functions to provide 2 criteria type options, namely Benefit or Cost.
Figure 3.
Display of the criteria data addition form Figure 4 is a display of the Criteria Table which functions to view criteria data starting from Criteria ID (ID Kriteri.
Criteria Name (Nama Kriteri.
Criteria Type (Jenis Kriteri.
Criteria Weight (Bobot ISSN 2087-3336 (Prin.
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Kriteri.
, and Action (Aks.
In the action column, there are edit and delete buttons that function to change or remove data.
Figure 5 is a display of the Criteria Scale Table which functions to view information related to criteria scale modeling.
In the action column, there are edit and delete buttons that function to replace or delete data.
An example of criteria scale modeling is: if the supply capacity is > 200 kg/day, then the scale value is 5.
This feature is a superior compared to similar research because with this feature, criteria assessment can be carried out with a dynamic assessment scale, according to user needs.
Figure 4.
Display of The Criteria Table Figure 5.
Display of on the criteria assessment scale table Figure 6 is a display of the Assessment Table which functions to view assessment data, starting from Alternative Address (Alamat Alternati.
Availability of Raw Material (Ketersediaan Bahan Bak.
Wastewater Management (Pengelolaan Air Limba.
Distance to Residential Area (Jarak dengan Pemukima.
Land Cost (Biaya Pemukima.
Accessibility (Aksesibilita.
, and Action (Aks.
In the action column, there are edit and delete buttons that function to change/remove data.
This form displays alternative values for each criteria.
Figure 6.
Display of the assessment table Figure 7 is a display of the Weight Normalization Results which functions to present the normalized criteria weight values in the decision calculation process.
The data displayed will be used in the final score calculation to determine the best alternative.
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Figure 7.
Display of the weight normalization results Figure 8 is a display of the Criteria Normalization Results which functions to present the normalized criteria values in the decision calculation process.
The data displayed in this table shows a comparison of each alternative based on the specified criteria.
The normalization result values are used in the final score calculation process to determine the best alternative.
Figure 8.
Display of the criteria normalization results Figure 9 is the Final Result display that functions to present the final score of each alternative based on the calculation of the normalized criteria weights.
The data displayed in the table shows a comparison of the total scores of each alternative, which is used to determine the best alternative.
The final score value is obtained from the calculation results of all criteria considered in the decision-making process.
Figure 9.
Display of the final result Figure 10 is a display of the Ranking Results in the image above which functions to present the final score of each alternative based on the calculation of the normalized criteria weights.
The data displayed in the table shows a comparison of the total scores of each alternative, which is used as a basis for determining the best alternative.
The final score value is obtained from the calculation of all criteria considered in the decision-making process, thus providing an objective picture of the ranking of each With the SMART method in Figure 30, the results obtained are that Jl.
Medokan Ayu Utara is the best alternative for the location of the tofu industry in Surabaya.
The results of the SMART calculation on the website in Figure 30 are also the same as the results of the manual calculation in Table This proves that the accuracy of the SMART calculation on the website is 100% correct and can be ISSN 2087-3336 (Prin.
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Figure 10.
Display of the ranking results CONCLUSION Based on the research conducted, the Simple Multi-Attribute Rating Technique (SMART) method has proven effective in assisting decision-making to determine the best location for the tofu industry in Surabaya.
The analysis results showed that the location area of Jl.
Medokan Ayu Utara has the highest score .
compared to other alternatives, making it the most optimal location.
Besides, this DSS was also able to accommodate criteria modeling with dynamic assessment scales according to user needs.
The development that can be done for further research is the need to add more criteria in determining the location of the tofu industry so that the results are more comprehensive.
A combination of DSS methods can also be done, for example, a combination with the AHP method in order to determine a more objective criteria weight based on the scale of weight importance.
REFERENCE