Journal of Information System and Technology Research Volume 4.
Issue 3.
September 2025 Journal of Information System and Technology Research journal homepage: https://journal.
id/index.
php/jistr/ Web-Based Decision Support System for Superior Corn Seed Selection Using FMADM and AHP Algorithms Donny Dwi Putra1*.
Abdul Halim Hasugian2 Computer Science Department.
Faculty of Science and Technology.
State Islamic University of North Sumatra
ARTICLE INFO
ABSTRACT
Article history:
Indonesia as an agricultural country still faces challenges in meeting national corn demand due to dependency on imports.
One critical issue is the inaccurate selection of superior seeds that suit local This study aims to develop a web-based decision support system (DSS) for superior corn seed selection using the Fuzzy Multi-Attribute Decision Making (FMADM) algorithm combined with the Analytical Hierarchy Process (AHP) method.
The research was conducted in Sei Tembo Village.
Langkat Regency, with data obtained through observation, interviews with farmers, and literature The AHP method was applied to determine the weights of five criteria: water content, pest resistance, productivity, fruit size, and harvest time.
Consistency testing produced a CR value of 0.
indicating reliable weighting.
The FMADM method was then used to rank 142 seed alternatives based on these weights.
The results showed that the proposed system successfully ranked Srikandi Putih 1 (A.
as the best alternative with a score of 0.
950, while Bima5 Bantimurung (A.
had the lowest score of 0.
Productivity was identified as the dominant factor .
eight = 0.
in determining superior seeds.
These findings demonstrate that the web-based DSS can improve accuracy and objectivity in seed selection, helping farmers reduce trial-and-error decisions.
Practically, this system supports agricultural productivity improvement and contributes to strengthening national food security by reducing reliance on corn imports.
Received September 16, 2025 Accepted September 30, 2025 Available online September 30.
Keywords:
Web-Based Decision System.
FMADM.
AHP.
Corn Seed Selection.
Corn Breeding.
Agricultural Technology.
Food Securit.
Support A 2025 The Author.
Published by AIRA.
This is an open access article under the CC BY-SA license .
ttp://creativecommons.
org/licenses/by-sa/4.
0/).
Corresponding Author:
Donny Dwi Putra.
Computer Science Department.
Faculty of Science and Technology.
State Islamic University of North Sumatra.
Jl.
Lap Golf.
Kp.
Tengah.
Pancur Batu District.
Deli Serdang Regency.
Medan City.
North Sumatra.
Indonesia 20353 Email: donnydwiputra01@gmail.
INTRODUCTION
Indonesia is an agrarian country with the agricultural sector playing a strategic role in the nationals economy.
Corn is a key food commodity, serving a dual purpose: as both human food and animal feed.
However, domestic corn productivity still faces various challenges, one of which is the inaccuracy in selecting superior seeds suitable for land conditions and the growing season.
Based on information provided by the Central Statistics Agency (BPS).
Indonesia's corn imports continue to fluctuate.
In 2023, total corn imports reached USD 252,551, then decreased to USD 130,033 in 2024, and then increased again to USD 132,356 in just the first two months of 2025.
This dependence on imports indicates that domestic corn production is unable to sustainably meet national demand.
One of the main causes is the practice of selecting seeds, which is still done manually and relies on individual experience, which risks producing suboptimal decisions.
At the local level, similar challenges are encountered in Sei Tembo Village, where farmers struggle to identify superior seeds suited to their local conditions.
Declining soil quality, increased pest infestations, and low agricultural technology literacy exacerbate these issues.
Therefore, a technology-based approach is needed that can provide accurate and systematic recommendations for selecting superior corn seeds.
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Recent studies have emphasized the important role of AHP and DSS methods in supporting multi-criteria decision Shahzad et al.
used Spherical Fuzzy AHP to analyze solar energy constraints, while Bottani et al.
developed a LARG-AHP framework in the supply chain.
Soori et al .
studied the development of intelligent technology-based DSS to support adaptive and transparent decision making, demonstrating the important role of algorithms in improving decision quality, while Popovic et al.
designed an AI-based agricultural DSS with sustainability criteria.
Closer to this study.
Junaedi et al.
applied AHP to crop variety selection, but it has not been integrated into a web-based system.
This research gap indicates that the application of FMADMAeAHP in a web-based decision support system for selecting superior corn seeds is still rare, especially in the Indonesian context.
One relevant solution is the implementation of a Decision Support System (DSS) with the Fuzzy Multi-Attribute Decision Making (FMADM) algorithm and the Analytical Hierarchy Process (AHP) method.
FMADM enables decisionmaking that takes into account uncertainty and data variation, while AHP serves to determin the priority weights for each criterion, such as water content, pest resistance, productivity, fruit size, and harvest time.
The combination of these two methods is believed to produce a more objective approach in determining the best corn seed alternatives.
Previous studies have demonstrated the successful application of AHP and FMADM in various decision-making contexts, such as business location selection, zakat recipient determination, and product selection.
At the international level, several other multi-criteria decision-making (MCDM) techniques such as TOPSIS and MOORA have also been widely applied to support complex decision processes, particularly in agriculture and resource management.
However, the combined application of FMADM and AHP specifically for web-based selection of superior corn seeds is still rarely explored, creating a significant research gap.
This study explicitly addresses that gap by developing a novel web-based DSS that integrates FMADM and AHP for superior corn seed selection.
Compared to other MCDM approaches.
FMADMAeAHP offers flexibility in handling uncertainty and provides a structured framework for weighting criteria, making it well-suited for local agricultural conditions.
The main contribution of this research is the development of a system that delivers recommendations quickly, accurately, and appropriately for farmers, while also demonstrating potential for broader adaptation in global agricultural decision-making.
addition, this approach not only helps increase agricultural productivity but also contributes to strengthening national food security by reducing dependence on corn imports.
RESEARCH METHOD
This study used a quantitative approach involving 142 superior corn varieties officially released by the Ministry of Agriculture of the Republic of Indonesia.
Thus, the number of alternatives analyzed represents the entire population of official varieties without sampling.
Data regarding criteria and weights were obtained through expert interviews and literature reviews.
To ensure data consistency and reliability, a Cronbach's Alpha test was conducted with a result of > 0.
7, indicating a good level of reliability.
Next, the Analytical Hierarchy Process (AHP) method was used to determine the criteria weights, with a Consistency Ratio (CR) test result of 0.
028 O 0.
1, thus meeting the consistency limit according to the Saaty criteria.
After the weights were obtained, the Fuzzy Multi-Attribute Decision Making (FMADM) method was applied to rank the alternatives through a matrix normalization process, so that each criterion was on the same scale and the assessment results could be analyzed objectively.
Figure 1.
Research framework Based on Figure 1, the application development process is in accordance with the stages in the following subchapters.
1 Identification of Literature Study Problems The main problem in corn seed selection is inaccurate decisions due to the lack of a system capable of providing objective recommendations.
Farmers still rely on personal experience and limited information, leading to the risk of selecting suboptimal seeds.
Therefore, a Decision Support System (DSS)-based approach using the Analytical Hierarchy Process (AHP) and Fuzzy Multi-Attribute Decision Making (FMADM) methods is needed.
2 Data Collection Data was obtained through field observations of the seed selection process, interviews with farmers and agricultural experts to determine determining factors, and literature review of journals and previous research.
This JISTR.
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data was used to develop criteria and pairwise comparison matrices in the AHP and as numerical input in the FMADM.
3 Criteria and Subcriteria Weighting The AHP method is used to calculate the importance weights for each criterion and subcriteria in corn seed This process includes pairwise comparisons, weight calculations, and consistency tests to ensure the resulting weights are valid and can be used in the ranking stage.
4 Alternative Ranking FMADM is used to calculate the final score for each seedling alternative based on the weighted criteria from the AHP.
The seedling with the highest score becomes the primary recommendation as superior seedling.
5 System Implementation The decision support system was developed as a web-based application.
This phase included the implementation of the FMADMAeAHP method and system testing to ensure that the resulting recommendations meet the needs of farmers in the field.
Table 1.
AHP Method Criteria and Code
CRITERIA/ATTRIBUTE NAME
Water content
Pest Resistance Productivity Fruit Size
Harvest Time
INFORMATION
Table 1.
explains that this study uses five predetermined criteria to assess and select superior corn seeds based on various factors that influence productivity and crop quality.
The corn seeds analyzed in this study will be evaluated using The AHP method is applied to calculate the weight of importance for each criterion, which subsequently serves as input in the alternative ranking process with the FMADM method.
RESULTS AND DISCUSSION
Data analysis This study analyzed 142 superior corn varieties officially released by the Indonesian Ministry of Agriculture.
These varieties encompass local, hybrid, and composite varieties, such as Metro (A.
Baster Kuning (A.
Kania Putih (A.
, as well as modern varieties such as Pioneer (A46AeA.
Semar (A72AeA.
, and Bisi (A82AeA.
Additionally, there are NK varieties (A107AeA.
, the Bima series (A126AeA.
, and even the newest varieties such as Provit A1 (A.
and Provit A2 (A.
With this broad coverage, the alternatives used represent the complete population of superior varieties in Indonesia, ensuring a comprehensive and representative analysis.
The results indicate that Srikandi Putih 1 (A.
ranked highest with a score of 0.
950, while Bima5 Bantimurung (A.
received the lowest score of 0.
The productivity criterion was the dominant factor, with a weight of 0.
confirming that increasing crop yields is a top priority in variety selection.
This finding is consistent with research by Junaedi et al.
, which also identified productivity as a key determining criterion in crop variety selection.
However, a comparison with other studies reveals differences in focus.
While Popovic et al.
's research focused on agricultural sustainability through an artificial intelligence-based DSS, this study emphasizes the integration of web-based FMADMAeAHP, which is simple and practical for farmers to use.
Consistent with the findings of Shahzad et al.
, the effectiveness of DSS is also significantly influenced by environmental factors.
In the context of corn, agroecological condit ions such as climate, soil type, and water availability have the potential to influence variety performance in the field.
Therefore, although this system generates objective recommendations based on quantitative criteria, its use still needs to be adapted to the local knowledge of farmers and extension workers.
Thus, the results of this study not only produce objective variety rankings but also emphasize the importance of considering external environmental variability so that web-based decision support systems can be more adaptive and support increased corn productivity nationally.
AHP Method Calculation The weighting of the criteria was carried out using information obtained from research results in Sei Tembo Village.
Kuala District.
Langkat Regency.
North Sumatra Province.
Table 2 Criteria Weighting .
dopted from field data of Sei Tembo Village and weighted using AHP method by Saaty .
) INFORMATION CRITERIA/ATTRIBUTE NAME Weight Water content Pest Resistance Productivity Fruit Size JISTR.
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Harvest Time The weighting of criteria is shown in Table 2, which presents five attributes influencing superior corn seed selection based on information obtained from field research in Sei Tembo Village.
The assignment of weights follows the Analytical Hierarchy Process (AHP) scale, where values range from 1 .
east importan.
to 9 .
ost importan.
(Saaty, 1.
Productivity (C.
has the highest weight .
, indicating it is the most dominant factor, while water content (C.
has the lowest weight .
, meaning it contributes the least in the decision-making process.
Table 3 Comparison Between Criteria Criteria C1 Total Table 3 presents the pairwise comparison between criteria based on the initial weighting.
The table shows how each criterion is compared against others to determine its relative importance.
For example, productivity (C.
has higher values compared to most criteria, indicating its stronger influence in the decision-making process.
Criteria Table 4.
Normalization and Priority Weighting Priority Weight Table 4.
normalize the criteria matrix with calculations to obtain the value (C1.
by taking the value from the comparison table between criteria Table 5.
Consistency Measure.
alculated using AHP consistency testing method as described by Saaty .
) Criteria Consistency Measure Table 5.
shows the Consistency Measure (CM) values obtained by multiplying the pairwise comparison matrix with the priority weight vector.
This step follows the standard procedure in AHP consistency testing (Saaty, 1980.
Wind & Saaty, 1.
The resulting values indicate the degree of consistency in the pairwise comparisons.
Table 6.
Consistency Index Average value Consistency Index Table 6 shows the results of searching for CI (Consistency Inde.
Table 7.
Consistency Ratio Consistency Ratio Table 7 shows the consistency ratio (CR) obtained from the pairwise comparison matrix.
The CR value is 0.
which is less than 0.
1, indicating that the comparison results are consistent and valid for use in the AHP calculation.
Consisting of CI and RI, we calculate Consistency Ratio :
CR = CI / RI
= 0.
031 / 1.
= 0.
= 0.
028 < 0.
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A CR value < 0.
100 is considered consistent and more than that is inconsistent.
So the comparison given for the criteria is consistent.
FMADM Method Calculation Determine the type of criteria weighting with FMADM Table 8.
Criteria/Attribute Weighting Type Source: (Sei Tembo Village Agricultur.
Code Criteria/Attributes Type Water content Cost Pest Resistance Benefits Productivity Benefits Fruit Size Benefits Harvest Time Benefits Table 8 shows the classification of each criterion into benefit or cost type according to FMADM provisions.
Water content (C.
is categorized as a cost criterion, meaning lower values are preferred, while the other four criteria (C2AeC.
are benefit types, where higher values indicate better performance.
Table 9.
Corn Seed Assessment Based on Each Criteria/Attribute Code Name
Metro
BasterKuning Kania Putih
Malin
Harapan Bima
Pandu
Permadi Bogor Composite2
A10 Harapan Baru A11 Arjuna
A12 Bromo
A13 Parikesit
A14 Abimayu
A15 Nakula
A16 Sadewa
A17 Wiyasa
A18 Kalingga
A19 Rama
A20 Bayu
A21 Antasena
A22 Wisanggeni A23 Bisma
A24 Surya
A25 Lagaligo
A26 Gumarang
A27 Lamuru
A28 Kresna
A29 Srikandi A30 Palakka
A31 Sukmaraga
A32 Srikandi Putih 1
A33 Srikandi Kuning 1
A34 Anoman 1
A35 C1
A36 C2
A37 C3
A38 C4
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A39
A40
A41
A42
A43
A44
A45
A46
A47
A48
A49
A50
A51
A52
A53
A54
A55
A56
A57
A58
A59
A60
A61
A62
A63
A64
A65
A66
A67
A68
A69
A70
A71
A72
A73
A74
A75
A76
A77
A78
A79
A80
A81
A82
A83
A84
A85
A86
A87
A88
A89
A90
A91
A92
A93
A94
A95
A96
A97
A98
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C10
A (Andala.
Pioneer 1
Pioneer 2
Pioneer 3
Pioneer 4
Pioneer 5
Pioneer 6
Pioneer 7
Pioneer 8
Pioneer 9
Pioneer 10
Pioneer 11
Pioneer 12
Pioneer 13
Pioneer 14
Pioneer 15
Pioneer 16
Pioneer 17
Pioneer 18
Pioneer 19
Pioneer 20
Pioneer 21
Pioneer 22
Pioneer 23
IPB 4
CPI1
CPI2
Semar 1 Semar 2 Semar 3 Semar 4 Semar 5 Semar 6 Semar 7 Semar 8 Semar 9 Semar 10 Bisi-1 Bisi-2 Bisi-3 Bisi-4 Bisi-5 Bisi-6 Bisi-7 Bisi-8 Bisi-9 Bisi-10 Bisi-11 Bisi-12 Bisi-13 Bisi-14 Bisi-15 Bisi-16 Bisi-18 JISTR.
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A99 SHS 1
A100 SHS 2
A101 SHS 11
A102 SHS 12
A103 Jaya 1
A104 Jaya 2
A105 NKRI (Negara Kesatuan RI) A106 N 35
A107 NK 11
A108 NK 22
A109 NK 33
A110 NK 55
A111 NK 66
A112 NK 81
A113 NK 82
A114 NK 88
A115 NK 99
A116 DK2
A117 DK3
A118 R 01
A119 P 28
A120 P29
A121 P31
A122 JK7
A123 JK8
A124 PAC224
A125 PAC759
A126 Bima1 A127 Bima2 Bantimurung A128 Bima3 Bantimurung A129 Bima4 Bantimurung A130 Bima5 Bantimurung A131 Bima6 Bantimurung A132 Bima7 A133 Bima8 A134 Bima9 A135 Bima10 A136 Bima11 A137 Bima12Q A138 Bima13Q A139 Bima14 Batara A140 Bima15 Sayang A141 Provit A1 A142 Provit A2 Table 9 shows the assessment of each corn seed alternative on five criteria using A.
B, and C scales.
Higher ratings (A) indicate better performance, such as Srikandi (A.
, while lower ratings (C) reflect weaker Table 10.
Normalization Matrix
Code
A10
A11
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A12
A13
A14
A15
A16
A17
A18
A19
A20
Table 10 presents the normalization results of 20 corn seed alternatives as an example calculation.
Normalization is performed to equalize the scale between criteria so that the values of each alternative can be compared objectively.
The selection of 20 alternatives in the table aims to provide a representative picture of the normalization calculation results for all 142 corn seed alternatives.
The normalization process is calculated using the following formula.
Normalization Formula:
For Benefit criteria (C2.
C3.
C4.
rij = xij / max.
For Cost criteria (C.
rij = min.
/ xij Where:
rij = normalized score of the i-th alternative on the j-th criterion
xij = the score of the i-th alternative under the j-th criterion
= maxim score of the j-th criterion
= minim score of the j-th criterion
Ranking Code
A32
A80
A106
A121
A105
A16
A33
A67
A70
A64
A83
A99
A123
A141
A48
A86
A81
A84
A74
A96
A139
A142
A92
A61
A112
A87
A101
A116
A122
A72
A78
A107
Table 11.
Corn Seed Ranking Name
Srikandi Putih 1
Semar 9
BasterKuning Kania Putih
N 35
P31
NKRI
Sadewa
Srikandi Kuning 1
Pioneer 22
CPI1
Pioneer 19
Bisi-2
SHS 1
JK8
Provit A1
Pioneer 3
Bisi-5
Semar 10
Bisi-3
Semar 3
Bisi-15
Bima14 Batara
Provit A2
Bisi-11
Pioneer 16
NK 81
Bisi-6
SHS 11
DK2
JK7
Semar 1
Semar 7
NK 11
Final Score JISTR.
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A114
A59
A93
A118
A90
A60
A62
A65
A27
A28
A20
A26
A29
A36
A108
A91
A94
A95
A12
A24
A37
A18
A75
A13
A25
A34
A71
A115
A127
A133
A134
A97
A126
A35
A39
A40
A41
A76
A137
A14
A42
A113
A44
A47
A110
A22
A51
A79
A140
A23
A30
A89
A17
A53
A73
A129
A46
NK 88
Pioneer 14
Bisi-12
R 01
Bisi-9
Pioneer 15
Pioneer 17
Pioneer 20
Lamuru
Kresna
Bayu
Gumarang
Srikandi NK 22
Bisi-10
Bisi-13
Bisi-14
Bromo
Surya
Permadi Kalingga
Semar 4
Parikesit
Lagaligo
Anoman 1
CPI2
NK 99
Bima2 Bantimurung
Bima8
Bima9
Bisi-16
Bima1
Semar 5
Bima12Q
Bima
Abimayu
NK 82
C10
Pioneer 2
NK 55
Malin Wisanggeni Pioneer 6 Semar 8 Bima15 Sayang Bisma Palakka Bisi-8 Wiyasa Pioneer 8 Semar 2 Bima4 Bantimurung Pioneer 1 JISTR.
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A56
Pioneer 11
A124 PAC224
A131 Bima6 Bantimurung
A117 DK3
Pandu
A45
A (Andala.
A57
Pioneer 12
A104 Jaya 2
A11
Arjuna
A15
Nakula
A85
Bisi-4
A132 Bima7
A135 Bima10
Harapan A31
Sukmaraga
A49
Pioneer 4
A52
Pioneer 7
A98
Bisi-18
A136 Bima11
A43
A68
Pioneer 23
A88
Bisi-7
A111 NK 66
A128 Bima3 Bantimurung
Bogor Composite2
A54
Pioneer 9
A10
Harapan Baru A19
Rama
A21
Antasena
A66
Pioneer 21
A69
IPB 4
A119 P 28
A100 SHS 2
A120 P29
A125 PAC759
A82
Bisi-1
A102 SHS 12
A103 Jaya 1
A109 NK 33
A138 Bima13Q
A38
A58
Pioneer 13
A63
Pioneer 18
A50
Pioneer 5
A77
Semar 6
A55
Pioneer 10 Metro A130 Bima5 Bantimurung Table 11 shows the final ranking of corn seed alternatives based on FMADMAeAHP calculations.
The final score is calculated using the formula:
Si = .
j y ri.
Where:
Si = final score of the i-th alternative wj = weight of the jth criterion .
rom AHP result.
rij = normalized value With weights from AHP:
w1 = 0.
044 (Water conten.
w2 = 0.
228 (Pest Resistanc.
w3 = 0.
484 (Productivit.
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w4 = 0.
094 (Fruit Siz.
w5 = 0.
146 (Harvest Tim.
Final Score Calculation Example for A1 (Metr.
S1 = .
044 y 0.
228 y 0.
484 y 0.
094 y 0.
146 y 0.
S1 = 0.
S1 = 0.
Based on the calculation results, the alternative with the highest score is A32 (Srikandi Putih .
with a value of 0.
950, which indicates that the variety has the best performance based on the five criteria used.
Conversely, the alternative with the lowest score is A130 (Bima5 Bantimurun.
with a value of 0.
A score close to 1 indicates that the variety has values close to the maximum on the benefit criterion and the minimum on the cost criterion, so this ranking system can be used as a basis for recommendations in determining superior corn seeds.
The discovery of several alternatives with identical final scores is normal in the FMADM method, which is caused by the same or very similar normalization values due to the similarity of initial values, constant criteria weights .
uch as productivity with a dominant weight of 0.
, and the use of fixed value categories such as a scale of 1Ae4 or AAeD which limits the variation of value combinations between alternatives.
Results of system implementation This testing stage is a stage that is intended to find out whether each function in the system is functioning according to the design that was made.
In the testing stage, it is carried out by using a web application with a web browser media, namely Google Chrome.
The following are the results of the tests carried out:
The results of the study showed that productivity .
was the most dominant factor in selecting superior corn This finding aligns with research by Nazilah et al.
found that the Bisi variety was superior at different research locations, indicating that the growing environment significantly influences variety performance.
Thus, this FMADMAHP-based decision support system helps tailor seed recommendations to local conditions.
Overall, this study confirms the superiority of the FMADMAeAHP approach over traditional subjective experiencebased methods.
The results are consistent with previous studies.
This study demonstrates the feasibility of adopting this method to support food security through the selection of superior seeds.
However, the variation in results between studies also highlights the importance of this system being flexible and regularly updated with local data to maintain its relevance to local agroecological conditions.
Decision Support System Login View.
Figure 2.
Login Page Figure 2 displays the login page of the decision support system for superior corn seed selection.
This page is used by users or administrators to enter the system by filling in their email and password.
The purpose of this interface is to ensure secure access before managing or retrieving recommendation data.
Displays the criteria, weight, type and priority weight that have been inputted according to the research results.
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Figure 3.
Criteria Data Figure 3 displays the criteria, their weights, and types based on the research results.
Priority weights indicate the level of importance, with higher values indicating a more dominant criterion.
The lower the Cost, the better, while the higher the Benefit, the better.
Display alternatives that have been inputted according to research results.
Figure 2.
Alternative Data Figure 4 displays alternative data in the form of corn seed varieties used in the study.
The purpose is to demonstrate the seed options that will be evaluated based on predetermined criteria.
The data is read by looking at the seed code, name, and description.
All alternatives are displayed in an active state for further calculation Presents the AHP computation results to identify the priority weights of each criterion through the pairwise comparison JISTR.
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Figure 3.
AHP Calculation Figure 5 shows AHP calculation to determine priorities weights of criteria through paired comparisons.
The matrix values indicate the comparison between criteria, while the normalization results provide the final weights for each criterion.
Displays the results of the FMADM calculation Figure 4.
FMADM Calculation Figure 6 displays the results of the FMADM calculation used to rank alternatives based on weighted criteria.
This is read by looking at the total score for each alternative, with the highest score indicating the best alternative.
Displaying Corn Seed Ranking JISTR.
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Figure 5.
Ranking of Superior Corn Seeds Figure 7 displays the ranking results of superior corn seeds based on the final calculated scores.
The goal is to determine the best alternative, with the highest score indicating the most recommended seed.
The ranking is determined by looking at the ranking order and score for each seed.
CONCLUSION
This study demonstrates that the integration of the FMADM method with the AHP approach can objectively support the selection of superior corn seeds.
The results indicate that productivity is the most dominant criterion, followed by pest resistance, harvest time, fruit size, and moisture content.
Among the 142 varieties analyzed.
Srikandi Putih 1 (A.
achieved the highest score .
, while Bima5 Bantimurung (A.
obtained the lowest .
These findings confirm that the system is capable of providing structured recommendations that align with farmersAo needs.
However, this research has limitations, particularly in not explicitly considering environmental variability such as soil conditions and regional climate, which may affect field performance.
For future development, the system could be enhanced with machine learning techniques to process larger and more diverse datasets, while integration into national agricultural policies would increase its scalability and contribute directly to strengthening IndonesiaAos food security.
REFERENCES