Muhammad Faizal, Agung Triayudi*, Rima Tamara Aldisa
Fakultas Teknologi Komunikasi dan Informatika, Sistem Informasi, Universitas Nasional, Jakarta Jl. Sawo Manila No.61, RW.7, Pejaten Bar., Ps. Minggu, Kota Jakarta Selatan, Daerah Khusus Ibukota Jakarta, Indonesia Email: 1muhammadfaizal24242@gmail.com, 2,*agungtriayudi@civitas.unas.ac.id, 3rima.tamara@civitas.unas.ac.id
Email Penulis Korespondensi: agungtriayudi@civitas.unas.ac.id Submitted: 17/01/2023; Accepted: 28/01/2023; Published: 29/01/2023
Abstract−In the business world, choosing the right business location is one of the main things that must be considered, the problem that occurs is choosing a business location that is not right for the business actors themselves. Therefore, an application is needed that helps business actors to determine the location. strategic for the business they are involved in. In this research, a decision support system application was made to make it easier for business actors to determine the location according to the criteria. Decision support applications are considered effective enough to create a ranking in determining strategic locations for business actors. The Weighted Product and SMART methods using 7 alternative data shows that the two methods produce data that is accurate and suitable when applied as a ranking for selecting business locations. The 2 methods have an elective execution score and the results of the weight values applied to each technique.
Keywords: Business Location; Decision Support System; Weighted Product; SMART
1. INTRODUCTION
In the current business world, especially with the advancement of technology that facilitates entrepreneurs in innovating, many business actors have started their businesses with small-scale enterprises such as SMEs. Micro, Small, and Medium Enterprises have been growing significantly every year [1]. For business owners, there are many important aspects to consider when starting a business. One crucial aspect is determining a strategic business location. The business location is considered crucial because it affects the profitability, growth, and convenience of the business [2].
Business owners often face challenges, such as the cost of capital, including location rental fees and inadequate facilities for their business premises [3]. Moreover, when choosing a business location, entrepreneurs must also consider the target market in that area. The population density and the presence of competitors and supporting businesses in the surrounding area are important factors in determining a strategic business location [4].
Based on the above conclusions, the author has decided to develop a system that can provide rankings to determine the right business location for SME entrepreneurs. A decision support system is deemed appropriate for addressing the problem of selecting a strategic business location. The methods used in this decision support system application are the Weighted Product and SMART methods, as they are considered suitable and optimal for the location decision-making problem [5].
The Weighted Product method, also known as the Weighted Product Model, is a decision-making method that aims to connect basic values, where each initial value needs to be multiplied by the weight of the relevant criteria. In previous research, a web-based decision support system for employee recruitment using the Weighted Product method was developed. This study aimed to recommend new employees by considering five alternative data [6]. Another study implemented the Weighted Product method in determining the best faculty members in the Computer Science Academy in Ternate. The research successfully ranked the alternative data using the Weighted Product method [7].
The SMART method is an adaptive linear model that assesses alternatives. Due to its ease of use and the ability to analyze responses, the SMART method is widely used. Transparency is considered the best analysis as it offers a comprehensive understanding of the problem and is acceptable to decision-makers. In previous research, the use of the Simple Multi-Attribute Rating Technique (SMART) in a decision support system for recommending majors was studied. Supratman’s research resulted in major recommendations using the SMART method [8].
The focus of this research is to determine a strategic business location using a decision support system with the Weighted Product and SMART methods. The study will use eight criteria: crowd density, competitors, facilities, rental prices, access, supplier distance, supporting businesses, and parking space availability at the business location. In previous research, the design of a decision-making system for determining the location of XYZcell store using the Weighted Product method resulted in faster calculation of criteria values and rankings for each alternative [9]. Another study successfully determined a strategic location for a new branch of Azuri Water using the ORESTE method in a decision support system [10]. The development of a location search system for businesses using the SMART method for new entrepreneurs showed that the method can be implemented to search for locations more quickly without affecting data accuracy and objectivity [11]. Additionally, Arya Permadi conducted research on a decision support system for determining the location of a new shoe laundry business at BECKS using the WP method. The study highlighted that the decision support system designed for the shoe laundry business at BECKS is not an absolute decision, and the assessment is subject to the involved parties [12].
The implementation of a decision support system using these two methods is expected to help business actors determine strategic and appropriate business locations according to their specific needs.
2. RESEARCH METHODOLOGY
Figure 1 illustrates the research design using the Weighted Product and SMART methods to determine Business Locations in DKI Jakarta Province.
Figure 1. Research Design Description:
a. Problem Identification
The initial stage is problem identification, where the researcher identifies a specific problem to be researched. This step is taken to ensure that the researcher can identify scientific problems that can be solved. This phase is designed based on the problem statement and is grounded in the background of the problem.
b. Data Collection
There are three techniques used to collect data for this research, namely observation, interviews, and literature study. (1) Interviews: Interviews were conducted by directly asking UMKM business owners to gather information for this research. (2) Observation: Observation was carried out by directly visiting several business locations in DKI Jakarta province to obtain valid data for this research. (3) Literature Study: Literature study was conducted to study and understand the theories used, including factors that are prerequisites for Decision Support Systems, the Weighted Product Method, the SMART Method, Micro, Small, and Medium Enterprises, and Business Locations.
c. Application Design using WP and SMART Methods
After the data collection stage, the next step is application design. Based on the literature studied and understood, the goal is to determine strategic business locations for UMKM. The Weighted Product and SMART methods are employed in this application to address the identified problems.
d. Application Development
The application development phase is the main part of the research process, as it involves solving problems and processing the collected data using the Weighted Product and SMART methods.
e. Application Implementation
The implementation phase involves applying the developed application to the ongoing research.
2.1 Metode Weight Product (WP)
The Weighted Product method can be defined as a multi-criteria decision analysis method that is widely used and falls under the category of Fuzzy Multiple Attribute Decision Making (FMADM) methods. It is a method for multi-criteria decision making and can be interpreted as a finite set of decision alternatives described by several decision criteria [13].
a. Improvement or normalization
Normalizing or correcting the weights to ensure that they add up to Wj = 1, where j = 1,2, …, n is the number of other options and Wj is the absolute sum of the weighted values [14].
b. Determining the Value of the Vector S.
Determine the value of vector S by duplicating each criterion with one more weighted improvement or standardization that has a positive ranking for benefit measures and a negative ranking for cost measures. Here, S represents the model choices, X denotes the alternatives, and n is the number of alternatives [15].
c. Determine the value of vector V.Menentukan Nilai Vektor V
Determine a value vector V where V is an elective decision that will be used to determine the position of each value vector S in relation to the absolute vector of the total S [16].
2.2 SMART Method
SMART is a method that uses linear adaptive model. In this method, the value of each alternative is predicted by SMART using the adaptive linear model. Due to its ease of meeting decision-maker’s needs and analyzing response, SMART method is widely used. Transparency is the best analysis as it offers a comprehensive understanding of the problem and is acceptable to decision-makers. Using a scale from 0 to 1, SMART weighting facilitates calculation and comparison of results for each alternative [17]. The stages of the SMART method are as follows:
a) Determine the criteria to be used.
b) Assign weight values to each criterion.
c) Normalize the weight values of the criteria.
d) Assign parameter values for each criterion.
e) Determine the Utility values [18].
Explanation
ui (ai): The utility value of criterion i for alternative i.
cmax: the maximum value of the criterion
cmin: the minimum value of the criterion
cout: the value of criterion i
f) Calculating the Final Value𝑚
Explanation
u(ai) : The total value for alternative i
wj : The normalized weight value for criterion j
uj(ai) : The utility value of criterion j for alternative i
3. RESULTS AND DISCUSSION
3.1 Location Selection Testing Data
Finding the value of the V-vector result, the preference of alternatives that are ultimately used to rank each total value of the S-vector.
Tabel 1. Alternative Location Data
| Alternative | Description |
| 1 | Kalibata |
| 2 | Pasar Minggu |
| 3 | Condet |
| 4 | Cilandak |
| 5 | Jagakarsa |
| 6 | Srengseng Sawah |
| 7 | Lenteng Agung |
From each obtained data, the weights will be determined. Here is the table of weights for each data:
a. Kriteria Keramaian
Tabel 2. Weight of the Criterion “Crowdedness”
| Crowdedness | Weight |
| Very Crowded | 100 |
| Crowded | 80 |
| Moderate | 60 |
| Quiet | 40 |
| Very Quiet | 20 |
In the table above, it shows the values of the sub-criteria, where the highest value for each sub-criteria is 100 and the lowest value is 20. The weight assigned to the crowdedness criterion is 20%.
b. Competitor Criteria
Tabel 3. Bobot Kriteria Kompetitor
| Competitor | Weight |
| None | 100 |
| 1 Competitor | 80 |
| 2 Competitors | 60 |
| 3 Competitors | 40 |
| ≥ 3 Competitors | 20 |
In the table above, it shows the values of the sub-criteria, where the highest value for each sub-criteria is 100 and the lowest value is 20. The weight assigned to the competitor criterion is 20%.
c. Facilities Criteria
Tabel 4. Weight of Facilities Criteria
| Facilities | Weight |
| Very Complete | 100 |
| Complete | 80 |
| Sufficient | 60 |
| Insufficient | 40 |
| Very Poor | 20 |
In the table above, it shows the values for each sub-criterion, where the highest value for each sub-criterion is 100 and the lowest value is 20. The weight assigned to the facilities criterion is 15%.
d. Price Criterion
Tabel 5. Weight of Price Criterion
| Price | Weight |
| Very Cheap | 100 |
| Cheap | 80 |
| Moderate | 60 |
| Expensive | 40 |
| Very Expensive | 20 |
In Table 5, it shows the values for the sub-criteria, where the highest value for each sub-criterion is 100 and the lowest value is 20. The weight assigned to the accessibility criterion is 15%.
e. Accessibility Criterion
Tabel 6. Weight of Access Criterion
| Access | Weight |
| All Types | 100 |
| 2 Cars | 80 |
| 1 Car | 60 |
| Motorcycle | 40 |
| None | 20 |
In Table 5, it shows the values for the sub-criteria, where the highest value for each sub-criterion is 100 and the lowest value is 20. The weight assigned to the accessibility criterion is 15%.
f. Supplier distance criteria
Tabel 7. Bobot Kriteria Jarak Pemasok
| Supplier Distance | Weight |
| Very close | 100 |
| Near | 80 |
| Close enough | 60 |
| Far | 40 |
| Very far | 20 |
Table 7 shows the values for the sub criteria, the largest value of each sub criterion is 100 and the smallest value is 20. The weight assigned to the supplier criterion is 5%.
g. Supporting Business Criteria
Tabel 8. Supporting Business Criteria Weight
| Supporting Business | Weight |
| Very good | 100 |
| Good | 80 |
| Not good | 60 |
| Less | 40 |
| Very less | 20 |
Table 8 shows the value of the sub-criteria, the largest value of each sub-criterion is 100 and the smallest value is 20. The weight assigned to supporting business criteria is 5%.
h. Parking Area Criteria
Tabel 9. Parking Area Criteria Weight
| Parking Lot | Weight |
| Very wide | 100 |
| Wide | 80 |
| Small | 60 |
| Very small | 40 |
| There isn’t any | 20 |
Table 9 shows the values for the sub criteria, the largest value of each sub criterion is 100 and the smallest value is 20. The weight assigned to the parking area criterion is 10%.
3.2 Perhitungan Weighted Product
Tabel 10. Tabel Rating WP
| ALT | C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 |
| 1 | 100 | 20 | 100 | 20 | 100 | 80 | 100 | 100 |
| 2 | 60 | 40 | 80 | 40 | 80 | 60 | 80 | 60 |
| 3 | 60 | 60 | 80 | 60 | 60 | 60 | 60 | 60 |
| 4 | 100 | 40 | 60 | 20 | 80 | 80 | 80 | 80 |
| 5 | 100 | 80 | 80 | 80 | 100 | 100 | 100 | 100 |
| 6 | 80 | 40 | 60 | 80 | 100 | 100 | 100 | 80 |
| 7 | 60 | 60 | 40 | 80 | 60 | 80 | 40 | 20 |
The preference weights taken in this weighted product decision table are: 20%, 20%, 10%, 5%, 5%, 10%, 5%, 15%. This weight was obtained from interviews between the author and MSME actors. The following is a sample taken from Temusapa coffee shop SMEs which can be seen in the table below.
Table 11. WP Method Ranking Results
| ALT | Results | Rank |
| 5 | 0,202 | 1 |
| 6 | 0,157 | 2 |
| 3 | 0,141 | 3 |
| 2 | 0,128 | 4 |
| 4 | 0,128 | 5 |
| 1 | 0,127 | 6 |
| 7 | 0,018 | 7 |
In the following table, the results of the calculation of the weighted product method are shown. Where at 11 shows that alternative 5 is the best alternative in calculating wp. So that the best alternative is suitable as a business location that is assessed in the Weighted Product calculation. From the calculations that have been done manually and the calculations obtained by the application produce similar values, so the authors state that the Weihted Product method has been successfully applied.
3.3 Calculation of the SMART method
Table 12. Calculation of Utility Value
| ALT | C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 |
| 1 | (100- 60)/(100- 60) | (20- 20)/(80- 20) | (100- 40)/(100- 40) | (20- 20)/(80- 20) | (100- 60)/(100- 60) | (80- 60)/(100- 60) | (100- 40)/(100- 40) | (100- 20)/(100- 20) |
| 2 | (60- 60)/(100- 60) | (40- 20)/(80- 20) | (80- 40)/(100- 40) | (40- 20)/(80- 20) | (80- 60)/(100- 60) | (60- 60)/(100- 60) | (80- 40)/(100- 40) | (60- 20)/(100- 20) |
| 3 | (60- 60)/(100- 60) | (60- 20)/(80- 20) | (80- 40)/(100- 40) | (60- 20)/(80- 20) | (60- 60)/(100- 60) | (60- 60)/(100- 60) | (60- 40)/(100- 40) | (60- 20)/(100- 20) |
| 4 | (100- 60)/(100- 60) | (40- 20)/(80- 20) | (60- 40)/(100- 40) | (20- 20)/(80- 20) | (80- 60)/(100- 60) | (80- 60)/(100- 60) | (80- 40)/(100- 40) | (80- 20)/(100- 20) |
| 5 | (100- 60)/(100- 60) | (80- 20)/(80- 20) | (80- 40)/(100- 40) | (80- 20)/(80- 20) | (100- 60)/(100- 60) | (100- 60)/(100- 60) | (100- 40)/(100- 40) | (100- 20)/(100- 20) |
| 6 | (80- 60)/(100- 60) | (40- 20)/(80- 20) | (60- 40)/(100- 40) | (80- 20)/(80- 20) | (100- 60)/(100- 60) | (100- 60)/(100- 60) | (100- 40)/(100- 40) | (80- 20)/(100- 20) |
| 7 | (60- 60)/(100- 60) | (60- 20)/(80- 20) | (40- 40)/(100- 40) | (80- 20)/(80- 20) | (60- 60)/(100- 60) | (80- 60)/(100- 60) | (40- 40)/(100- 40) | (20- 20)/(100- 20) |
The preference weights taken in this SMART decision table are: 20%, 20%, 15%, 15%, 10%, 5%, 5%, 10%. This weight was obtained from interviews between the author and MSME actors, the sample was taken from the Temusapa Cofeeshop Business.
Tabel 13. Utility calculation results
| ALT | C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 |
| 1 | 1 | 0 | 1 | 0 | 1 | 0.5 | 1 | 1 |
| 2 | 0 | 0.333 | 0.667 | 0.333 | 0.5 | 0 | 0.667 | 0.5 |
| 3 | 0 | 0.667 | 0.667 | 0.667 | 0 | 0 | 0.333 | 0.5 |
| 4 | 1 | 0.333 | 0.333 | 0 | 0.5 | 0.5 | 0.667 | 0.75 |
| 5 | 1 | 1 | 0.667 | 1 | 1 | 1 | 1 | 1 |
| 6 | 0.5 | 0.333 | 0.333 | 1 | 1 | 1 | 1 | 0.75 |
| 7 | 0 | 0.667 | 0 | 1 | 0 | 0.5 | 0 | 0 |
Table 13 is a table of the results of calculating the utility value, when you get the utility value, then determine the final result value which can be seen in the table below.
Table 14. SMART Method Preference Value
| Alt | Calculation of Final Result Value | Weight |
| 1 | ((100-60)/(100-60) x 0.2) + ((20-20)/(80-20) x 0.2) + ((100-40)/(100-40) x 0.15) + ((20-20)/(80- 20) x 0.15) + ((100-60)/(100-60) x 0.1) + ((80-60)/(100-60) x 0.05) + ((100-40)/(100-40) x 0.05) + ((100-20)/(100-20) x 0.1) | 0.625 |
| 2 | ((60-60)/(100-60) x 0.2) + ((40-20)/(80-20) x 0.2) + ((80-40)/(100-40) x 0.15) + ((40-20)/(80- 20) x 0.15) + ((80-60)/(100-60) x 0.1) + ((60-60)/(100-60) x 0.05) + ((80-40)/(100-40) x 0.05) + ((60-20)/(100-20) x 0.1) | 0.35 |
| 3 | ((60-60)/(100-60) x 0.2) + ((60-20)/(80-20) x 0.2) + ((80-40)/(100-40) x 0.15) + ((60-20)/(80- 20) x 0.15) + ((60-60)/(100-60) x 0.1) + ((60-60)/(100-60) x 0.05) + ((60-40)/(100-40) x 0.05) + ((60-20)/(100-20) x 0.1) | 0.4 |
| 4 | ((100-60)/(100-60) x 0.2) + ((40-20)/(80-20) x 0.2) + ((60-40)/(100-40) x 0.15) + ((20-20)/(80- 20) x 0.15) + ((80-60)/(100-60) x 0.1) + ((80-60)/(100-60) x 0.05) + ((80-40)/(100-40) x 0.05) + ((80-20)/(100-20) x 0.1) | 0.5 |
| 5 | ((100-60)/(100-60) x 0.2) + ((80-20)/(80-20) x 0.2) + ((80-40)/(100-40) x 0.15) + ((80-20)/(80- 20) x 0.15) + ((100-60)/(100-60) x 0.1) + ((100-60)/(100-60) x 0.05) + ((100-40)/(100-40) x 0.05) + ((100-20)/(100-20) x 0.1) | 0.95 |
| 6 | ((80-60)/(100-60) x 0.2) + ((40-20)/(80-20) x 0.2) + ((60-40)/(100-40) x 0.15) + ((80-20)/(80- 20) x 0.15) + ((100-60)/(100-60) x 0.1) + ((100-60)/(100-60) x 0.05) + ((100-40)/(100-40) x 0.05) + ((80-20)/(100-20) x 0.1) | 0.642 |
| 7 | ((60-60)/(100-60) x 0.2) + ((60-20)/(80-20) x 0.2) + ((40-40)/(100-40) x 0.15) + ((80-20)/(80- 20) x 0.15) + ((60-60)/(100-60) x 0.1) + ((80-60)/(100-60) x 0.05) + ((40-40)/(100-40) x 0.05) + ((20-20)/(100-20) x 0.1) | 0.308 |
After the calculation process is complete, then a relative calculation is carried out where later the value of the result will be the pranking value in the SMART method. Table of calculation results and ranking can be seen in table 15.
Table 15. SMART Method Ranking Results
| ALT | Results | Rank |
| 5 | 0,95 | 1 |
| 6 | 0,642 | 2 |
| 1 | 0,625 | 3 |
| 4 | 0,5 | 4 |
| 3 | 0,4 | 5 |
| 2 | 0,35 | 6 |
| 7 | 0,308 | 7 |
From the table above, several alternatives from the ranking results have the same results as the results from manual calculations, so the authors conclude that this method can be applied in selecting MSME business locations.
3.4 Implementasi Sistem
The results of the design that has been made in this study is the application of selecting strategic business locations for MSME actors using the Weighted product and SMART methods.
a. Main Menu Display
In the main view of the application that has been made there is a menu adding admin, location, rating, results and logout.
Figure 2. Main Menu Display
b. Location Menu Display
This location menu is used to add alternative locations.
Figure 3. Location Menu Display
c. Analysis View
Analysis Display is a display to provide an assessment of each predetermined criteria and location. Can be seen in the image below.
Figure 4. Analysis view
d. WP Calculation Display
The appearance of the calculation of the Weighted Product is a display of the calculation process for each criterion value and weight that has been determined using the Weighted Product Method.
Figure 5. Calculation display
e. SMART Calculation Display
The SMART calculation display is a display of the calculation process for each criterion value and weight that has been determined using the SMART method.
Figure 6. Display of SMART Calculations
f. Display Comparison Results
Display of comparison results is a display of comparison results from the Weighted Product and SMART methods.
Figure 7. Display of Comparison Results
4. CONCLUSION
Based on the research that has been done, the comparison of the WP and SMART methods on 7 alternative data shows that the two methods get accurate data and are suitable when applied as a business location selection ranking. In the first rank is occupied by the alternative Jagakarsa location by obtaining the highest score with the 2 methods, the similarity in ranking is obtained from the alternative performance scores and the criteria weight values applied to the 2 methods. In the research results the authors suggest using the Weighted method Product.
REFERENCES
- M. Ikhlas and L. Jafnihirda, “Sistem Pendukung Keputusan, MFEP, UMKM, UML,” vol. 7, no. 2, pp. 240–253, 2021, [Online]. Available: http://202.62.11.57/index.php/cogito/article/view/318
- D. Diana, S. O. Kunang, and I. Seprina, “Sistem Pendukung Keputusan Pemilihan Lokasi Usaha Jasa Umkm Menerapkan Analytical Hierarchy Process –Weighted Product Method,” J. Ilm. Matrik, vol. 22, no. 2, pp. 224–231, 2020, doi: 10.33557/jurnalmatrik.v22i2.1005.
- N. A. Rahayu, B. S. Ginting, and M. Simanjuntak, “Sistem Pendukung Keputusan Seleksi Penerimaan Bantuan Program Sembako Menggunakan Metode Smart (Simple Multi Attribute Rating Technique) (Studi Kasus : Dinas Sosial Kota Binjai),” J. Sist. Inf. Kaputama, vol. 5, no. 1, pp. 63–74, 2021.
- T. A. Nadila, S. Andryana, and I. D. Sholihati, “Analisa Perbandingan Metode MOORA, Promethee, dan Weighted product dalam Penentuan Lokasi Usaha,” J. JTIK (Jurnal Teknol. Inf. dan Komunikasi), vol. 5, no. 3, p. 282, 2020, doi: 10.35870/jtik.v5i3.185.
- A. S. Manalu, “Dengan metode SMART ( Simple Multi Attribute Dengan metode SMART ( Simple Multi Attribute Rating Technique ) Berbasis Web ( Studi,” 2018.
- M. S. . Mahendra, S. M.A, and M. . Suyadnya, “Pegawai Dengan Metode Weighted Product,” J. SPEKTRUM, vol. 7, no. 1, pp. 90–96, 2020.
- I. DJUFRI, M. H. Abdullah, and S. Turuy, “Implementasi Metode Weighted Product (Wp) Dalam Sistem Pengambilan Keputusan Dosen Terbaik Teknik Komputer Akademi Ilmu Komputer Ternate,” JSAI (Journal Sci. Appl. Informatics), vol. 3, no. 3, pp. 175–182, 2020, doi: 10.36085/jsai.v3i3.1166.
- E. Supratman, “Penggunaan Metode Simple Multi Attribut Rating Technique (Smart) Pada Sistem Penunjang Keputusan Rekomendasi Jurusan Studi Kasus : Siswa Smk N 5 Palembang,” J. Informanika, vol. 7, no. 2, pp. 105–112, 2021.
- E. Herliani and Y. Salim, “Sistem Pengambilan Keputusan Penentuan Lokasi Toko Xyzcell Menggunakan Weight Product,” Respati, vol. 16, no. 3, p. 61, 2021, doi: 10.35842/jtir.v16i3.419.
- M. Suroso et al., “Water Menggunakan Metode Oreste”.
- A. D. Kurniadi and D. R. Prehanto, “Rancang Bangun Sistem Pencarian Lokasi Usaha Dengan Metode Smart Bagi Pengusaha Baru,” JEISBI (Journal Emerg. Inf. Syst. Bus. Intell., vol. 02, no. 02, pp. 24–29, 2021.
- A. Permadi, Z. Panjaitan, and S. Kusnasari, “Sistem Pendukung Keputusan Penentuan Lokasi Baru Usaha Laundry Sepatu di BECKS Menggunakan Metode WP (Weighted Product),” J. Cyber Tech, vol. 1, no. 3, pp. 1–11, 2021, [Online]. Available: https://ojs.trigunadharma.ac.id/index.php/jct/article/view/4760
- Burhanuddin and Dini, “Beasiswa Dengan Menggunakan Metode Weighted Product,” Univ. Sari Mutiara Indones., vol. 2, no. 2, pp. 83–87, 2017.
- P. M. Kusumantara, M. Kustyani, and T. Ayu, “Pendukung Keputusan Pemilihan Wedding Organizer Di,” Tek. Eng. Sains J., vol. 3, no. I, pp. 19–24, 2019.
- E. P. Silmina, “Penentuan Zona Risiko COVID-19 Menggunakan Simple Additive Weighting,” vol. 5, no. 02, pp. 88– 97, 2021.
- H. N. Fakhriyah, “Implementasi Metode Weighted Product pada Sistem Pendukung Keputusan Analisa Kenaikan Jabatan di BKPSDM Kota Probolinggo,” 2021, [Online]. Available: http://etheses.uin-malang.ac.id/26446/
- J. Sembiring, . Ambiyar, and U. Verawardina, “Implementasi Metode Simple Multi Attributerating Technique (SMART) dalam Keputusan Pemilihan Model Pembelajaran di Masa Pandemi Covid-19,” Techno.Com, vol. 20, no. 2, pp. 232– 244, 2021, doi: 10.33633/tc.v20i2.4381.
- R. Andri Agus, M. Ardiansyah Sembiring, M. Istaufa Arif Sinaga, and S. Royal, “Penerapan Metode Smart Dalam Menentukan Lokasi Toko Roti,” J. Sci. Soc. Res., vol. 4307, no. 2, pp. 442–449, 2022, [Online]. Available: http://jurnal.goretanpena.com/index.php/JSSR
- A. Elia, A. Fadilah, M. Fitriani, and P. Suryani, “Perbandingan Metode SMART, SMARTER dan TOPSIS dalam Pemilihan Lokasi Toko Serba Murah Pulau Kijang: Comparison of SMART, SMARTER AND TOPSIS …,” … Indones.
- J. …, vol. 1, no. October, pp. 170–176, 2021, [Online]. Available: https://journal.irpi.or.id/index.php/malcom/article/view/140%0Ahttps://journal.irpi.or.id/index.php/malcom/article/dow nload/140/71
- Martine, “Penerapan Metode Weighted Product (Wp) Dalam Sistem Pendukung Keputusan Pemilihan Laptop Berdasarkan Kebutuhan Spesifikasi Calon Pembeli Berbasis Website,” 2018.