International Journal of Management Science and Information Technology IJMSIT
E-ISSN: 2774-5694
P-ISSN: 2776-7388
Volume 6 .
January-June 2026, 211-222 DOI: https://doi.
org/10.
35870/ijmsit.
Analysis of Decision Support System to Determine Toddlers Eligible for Additional Food at Posyandu in Perkebunan Tanah Datar Village Using Profile Matching Method Revina Salsabila 1*.
Nurul Rahmadani 2.
Muhammad Iqbal 3 1*,2,3 Information Systems Study Program.
Faculty of Computer Science.
Universitas Royal.
Asahan Regency.
North Sumatra.
Indonesia Email: r2367200@gmail.
com 1*, cloudyrara@gmail.
com 2, iqbalmh@royal.
Abstract Article history:
Received March 4, 2026 Revised March 12, 2026 Accepted March 13, 2026 The determination of toddlers eligible for the Supplementary Feeding Program (PMT) at the Integrated Health Post in Perkebunan Tanah Datar Village is still conducted manually, which can lead to subjectivity and inaccurate targeting of assistance.
This study aims to develop a Decision Support System using the Profile Matching method to determine priority PMT recipients in a more objective and systematic manner.
The assessment compares the actual condition of toddlers with an ideal profile by calculating GAP values across several criteria, including stunting status, nutritional status, parentsAo income, and motherAos education level.
These criteria are processed using Core Factor .
%) and Secondary Factor .
%) weighting to generate priority rankings.
The system evaluation was conducted using seven toddler data samples, producing ranking results in which toddler B6 obtained the highest priority score of The results indicate that the proposed method is able to generate consistent eligibility rankings and support a more transparent and measurable decision-making process compared to manual selection.
automating the calculation of GAP values and weighting factors, the system reduces subjectivity and improves the efficiency and accuracy of PMT recipient determination.
However, this study is limited by the relatively small dataset and its implementation in a single Posyandu Future research may involve larger datasets and additional evaluation criteria to improve the robustness and applicability of the model in broader community health service settings.
Keywords:
Decision Support System.
Profile Matching.
Supplementary Feeding.
Toddler.
Integrated Health Post.
INTRODUCTION
Rapid advances in information technology have transformed the way healthcare services are managed, including services delivered at the community level, including those delivered at the community level.
The utilization of information systems enables health data to be processed systematically, allowing decisionmaking processes to be conducted more quickly, accurately, and measurably (Organization 2.
(Kementerian Kesehatan Republik Indonesia 2.
In the field of child health, accurate decision-making is particularly critical because it is closely related to efforts to prevent nutritional problems and improve the quality of toddler growth and development.
The availability of reliable information systems can support healthcare providers in making evidence-based decisions, thereby improving the effectiveness of community health programs.
One form of nutritional intervention implemented at the community level is the Supplementary Feeding Program (PMT), which targets toddlers requiring additional nutritional support.
This program is implemented through Posyandu as the frontline of community healthcare services responsible for monitoring child growth and providing nutritional education to families.
Previous studies indicate that nutrition intervention programs Volume 6 .
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such as supplementary feeding play an important role in improving children's nutritional status and reducing the risk of stunting when implemented consistently and supported by effective monitoring systems (Cahyaningsari.
Saadah, and Usnawati 2.
However, determining which toddlers should be prioritized as PMT beneficiaries is not a simple process, as it requires the simultaneous consideration of multiple child health indicators and family-related factors.
When assessments are conducted manually based on subjective judgment, the resulting decisions may lack consistency and are difficult to measure in terms of accuracy.
This condition also occurs in the implementation of the PMT program in Desa Perkebunan Tanah Datar.
The determination of program eligibility still relies on manual considerations without a structured assessment system capable of integrating various indicators objectively.
As a result, the decision-making process may become inefficient and potentially lead to inaccurate targeting of PMT beneficiaries.
This situation highlights the need for a systematic approach that can assist health cadres in processing assessment indicators into decision recommendations that are more transparent, measurable, and analytically accountable.
Previous research also emphasizes that the use of digital decision support systems can help healthcare workers process health data and determine appropriate interventions for toddlers at risk of nutritional problems (Akbar.
Taufik Hidayat 2.
One solution that can be implemented is the development of a Decision Support System (DSS) using the Profile Matching method.
According to (OAoBrien and Marakas 2.
, a decision support system functions to transform raw data into strategic information to support objective and efficient decision-making.
Several previous studies have shown that multi-criteria decision-making methods can support selection processes based on structured criteria.
For example, (Sasono Wibowo 2.
applied the Profile Matching method in evaluating child growth and development and demonstrated that the method is capable of assessing the conformity of individual conditions to developmental standards in a more measurable way.
(Harahap.
Siregar, and Wulan 2.
developed a Profile Matching-based decision support system for contraceptive selection and found that the profile matching approach produced more consistent recommendations by considering the suitability of user conditions with ideal criteria.
Furthermore, (Mahendra et al.
showed that the application of Profile Matching in selection processes reduced subjectivity and resulted in more stable In addition, decision support systems have also been widely applied in the health sector to assist in determining nutritional status and identifying toddlers at risk of stunting through structured data analysis (Arifviando and Irawan 2.
However, although previous studies have demonstrated the effectiveness of decision support systems and multi-criteria methods in various contexts, most of these studies focus on general selection problems or nutritional status classification and have not specifically addressed the determination of eligibility for community-based nutritional intervention programs such as the Supplementary Feeding Program (PMT).
addition, earlier research generally emphasizes ranking alternatives based on weighted criteria without thoroughly analyzing the conformity between the actual conditions of toddlers and the ideal profile required for PMT beneficiaries.
This condition indicates a research gap in the application of decision support systems that integrate health and socio-economic indicators to determine PMT eligibility in community healthcare Based on this research gap, this study offers novelty through the integration of the Profile Matching method with operationally defined PMT eligibility indicators in the context of community healthcare Unlike previous studies that mainly focus on value ranking or criteria weighting, the proposed approach evaluates the conformity between the actual profile of toddlers and an ideal profile as the basis for decision recommendations.
This approach produces a selection mechanism that is more transparent, measurable, and analytically traceable.
Therefore, this study proposes the development of a web-based Decision Support System using the Profile Matching method to determine the eligibility of toddlers as PMT recipients.
The method compares actual conditions with an ideal profile based on operationally defined indicators to generate a quantitative level of conformity as the basis for decision recommendations.
This study aims to design a system capable of supporting the determination of PMT recipients in a more objective, efficient, and consistent manner within the context of community healthcare services.
RESEARCH METHOD
The research workflow employed in the development of the Decision Support System for determining eligible toddlers for the Supplementary Feeding Program (PMT) was systematically structured, beginning with problem identification and concluding with system testing.
Each stage was carefully designed to ensure that the decision-making process is objective, measurable, and accountable.
The stages of the research methodology are presented in Figure 1.
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Figure 1.
Research Framework Data Collection Method Data collection in this study was conducted through a series of complementary stages to obtain a comprehensive understanding of the process for determining eligible toddlers for the Supplementary Feeding Program (PMT) at Posyandu Desa Perkebunan Tanah Datar.
The first stage involved direct observation at the research site.
The researcher observed the implementation flow of Posyandu activities, including the weighing and recording of toddlers, as well as the procedures used by health workers and community health cadres to determine priority and assessment criteria for PMT recipients.
Subsequently, interviews were conducted with the village midwife and Posyandu cadres to obtain more detailed information regarding the criteria applied, the considerations taken into account during the selection process, and the challenges encountered, particularly when the number of eligible toddlers exceeded the available assistance capacity.
strengthen the field findings, relevant documentation was also collected and analyzed.
In addition, a literature review was conducted through credible online sources to establish a theoretical foundation concerning decision support systems, the Profile Matching method, fundamental concepts of nutrition, and the implementation of the PMT program.
The interview results were analyzed using a qualitative descriptive Information obtained from the village midwife and Posyandu cadres was used to identify key indicators considered in determining the eligibility of toddlers for the Supplementary Feeding Program (PMT).
These indicators were then translated into measurable assessment criteria in the Profile Matching The interview results also served as the basis for determining the ideal values of each criterion and for classifying the criteria into Core Factors and Secondary Factors.
Problem Identification The determination of PMT recipients at Posyandu Desa Perkebunan Tanah Datar is still conducted manually and relies heavily on subjective judgment by Posyandu cadres.
This condition results in an inefficient process, limited accuracy, and a potential risk of misallocation.
Based on the case observed in this study, the number of registered toddlers frequently exceeds the available quota, which may lead to inconsistencies and perceived unfairness in the selection process.
This issue constitutes the primary focus of the present research.
Therefore, a more structured and objective system is required to support a transparent, consistent, and efficient decision-making process in determining eligible PMT recipients.
Profile Matching Method This study employs the Profile Matching method, a decision-making technique commonly used to compare the actual condition of an object with a predetermined standard or ideal profile.
The core principle of this method is to measure the degree of conformity between actual values and ideal values.
The smaller the difference between the two, the higher the level of suitability of the object with respect to the expected criteria (Fajar, and Pujiyanta 2.
In practice, the Profile Matching method operates by calculating the difference .
between the actual score and the target score for each criterion.
These gap values are then converted into weighted scores based on a predefined conversion scale (Maharani and Anggraeni 2.
The Volume 6 .
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weighted scores are subsequently processed to determine the overall suitability of each alternative.
In this study, the ideal profile for each criterion was determined based on interviews with the village midwife and Posyandu cadres, who are authorized healthcare professionals responsible for assessing toddlersAo nutritional Therefore, the evaluation standards applied in this research were not subjectively defined by the researcher but were established based on professional judgment from qualified health personnel.
The evaluation process using the Profile Matching method generally consists of several stages, as Determination of Assessment Criteria and Their Weights The assessment criteria were established based on the indicators applied in Posyandu services.
These criteria were categorized into two groups:
Core Factors (CF).
Core Factors represent the primary criteria that directly determine the priority of PMT allocation.
These include Stunting status, and Nutritional status of the toddler.
Secondary Factors (SF).
Secondary Factors represent supporting criteria that influence the level of These include ParentsAo income, and MotherAos educational level.
The gap value represents the difference between the actual score of each toddler and the predetermined ideal score (Hakim.
Geasela, and Hansen 2.
The gap calculation is performed using Equation:
GAP = Alternative Value Oe Ideal value The resulting gap values were subsequently converted into numerical weights to represent the level of priority for PMT allocation.
The weight mapping scheme was determined through consultation with healthcare professionals to ensure that it accurately reflects the operational priority of nutritional intervention in community health services.
In this study, the actual values were obtained from Posyandu records and toddler health assessments conducted by healthcare personnel.
The assessment used a scoring scale ranging from 1 to 4, where a score of 4 represents the highest priority level according to the PMT eligibility criteria.
Meanwhile, the ideal value was set at 4 for each criterion as the benchmark for evaluating the conformity between the toddlerAos actual condition and the expected standard for PMT GAP mapping constitutes a fundamental procedure in the Profile Matching method, aiming to determine the difference between the actual profile values and the expected .
profile values (Pranoto et al.
This process enables the measurement of the degree of conformity between real conditions and predefined standards as the basis for subsequent evaluation.
Table 1.
GAP Value Weighting Difference (GAP) Weight Value The GAP weighting scheme was developed through the integration of relevant literature on the Profile Matching method and professional considerations from healthcare practitioners based on Posyandu service practices.
To provide a clearer understanding of the Profile Matching calculation procedure, an example of GAP computation for one toddler is presented in Tables 2 and 3.
This example illustrates how the difference between the actual value and the ideal value is converted into weighted scores that will later be used in the calculation of Core Factors and Secondary Factors.
Table 2.
Example Data Criteria Stunting Status Nutritional Status Parents Income Mother Education Ideal Value Toddler Value The GAP calculation results based on the example data are presented in Table 3.
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Criteria Table 3.
GAP Calculation Calculation Stunting Status
Nutritional Status
Parents Income
Mother Education 3Oe4
3Oe4
2Oe4
2Oe4
GAP
Weight The average Core Factor (CF) score was calculated using Equation ycAyaya = Oc ycAya Oc yaya Where:
NCF = Average Core Factor score NC = Total GAP weight score of the Core Factor criteria IC = Number of items within the Core Factor The average Secondary Factor (SF) score was calculated using Equation ycAycIya = Oc ycAycI Oc yaycI Where:
NSF = Average Secondary Factor score NS = Total weighted GAP value of the Secondary Factor criteria IS = Number of items within the Secondary Factor category Total Score Calculation and Ranking The total score of each alternative was calculated by combining the weighted Core Factor and Secondary Factor values as formulated in Equation.
ycAycN = .
cu% y ycAyay.
c% y ycAycIy.
Where:
NT = Total score NCF = Average Core Factor score NSF = Average Secondary Factor score x% = Weight of the Core Factor .
%) y% = Weight of the Secondary Factor .
%) The determination of assessment criteria and their classification into Core Factors and Secondary Factors was based on the results of interviews with the village midwife and Posyandu cadres, as well as supported by relevant literature on child nutrition assessment.
Indicators that directly reflect the nutritional condition of toddlers were categorized as Core Factors because they represent the main determinants of PMT Meanwhile, socio-economic indicators that indirectly influence the risk of nutritional problems were categorized as Secondary Factors.
RESULTS AND DISCUSSION
Analysis Results Data analysis Data analysis represents the initial stage in the implementation of a Decision Support System (DSS) (Millah et al.
In this study, the Profile Matching method was applied using data obtained from observations and interviews.
The alternative data and assessment criteria used as inputs for the decision support system calculation are presented table 4.
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Toddler Table 4.
Alternative Data and Criteria Assessment
Criteria
Status Stunting Status Gizi
Parents' Income
Risk of Stunting Good Nutrition Risk of Stunting Malnutrition Normal
Malnutrition Risk of Stunting Malnutrition Severe Stunting Malnutrition Normal
Good Nutrition Risk of Stunting Malnutrition Mother's Education SMA
SMA
SMP
SMA
SMP
SMA
Identification of Criteria and Sub-Criteria The criteria used in the assessment consisted of stunting status and nutritional status, which were classified as Core Factors, as well as parentsAo income and motherAos educational level, which were categorized as Secondary Factors.
The weighting composition of 60% for Core Factors and 40% for Secondary Factors was determined based on priority considerations in accordance with internal Posyandu policy.
Table 5.
Core Factor and Secondary Factor Criteria Criteria Factor Status Stunting Core Factor Status Gizi Core Factor Parents' Income Secondary Factor Mother's Education Secondary Factor Bobot The sub-criteria weighting scheme applied in this study was established based on empirical findings derived from direct field observations.
Table 6.
Mapping of Sub-criteria Weight Values C1 Status Stunting Status Stunting Stunting Berat Stunting Sedang Risiko Stunting Normal C2 Status Gizi Status Gizi Gizi Buruk Gizi Kurang Gizi Baik Gizi Lebih C3 Parents' Income Parents' Income < Rp 1.
Rp 1.
000 - 2.
Rp 2.
000 Ae 3.
> Rp 3.
C4 Mother's Education Mother's Education Tidak Sekolah SMP
SMA
Bobot Bobot Bobot Bobot Calculation Analysis Determination of Alternative Weight Scores Each alternative was assigned a numerical weight based on the predefined sub-criteria under each evaluation criterion.
These weight scores served as the basis for subsequent GAP calculation.
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Toddler Table 7.
Alternative Weight Values Kriteria Status Stunting Status Gizi Parents' Income Mother's Education Calculating the Difference The GAP calculation was performed to measure the degree of deviation between the actual alternative score and the predefined target score, thereby quantifying the level of conformity with the ideal standard.
Table 8.
Difference Calculation Alternatif Toddler Target Value that has been set Determination of GAP Weights After the GAP values for each prospective PMT recipient were obtained, each alternative was assigned a weighted score based on the corresponding GAP value, as presented in Table 1.
Each criterion had previously been categorized into either the Core Factor or the Secondary Factor group, with predetermined weighting proportions applied to each factor.
Alternatif Table 9.
GAP Value CF .
%) Toddler SF .
Total Score The total score represents the final value used as the basis for ranking toddlers eligible for PMT This score was obtained by combining the Core Factor and Secondary Factor values according to the predetermined weighting proportions.
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Alternatif Table 10.
Calculation Results Toddler Hasil Based on the calculation results presented in Table 9, toddler B6 obtained the highest total score of 4.
indicating the highest level of priority for receiving the Supplementary Feeding Program (PMT).
This result suggests that the toddler's condition is closest to the ideal profile defined by the evaluation criteria.
Meanwhile, toddler B5 obtained the lowest score of 3.
1, indicating a lower level of priority compared to the other alternatives.
The ranking results demonstrate how the Profile Matching method systematically evaluates each criterion and produces a measurable priority order.
By applying this approach, the decisionmaking process becomes more objective because each toddler is evaluated based on quantifiable indicators rather than solely relying on subjective judgment.
System Design According to (Arianti et al.
Unified Modeling Language (UML) is utilized as a system design tool to model the workflow of the developed Decision Support System.
The application of UML aims to provide a clear structural and functional representation of the system.
In this study, a Use Case Diagram was employed to illustrate how actors interact with the system.
Figure 2.
Use Case Diagram System Implementation The system implementation was carried out using Visual Studio Code as the development environment, with PHP as the programming language.
Login Page Implementation The login page serves as the initial interface through which users access the system.
It functions to authenticate user credentials before granting access to the main features of the application.
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Figure 3.
Login Page Dashboard Page Implementation The dashboard page presents general information about the Posyandu, including its vision and mission, organizational profile, and relevant information regarding the Supplementary Feeding Program (PMT).
This interface provides users with an overview of institutional context before proceeding to data processing and decision analysis features.
Figure 4.
Dashboard page Results Page Implementation The results page displays the output of the assessment data processing, presenting the ranked list of alternatives recommended as the basis for decision-making.
This interface provides a clear representation of the final evaluation scores and priority order generated by the system.
Figure 5.
Results Page Comparison with Manual Selection Prior to the implementation of the decision support system, the determination of PMT recipients at Posyandu Desa Perkebunan Tanah Datar was conducted manually by Posyandu cadres based on observational judgment and general consideration of toddlersAo conditions.
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health workers to use their practical experience, it often resulted in inconsistencies and difficulties when the number of eligible toddlers exceeded the available assistance quota.
By implementing the Profile Matching-based decision support system, the evaluation process becomes more structured and transparent.
Each toddler is assessed using the same criteria and scoring mechanism, which reduces the potential for subjective bias.
In addition, the system is capable of automatically generating ranking results based on calculated scores, enabling health workers to identify toddlers who should be prioritized for PMT assistance more efficiently.
Therefore, the system provides a more systematic and accountable decision-making process compared to the previous manual method.
Implementation Challenges Despite the advantages offered by the developed decision support system, several challenges may arise during its implementation.
One of the main challenges is the need for user training, particularly for Posyandu cadres who may have limited experience with digital systems.
Adequate training and guidance are necessary to ensure that users can operate the system effectively and accurately input assessment data.
Another potential challenge is system accessibility, especially in areas with limited technological infrastructure or unstable internet connectivity.
In such situations, the availability of appropriate hardware and stable network access becomes an important factor in ensuring the successful utilization of the system in routine Posyandu activities.
This study has several limitations that should be acknowledged.
First, the number of toddler samples used in this study was relatively small, consisting of only seven alternatives.
A larger dataset may provide more comprehensive evaluation results and improve the reliability of the decision-making model.
The criteria used in this study were limited to four indicators, namely stunting status, nutritional status, parentsAo income, and motherAos educational level.
Future research may consider incorporating additional indicators, such as health history or household environmental conditions, to provide a more comprehensive assessment of toddlersAo eligibility for the Supplementary Feeding Program.
System Testing (Black Box Testin.
Software testing was conducted to ensure that the developed system functions in accordance with user requirements and is capable of producing accurate and reliable outputs (Apriliandra and Nuryasin 2.
The testing method applied in this study was Black Box Testing, a software testing approach that focuses on evaluating system functionality without examining the internal code structure.
Based on the testing results, all system functions operated as intended.
Therefore, the system was deemed functionally valid and suitable for use as a decision-support tool in Posyandu health services.
No.
Test Scenario Table 11.
System Testing Expected Result Testing Result Conclusion The administrator enters a The system verifies the The system successfully Functioned 1 username and password on the credentials and displays the displayed the administrator login page dashboard page The administrator adds toddler The toddler data are stored in the system database The toddler data were successfully saved and Functioned The system performs the Profile Matching calculation The system generates GAP values and ranking results for PMT recipients The system successfully displayed the ranking Functioned The ranking data were successfully displayed Functioned The administrator views the The system displays the 4 PMT recipient recommendation priority order of PMT Based on the Black Box Testing results presented in Table 10, all system functions operated as Each tested feature successfully produced the intended output without encountering functional The login module properly authenticated users, the data input module successfully stored toddler assessment data, and the Profile Matching calculation module correctly generated GAP values and ranking These results indicate that the developed decision support system is capable of supporting the process of determining eligible toddlers for the Supplementary Feeding Program (PMT).
The system can process input data, perform automatic calculations using the Profile Matching method, and generate ranking results accurately and efficiently.
Therefore, the system is considered functionally reliable and ready to be utilized as a decision support tool in Posyandu services.
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CONCLUSION
This study demonstrates that the implementation of the Profile Matching method effectively supports the determination of toddlers eligible for PMT in a more objective and measurable manner compared to manual assessment practices at Posyandu Desa Perkebunan Tanah Datar.
The profile conformityAebased approach, which evaluates alignment between actual conditions and predefined ideal standards, enhances transparency and consistency in the selection process.
Furthermore, the developed system operated in accordance with user requirements and can be utilized as a decision-support tool within community-level healthcare services.
However, this study is limited by the relatively small dataset and its focus on a single Posyandu location, which restricts the generalizability of the findings.
Future research is recommended to apply the system to larger datasets or to conduct comparative analysis with alternative multi-criteria decision-making methods in order to evaluate model stability and robustness.
Practically, this research contributes a structured decision support model that assists Posyandu cadres in determining PMT recipient priorities more accurately and systematically.
The proposed system also has the potential to be implemented in other Posyandu or community health service centers that face similar challenges in determining PMT recipients.
By adopting a structured and data-driven evaluation approach, the system can improve the efficiency, transparency, and accountability of nutritional assistance programs.
In a broader context, the implementation of such decision support systems may support community-based health services in delivering more targeted nutritional interventions to reduce the risk of stunting and malnutrition among toddlers.
ACKNOWLEDGEMENTS
The authors gratefully acknowledge the support and research permit provided by the Posyandu (Integrated Health Pos.
in Perkebunan Tanah Datar Village, which enabled the successful completion of this study.
also extend our sincere appreciation to the principal and all staff who contributed to data collection and system evaluation.
This research was conducted as part of the academic requirements for the Information Systems Study Program at Royal University.
REFERENCES