EduMatika: Jurnal MIPA Vol.
No.
Desember 2025, pp.
ISSN: 2721-3838.
DOI: 10.
30596/jcositte.
Application of the TOPSIS Method for Electronic Supplier Selection Based on Multi-Criteria Chairunnisa Selviani1.
Dewi Aslamiah Nasution2.
Nabila Amelia3.
Nadya Putri Zulida4.
Nisya Aulia Salsabila5.
Muhammad Romi Syahputra6 1,2,3,4,5,6Universitas Sumatera Utara Email: icaanisaa1502@gmail.
ABSTRAK
Penelitian ini bertujuan menentukan supplier komponen elektronik yang paling optimal dengan pendekatan kuantitatif berbasis kinerja.
Metode yang digunakan adalah TOPSIS dengan empat kriteria utama, yaitu harga, kualitas .
efect rat.
, ketepatan pengiriman, dan keberlanjutan lingkungan.
Analisis terhadap lima supplier menghasilkan nilai closeness coefficient sebagai berikut: S5 sebesar 0,7777.
S3 sebesar 0,6712.
S1 sebesar 0,5524.
S2 sebesar 0,4310, dan S4 sebesar 0,3895.
Hasil ini menempatkan S5 sebagai alternatif terbaik, diikuti S3 dan S1.
Dampak penelitian ini adalah meningkatkan objektivitas dan akurasi proses pemilihan supplier serta meminimalkan risiko keputusan yang bias.
Berdasarkan temuan tersebut, penelitian merekomendasikan strategi dual sourcing dengan alokasi 60% pasokan untuk S5 dan 30% untuk S3, sementara S1 dapat dikembangkan sebagai opsi jangka menengah melalui program peningkatan kualitas dan pengurangan tingkat cacat.
Keyword: Supplier.
Elektronik.
TOPSIS.
Kriteria.
Kualitas ABSTRACT This study aims to determine the most optimal electronic component supplier using a quantitative performance-based approach.
The method used is TOPSIS with four main criteria, namely price, quality .
efect rat.
, delivery accuracy, and environmental Analysis of five suppliers produces the following closeness coefficient values: S5 of 0.
S3 of 0.
S1 of 0.
S2 of 0.
4310, and S4 of 0.
These results place S5 as the best alternative, followed by S3 and S1.
The impact of this study is to increase the objectivity and accuracy of the supplier selection process and minimize the risk of biased decisions.
Based on these findings, the study recommends a dual sourcing strategy with an allocation of 60% of supplies to S5 and 30% to S3, while S1 can be developed as a medium-term option through quality improvement and defect rate reduction programs.
Keyword: Supplier.
Electronics.
TOPSIS.
Criteria.
Quality Corresponding Author:
Chairunnisa Selviani.
Universitas Sumatera Utara.
Jl.
Dr.
Mansyur No.
Medan.
Kota Medan.
Sumatera Utara 20155.
Indonesia Email: icaanisaa1502@gmail.
INTRODUCTION
The global electronic components industry reached a value of approximately US$338.
87 billion in 2024 and is projected to grow to US$500.
93 billion by 2030, with a compound annual growth rate (CAGR) of This significant growth requires companies to be increasingly selective in selecting suppliers to maintain Supplier selection decisions directly impact final product quality, cost efficiency, and the sustainability of a company's operations.
A major challenge in this process arises from conflicts between assessment parameters.
Low-cost suppliers are not always able to provide the best quality, while high-quality suppliers typically command higher Effective supplier quality management has even been shown to reduce defect rates by up to 46%.
Furthermore, global pressure for environmental sustainability makes this aspect increasingly important to consider as part of a company's social responsibility and long-term strategy.
Journal homepage: https://jurnal.
id/index.
php/EMJU EduMatika: Jurnal MIPA Supplier selection is a crucial activity in supply chain management, as it determines business partners capable of meeting quality standards, competitive pricing, and reliable distribution.
Complexity arises when various assessment factors conflict, requiring a systematic approach to avoid suboptimal decisions, which can ultimately negatively impact product quality and customer satisfaction.
TOPSIS (Technique for Order Preference by Similarity to Ideal Solutio.
is a proven MCDM method for solving supplier selection problems.
This method excels in calculation efficiency, application flexibility, and its ability to produce stable and objective rankings by comparing each alternative against both positive and negative ideal solutions.
Several previous studies have demonstrated the success of TOPSIS implementation in supplier Gunawan .
developed a TOPSIS-based decision support system with five parameters and successfully obtained accurate rankings.
Menon and Ravi .
combined AHPAeTOPSIS to identify sustainable suppliers in the electronics industry.
Qu et al.
applied fuzzy TOPSIS, considering economic, social, and environmental aspects.
However, a research gap remains regarding how to comprehensively integrate environmental sustainability criteria with operational and economic parameters in the electronic components industry.
Based on this gap, this study asks the main question: "How can we accurately rank electronic component suppliers while simultaneously considering quality, economics, distribution reliability, and environmental sustainability?" The main objective of this study is to produce objective supplier rankings through a multi-criteria evaluation encompassing product quality, economic efficiency, distribution reliability, and environmental Thus, this study contributes to improving decision-making quality, reducing assessment subjectivity, and strengthening the development of MCDM-based decision support systems in supply chain management and strategic relationship management with business partners.
RESEARCH METHOD
1 Research Type and Data This study uses a quantitative approach with a descriptive-analytical method to evaluate five electronic component supplier options.
It should be noted that the data used is simulated data, not real data from specific This simulated data was created based on references from previous research on supplier performance in the electronics industry, as an example of the application of the TOPSIS method.
There are three main reasons for using simulated data in this study.
First, to protect strategic and confidential business information.
Second, to create a controlled experimental environment, allowing the TOPSIS method process to be explained clearly and easily understood.
Third, the simulated data facilitates the replication of the study because the parameters used are clear and Although the data used is simulated, the method's framework remains valid and can be applied to real-world data in the future.
Five electronic component suppliers, labeled S1 to S5, were analyzed in this study, each representing different performance characteristics, reflecting industry conditions.
The performance data was organized to represent realistic values based on previous related research.
2 Assessment Criteria and Weighting Four criteria were used in this study, with weights determined based on previous research.
Table 1 below summarizes the assessment criteria and their characteristics.
Table 1.
Supplier Assessment Criteria Code Criteria Name Type Weight Information Price Cost Factors that directly influence production costs and company profits Product Quality Benefit Important factors for customer satisfaction and (Defect Rat.
company reputation Delivery Accuracy Benefit It is important for smooth operations and supply Environmental Benefit Increasingly important due to environmental Sustainability regulations and corporate social responsibility Price represents a crucial economic dimension that directly impacts production costs.
Product quality is measured by defect rates and is given the highest weight due to its role in customer satisfaction and building a company's reputation.
On-time delivery is crucial for maintaining smooth operations and avoiding supply (Chairunnisa Selvian.
A EduMatika: Jurnal MIPA chain disruptions.
Environmental sustainability is becoming increasingly important due to increasingly stringent environmental regulations and demands for corporate social responsibility.
3 TOPSIS Method The TOPSIS method is used to rank suppliers based on the concept that the best supplier is the one with the shortest distance from the positive ideal solution and the furthest distance from the negative ideal The following are the steps in the TOPSIS method:
Step 1: Create an Initial Decision Matrix The decision matrix X is constructed based on the criteria values for each supplier:
ycu11 ycu12 U ycu1ycu
ycu21 ycu22 U ycu2ycu
ycU=[ U
U ]
ycuyco1 ycuyco2 U ycuycoycu where ycu_ycnyc is the value of the ycn th supplier on the yc th criterion, yco is the number of suppliers and ycu is the number of criteria.
Step 2: Normalizing the Decision Matrix Normalization is performed using the vector normalization method:
ycuycnyc ycycnyc = Oo Ocyco ycn=1 ycuycnyc where yc_ycnyc is the normalized matrix element ycI.
Step 3: Create a Weighted Normalized Decision Matrix Each column of the R matrix is multiplied by the criterion weight wj:
ycycnyc = ycyc y ycycnyc ycu where Ocyc=1 ycyc = 1 Step 4: Determine the Positive and Negative Ideal Solutions Positive ideal solution .
a ):
ya = .
c1 , yc2 .
A , ycycu } ycyc = max .
cycnyc ) if yc is the benefit criterion ycyc = min .
cycnyc ) if yc is the cost criterion Negative ideal solution .
aOe ):
ycycOe = min .
cycnyc )if yc is the benefit criterion yaOe = .
c1Oe , yc2Oe .
A , ycycuOe } ycycOe = max .
cycnyc ) if yc is the cost criterion Step 5: Calculating Euclidean Distance ycu yaycn = oc yc=1 .
cycnyc Oe ycyc )2 The distance of each alternative from the negative ideal solution:
ycu yaycnOe = oc yc=1 Step 6: Calculating the Closeness Coefficient yayaycn = .
cycnyc Oe ycycOe )2 yaycnOe yaycn yaycnOe where 0 O yayaycn O 1 Step 7: Ranking Alternatives The alternative with the highest yayaycn value is the best alternative.
4 Sensitivity Analysis EduMatika: Jurnal MIPA Vol.
No.
Desember 2025: 58 Ae 64 EduMatika: Jurnal MIPA To test the robustness of the ranking results, a sensitivity analysis was conducted on four criteria weighting scenarios reflecting different strategic priorities: Original Scenario .
alanced weight.
Focus on Quality .
CC = 0.
Priority on Cost .
CA = 0.
, and Focus on Sustainability .
CE = 0.
RESULTS AND DISCUSSION
1 Supplier Simulation Data The simulation dataset used in this research is designed to represent the real-world conditions of electronic component suppliers with varying levels of capability.
Table 2 presents the performance profiles of the five alternative suppliers analyzed.
Table 2.
Electronic Component Supplier Performance Data (Simulatio.
Supplier Company name C1: Price C2: Defect C3: On-Time C4: Green (Rp/uni.
Rate (%) Delivery (%) Index PT Elektronika Jaya CV Mitra Komponen PT Techno Supply UD Global Electronics PT Green Component The information in Table 2 shows the differences in capabilities between suppliers.
Supplier S2 has the most competitive price of Rp 570,000, but at the expense of a relatively high defect rate of 3.
performs very well with the lowest defect rate .
8%) and high on-time delivery .
%), despite its higher price of Rp 780,000.
S5 received the highest sustainability score .
, indicating a strong commitment to environmental aspects.
S4 performs the worst in most assessment aspects.
2 TOPSIS Method Calculation Decision Matrix Normalization The normalization procedure is executed using the vector normalization formula.
For example, for element S1 in the price parameter (C.
yc11 = = 0.
Oo675.
The same process is performed for all elements of the matrix, resulting in a normalized matrix R.
Weighted Normalized Matrix Each element of the normalized matrix is multiplied by the corresponding criterion weight.
For element S1 in the price criterion:
yc11 = yc1 y yc11 = 0.
25 y 0.
4456 = 0.
The results of the weighted normalized matrix calculation are shown in Table 3.
Table 3.
Weighted Normalized Matrix Supplier Positive and Negative Ideal Solutions Based on the type of criteria .
ost or benefi.
, positive and negative ideal solutions are determined:
A C1 (Harga - Cos.
: ya = 0.
, yaOe = 0.
A C2 (Defect Rate - Cos.
: ya = 0.
, yaOe = 0.
A C3 (On-Time Delivery - Benefi.
: ya = 0.
, yaOe = 0.
A C4 (Green Index - Benefi.
: ya = 0.
, yaOe = 0.
Distance and Closeness Coefficient Calculation The distance of each supplier to the positive and negative ideal solutions is calculated using the Euclidean distance formula.
For example, for S1:
(Chairunnisa Selvian.
A EduMatika: Jurnal MIPA ya1 = Oo.
1114 Oe 0.
1106 Oe 0.
1142 Oe 0.
0902 Oe 0.
= 0.
ya1Oe = Oo.
1114 Oe 0.
1106 Oe 0.
1142 Oe 0.
0902 Oe 0.
= 0.
yaya1 = = 0.
The same calculations were performed for all suppliers, and the final results are shown in Table 4.
3 Supplier Ranking Results Table 4.
TOPSIS Calculation Results and Final Ranking Ranking Supplier Company name Category PT Green Component 0.
7777 Very good PT Techno Supply Good PT Elektronika Jaya 6738 Pretty good CV Mitra Komponen 0.
3005 not enough UD Global Electronics 0.
1946 Inadequate Table 4 shows that PT Green Component (S.
received the top ranking with the maximum proximity coefficient value .
This value indicates that S5 has the closest distance to the positive ideal solution and the furthest distance from the negative ideal solution compared to other competitors.
S5's superiority comes from a good combination of three main aspects: the highest sustainability score .
, the lowest defect rate .
1%), and good delivery accuracy .
%), despite its price being at a moderate level (Rp 720,000/uni.
PT Techno Supply (S.
ranked second with a CC of 0.
This supplier demonstrates excellent operational performance with the lowest defect rate .
8%) and the highest delivery accuracy .
%).
However, its premium pricing strategy (Rp 780,000/uni.
presents a trade-off that must be considered.
S3 is suitable for applications that prioritize maximum reliability and resilience to quality failures.
PT Elektronika Jaya (S.
, ranked third (CC = 0.
, demonstrated balanced performance across all parameters with no significant weaknesses.
This supplier could be a realistic choice for medium-priority components that do not require specific top-tier performance.
CV Mitra Komponen (S.
and UD Global Electronics (S.
ranked at the bottom with CC scores of 3005 and 0.
1946, respectively.
These two suppliers demonstrated significant weaknesses in quality .
igh defect rate.
and sustainability .
ow green inde.
, making them not recommended for establishing long-term strategic relationships.
4 Analysis of Trade-offs Between Criteria The evaluation results demonstrate a typical trade-off pattern in multi-criteria decision-making (MCDM), consistent with the DSS concept, which emphasizes that every purchasing decision involves performance trade-offs across dimensions.
The finding that S2 has the lowest price (Rp 570,.
but the highest defect rate .
8%) reinforces the principle that decisions in the electronics supply chain cannot rely solely on cost parameters, as poor quality can increase the costs of both internal and external failures.
Conversely.
S3, with the highest price (Rp 780,.
but the lowest defect rate .
8%), demonstrates the cost quality trade-off phenomenon often mentioned in Total Cost of Ownership (TCO) theory.
Calculations show that a 1% reduction in the defect rate requires an additional cost of Rp 70,000 Rp 75,000 per unit, consistent with the literature that improving the reliability of electronic components requires significant marginal cost investments.
In the sustainability dimension, the cost per green point comparison shows that S5 (Rp 8,182/poin.
is more efficient than S3 (Rp 9,512/poin.
These findings support the ecological DSS theory that environmental performance can be optimized without excessive cost increases if suppliers have mature manufacturing processes.
No supplier excels across all parameters, so selection decisions must follow the compensatory model principle in DSS that is, strengths in one aspect can offset weaknesses in others.
In this context.
S5 is the most balanced supplier because it consistently ranks second or third in almost all criteria.
This phenomenon is important in the electronics industry, which is highly sensitive to quality and continuity of supply, where suppliers with balanced performance tend to present lower operational risks.
5 Sensitivity Analysis A sensitivity analysis was conducted to assess decision robustness across various weighting scenarios, in accordance with the DSS principle that decisions should be stable despite changes in preferences or external The results show that S5 remained the highest ranked in three of the four scenarios (Original.
Price EduMatika: Jurnal MIPA Vol.
No.
Desember 2025: 58 Ae 64 EduMatika: Jurnal MIPA Priority.
Sustainabilit.
, indicating strong decision stability.
The very low Coefficient of Variation (CV) for S5, 3.
08%, demonstrates that S5's closeness coefficient is insensitive to weighting changes.
This reflects the supplier's comprehensive and adaptive characteristics to various strategic demands.
The Quality Focus scenario .
CC = 0.
ranked S3 first with a CC = 0.
7924, consistent with qualitydriven supply chain theory, where suppliers with the highest reliability automatically receive the highest preference scores when quality is given a high weighting.
Meanwhile, in the Sustainability scenario .
CE = .
S5 achieved the highest CC .
, aligning with green supply chain management literature that emphasizes the importance of ecological efficiency in the electronics industry, which faces stringent regulations regarding emissions and waste.
On the other hand.
S4 consistently ranked last in all scenarios, indicating a systemic internal problem that cannot be addressed simply by changing the criteria weights.
The sensitivity results indicate that the decision is not only accurate in a single weighting scenario but also robust within the DSS framework, making it suitable for use as a basis for long-term strategic recommendations.
6 Strategic Recommendations Based on the analysis results and DSS-based decision-making principles, a dual sourcing strategy is recommended to minimize risk, increase supply resilience, and optimize costs and quality in the electronics industry supply chain.
S5 is recommended as the primary supplier with a 60% volume allocation due to its most balanced characteristics, stability in sensitivity analysis, and efficiency in the sustainability dimension critical attributes in the electronics industry facing demands for sustainability, innovation speed, and consistent production batches.
S3 with the best quality performance, is allocated 30% for critical components requiring low defect rates and high delivery accuracy.
This aligns with supplier segmentation theory, which states that high-quality suppliers should be used for high-risk modules to prevent systemic defects in electronic products.
S1 is recommended as a supplier development strategy due to its relatively high potential.
The 12month performance improvement targets reducing the defect rate from 2.
5% to 2.
0%, increasing the green index from 75 to 80, and increasing on-time delivery from 92% to 93% are designed to align with continuous improvement standards in the electronics industry.
If the target is achieved.
S1's volume can be increased to 15Ae20% as a form of risk diversification.
S2 and S4 are recommended to be gradually reduced due to their performance not meeting quality and delivery standards, as well as high operational risk.
S2 can be retained as a backup supplier with 100% quality inspection to prevent quality leakage, while S4 is recommended to be discontinued within six months because its performance demonstrates unacceptable instability in the electronic supply chain system.
CONCLUSION
This study demonstrates the effectiveness of the TOPSIS method for selecting electronic component suppliers, considering economic, quality, delivery, and sustainability aspects.
PT Green Component (S.
was selected as the best supplier with a closeness coefficient of 0.
7777, followed by PT Techno Supply (S.
with a value of 0.
These results emphasize the importance of balancing performance, particularly quality and environmental aspects, in supply chain decision-making.
This study provides theoretical contributions that strengthen TOPSIS's role as part of an objective and consistent decision support system.
A quality weight of 0.
30 is the most dominant factor and aligns with the concept of TQM, while the relationship between quality improvement and investment of IDR 70,000AeIDR 75,000 per 1% reduction in defect rate supports the concept of TCO.
This study also provides practical benefits by demonstrating the stability of supplier rankings.
S5 and S3 remained in the top two positions across all sensitivity scenarios with Coefficients of Variation of 3.
51%, respectively.
These findings can be used as a basis for developing procurement strategies, supplier segmentation, and risk management.
Further research can focus on integrating TOPSIS with weighting methods such as AHP or ANP, validating it with electronics industry data, and developing a web-based decision support system to facilitate operational implementation of the model.
REFERENCES