JURNAL RISET INFORMATIKA Vol. 5, No. 1 December 2022 P-ISSN: 2656-1743 |E-ISSN: 2656-1735 DOI: https://doi.org/10.34288/jri.v5i1.493 Accredited rank 3 (SINTA 3), excerpts from the decision of the Minister of RISTEK-BRIN No. 200/M/KPT/2020 129 COMPARISON OF SAW, WP, AND TOPSIS METHODS IN DETERMINING THE BEST JOURNALISTS N.I.S. Baldanullah1, Febrianti Adhania2, Desti Fitriati3 Program Studi Teknik Informatika1,2,3 Fakultas Teknik Universitas Pancasila1,2,3 Jakarta, Indonesia baldanullah55@gmail.com1, febriadh@gmail.com2, desti.fitriati@univpancasila.ac.id3 (*) Corresponding Author Abstract Journalists are human resources that have a significant influence on journalistic companies. A system is needed to support the company's decision to select and measure its reporters. PT. Inipasti Communika is one of the journalistic companies that has never previously measured and assessed its journalists, so it has difficulty assessing and measuring its journalists. This study aims to provide a solution using the Decision Support System in decision-making using the SAW, WP, and TOPSIS methods and provide the final decision results based on comparing these methods. This study uses criteria and criteria values from these companies. The company's data related to its journalists is the privacy of PT. It is a Community, so the alternative value used is dummy data that is still by the original standards of the company's data. This study concludes that the three methods can provide the best alternatives with the same results. Keywords: Decision Support System; SAW Method; WP Method; TOPSIS Method Abstrak Wartawan merupakan sumber daya manusia yang memiliki pengaruh besar pada perusahaan jurnalistik. Diperlukan suatu sistem dalam mendukung keputusan perusahaan tersebut untuk memilih dan mengukur wartawan mereka. PT. Inipasti Communika adalah salah satu perusahaan jurnalistik yang sebelumnya belum pernah mengukur serta menilai wartawan mereka, sehingga mengalami kesulitan dalam melakukan penilaian serta pengukuran pada wartawan mereka. Penelitian ini bertujuan untuk memberikan suatu solusi menggunakan Decision Support System dalam pengambilan keputusan dengan menggunakan metode SAW, WP dan TOPSIS serta memberikan hasil akhir keputusan berdasarkan perbandingan metode tersebut. Penelitian ini menggunakan kriteria dan nilai kriteria dari perusahaan tersebut. Data perusah aan terkait wartawan mereka merupakan privasi PT. Inipasti Communika sehingga nilai alternatif yang digunakan merupakan data dummy yang masih sesuai dengan standar asli data perusahaan tesebut. Penelitian ini memberikan kesimpulan bahwa ketiga metode yang digunakan mampu memberikan alternatif terbaik dengan hasil yang sama. Kata kunci: Sistem Penunjang Keputusan; Metode SAW; Metode WP; Metode TOPSIS INTRODUCTION A company with high-quality human resources is an advantage for all companies. A company's progress depends on its employees' performance, so it becomes essential to advancing a company(Hafiz & Ma’mur, 2018). A company certainly has various kinds of benchmarks in assessing their employees’ work results. However, not all companies, especially those still developing, have a tool and system for measuring the performance and quality of their employees. PT. Inipasti Communika is an incorporated mass media company based online that provides information or news about something in general. Media companies are very dependent on their journalists because they rely on the quality and quantity of a piece of news. This company has never previously measured and assessed the journalists they have, making it difficult to measure and assess their reporters. This research tries to provide a method for overcoming the company's problems in deciding to assess and measure the performance and achievements of their journalists so that the conclusions and results will be a reference in assessing the best journalists in the company. P-ISSN: 2656-1743 | E-ISSN: 2656-1735 DOI: https://doi.org/10.34288/jri.v5i1.493 JURNAL RISET INFORMATIKA Vol. 5, No. 1 December 2022 Accredited rank 3 (SINTA 3), excerpts from the decision of the Minister of RISTEK-BRIN No. 200/M/KPT/2020 130 This research uses a Decision Support System (DSS) to provide conclusions supporting a specific goal for the company. A Decision Support System (DSS) is a system that can provide effective problem-solving so that the results can help in decision-making obtained from the results of processing existing information using various methods. This system is designed to support all decision-making stages, identify problems, select the necessary data, determine the models and approaches used in the decision-making process, and evaluate results(Aisyah & Putra, 2021). The decision support system has various methods that are used; this research uses Simple Additive Weighting (SAW), Weight Product (WP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods used to help PT. This is the case for Communika in determining a particular decision based on the results of the ranking decision against the company's journalists through the decision support system. Simple Additive Weighting (SAW) is a method that seeks the weighted summation of the rating in each alternative on all criteria by normalizing the decision matrix into a scale that compares with all existing alternative ratings (Syarief & Suwandana, 2018). Weight Product (WP) is a method of decision-making using multiplication between predetermined criteria values that are previously the value of each existing criterion raised to the rank of a predetermined criterion (Rizal et al., 2021). TOPSIS is a method that can provide problem-solving with structured and unstructured conditions. This method provides a prediction and information that can be a benchmark in making a more appropriate decision by choosing alternatives to calculate the nearest value (Hutasuhut et al., 2021). This study will compare decisions and provide suggestions regarding the results of which journalists' rankings will be used from the method used as a benchmark for PT. Inipasti Communika assists the company in achieving specific goals based on what is produced by the decision support system using the method carried out. Previously, research has been carried out on accuracy comparison using the SAW and WP. TOPSIS methods where this accuracy comparison if the relevant company already has data on the results of previous decisions and made a new decision using existing methods, as in research (Supiyan, 2019). This study compares TOPSIS and SAW, WP methods in determining BMT EL-Raushan financing. The research concluded that comparing the simple additive weighting Method, Weighted Product, and TOPSIS methods shows that the WP method is more accurate than the SAW and TOPSIS methods. It seems that from the three methods' accuracy level values, the WP method's highest accuracy with an accuracy value of 94%. The research conducted by (Kungkung & Haryadi Kiswanto, 2018)aims to analyze the comparison of SAW, WP, and TOPSIS using hamming distance in the case study of the selection of new students at SPP Negeri Kupang with the conclusion that based on these three methods, methods that are close to the results of accurate decisions of related parties are the SAW and TOPSIS methods. However, the SAW and TOPSIS methods are closer to the results of the school's decision. These three methods are feasible to be used by the Kupang State Agricultural Tuition in processing new student admissions to support obtaining objective verdict results. As for the research using a single method, such as (Noval et al., 2020), this research uses the Simple additive Weighting method to determine the best employees at PT. Persada Nusantara Telekomunikasi with the hope of being able to provide a choice objectively for the company. The results offer options with the best value from several alternatives tested using five criteria and show that selecting the best employees is not only indicated by one criterion. Still, some criteria also have a competency value according to existing criteria. Research conducted by (Salim et al., 2022) uses the TOPSIS method to determine the best employees at PT. Regency Motor, the company, often experiences several problems, such as the calculation of employee criteria values that experience similarities with each other and take a long time in their calculations. It is not uncommon for errors in the computation of values, so the author uses the TOPSIS method to overcome these problems using six criteria, and the results provide results based on the TOPSIS calculations carried out. Research is also conducted by (Sihaloho et al., 2022) using the Weighted Product method in selecting the best employees on the CV. Neosoft Art Medan, in the study, the problem faced was that managers at the company had difficulty assessing employee performance, so the author helped the manager by using the weighted product method with five criteria. The study concluded that using the wp method could speed up selecting the best employee accurately and make it easier for the manager to decide the company's above employees. The research only uses methods in a single decision support system based on various studies JURNAL RISET INFORMATIKA Vol. 5, No. 1 December 2022 P-ISSN: 2656-1743 |E-ISSN: 2656-1735 DOI: https://doi.org/10.34288/jri.v5i1.493 Accredited rank 3 (SINTA 3), excerpts from the decision of the Minister of RISTEK-BRIN No. 200/M/KPT/2020 131 that have been carried out in determining and choosing decisions related to the best employees in a company. In this study, three methods will be carried out, namely Simple Additive Weighting (SAW), Weight Product (WP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), so that it are expected to provide conclusions where the results can give a ranking comparison that can provide strong confidence based on the calculation results for PT. This is Communika. RESEARCH METHODS This research was carried out with stages, namely data collection, then continued with the calculation stages on various calculation methods. Here is Figure 1 flow chart of research methods. Figure 1. Research method flowchart Data Acquisition Based on data obtained from PT. Inipasti communika, here is Table 1 related to the criteria for creating a Decision Support System using existing methods. Table 1. Criteria and Weights Code Criteria Weight Attribute C1 Number of News Releases 35 Benefit C2 Average of News Views 30 Benefit C3 Violation of the Code of Ethics 25 Cost C4 Language Skills 10 Benefit C5 Length of Service 5 Cost Based on the table above, five criteria are criteria for assessing journalists at PT. Inipasti communika with the following information: 1. The number of news releases is the total number of news written by journalists and has been published by the release team in 1 entire month 2. Average news views are the average person who reads the news written by each journalist in 1 entire month 3. Violation of the Code of Ethics is how many ethical violations have been committed by journalists since becoming journalists. 4. Language skills are how much of a language journalists speak. 5. Length of Service is how long the journalist worked at the PT. Inipasti Communika. Here is table 2 of weight values on each criterion. Table 2. Weights of Each criterion Criteria Crips Weight of Crips Number of News Releases <30 30-50 51-70 >70 10 20 30 40 Average of News Views <100 101-500 501-1000 >1000 10 20 30 40 Violation of the Code of Ethics 0 1 2 >2 10 20 30 40 Language Skills 1 2 3 >3 10 20 30 40 Length of Service 2-6 month 6-12 month 1-2 years >2 years 10 20 30 40 PT. Inipasti Communika does not want to open alternative data to the public. The company only provides data references, and researchers will use dummy data which can still be used as a standard according to actual company data. Table 3 displays alternative data that will be used in this study. P-ISSN: 2656-1743 | E-ISSN: 2656-1735 DOI: https://doi.org/10.34288/jri.v5i1.493 JURNAL RISET INFORMATIKA Vol. 5, No. 1 December 2022 Accredited rank 3 (SINTA 3), excerpts from the decision of the Minister of RISTEK-BRIN No. 200/M/KPT/2020 132 Table 3. Alternative Values Alternative C1 C2 C3 C4 C5 A1 A2 A3 A4 A5 A6 A7 56 36 63 24 52 60 44 764 1122 342 648 984 498 1068 2 1 3 2 1 3 1 1 2 2 1 2 1 2 7 15 36 4 16 11 27 Simple Additive Weighting (SAW) In the SAW method, there are two attributes, such as the benefit criterion and cost criteria (cost). Both criteria are the basis for the selection of criteria when making decisions. Method SAW is a widely used method to complete the retrieval of Decisions practically (Hermanto & Izzah, 2018) The Simple Additive Weighting (SAW) method is used to find optimal alternatives from some alternatives with specific criteria. The definition of the Simple Additive Weighting (SAW) method is often also known as the weighted summation method. The basic concept of the SAW method is to look for a weighted summation of the performance rating on each alternative on all attributes. This method requires normalizing the decision matrix X to a scale that can be compared with all alternative ratings (Wijaya & Insan, 2018). π‘Ÿπ‘–π‘— = { π‘₯𝑖𝑗 π‘€π‘Žπ‘₯𝑖 π‘₯𝑖𝑗 (𝑏𝑒𝑛𝑒𝑓𝑖𝑑) 𝑀𝑖𝑛𝑖π‘₯𝑖𝑗 π‘₯𝑖𝑗 (π‘π‘œπ‘ π‘‘) ........................ (1) Information: Rij: Normalized performance rating value Xij: The attribute value that each criterion has Max xij: The most significant value of each criterion Min xij: The smallest value of each criterion The preference value for each alternative (Vi) can be seen in the following equation: 𝑉𝑖 = βˆ‘ 𝑀𝑗 π‘Ÿπ‘— 𝑛 𝑗=1 ............................................. (2) Information: Vi: Rankings for each alternative wj: The weight value of each criterion rij: Normalized performance rating value Figure 2 is a flowchart of the Simple Additive Weighting (SAW) method. Figure 2. SAW Flowchart Weighted Product (WP) The WP method is called dimensioned analysis because its mathematical structure eliminates units of measure. The WP method is a finite set of decision alternatives described in some respects as decision criteria. So this method does not need to be normalized. This method has several advantages. Namely, variable costs and benefits help determine the criteria influencing decisions. This method is more straightforward than others because the calculation is not so complex and easier to understand (Novira et al., 2020). The Weighted Product (WP) method seeks decisions by multiplying to relate attribute ratings, where the attribute must first be raised to the rank of the attribute in question. In the WP method, normalization is performed before multiplying each attribute's value. The value of weights that are profit (benefit), then the value of the lift is positive while the cost (cost) of the lifting is negative (Rani et al., 2021). The determination of the normalized weight value with the symbol W can be seen in the following formula: 𝑀𝑗 = 𝑀𝑗 βˆ‘ 𝑀𝑗 ............................................................ (3) JURNAL RISET INFORMATIKA Vol. 5, No. 1 December 2022 P-ISSN: 2656-1743 |E-ISSN: 2656-1735 DOI: https://doi.org/10.34288/jri.v5i1.493 Accredited rank 3 (SINTA 3), excerpts from the decision of the Minister of RISTEK-BRIN No. 200/M/KPT/2020 133 The determination of the value of the vector S can be seen in the following formula: 𝑆𝑖 = ∏ π‘₯𝑖𝑗 𝑀𝑗𝑛 𝑗=1 ............................................................ (4) Information: S: Alternative preference by analogy as vector S x: Criterion value w: Weight of criteria i: Alternatives j: Criteria n: Many criteria The determination of the value of the vector V can be seen in the following formula: 𝑣𝑖 = ∏ π‘₯ 𝑖𝑗 𝑀𝑗𝑛 𝑗=1 ∏ π‘₯ 𝑗 𝑀𝑗𝑛 𝑗=1 ............................................................... (5) Information: V: Alternative preference with vector analogy V x: Criterion value w: Weight of criteria i: Alternatives j: Criteria n: Many criteria Figure 3. Weight product flowchart Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) Designing a decision support system using the TOPSIS method is one of the choices in this study because it can rank selected alternatives, where the best-selected alternatives have the shortest distance from the positive ideal solution and the longest distance from the negative ideal solution. A positive ideal solution is defined as a solution that maximizes the profit attribute and minimizes the cost attribute, while a negative ideal solution is defined as a solution that minimizes the profit attribute and maximizes the cost (Sugiarto, 2021). Here is the formula for forming a normalized decision matrix: π‘Ÿπ‘–π‘— = π‘₯𝑖𝑗 βˆšβˆ‘ (π‘₯𝑖𝑗) 2π‘š 𝑖=1 ............................................................. (6) Information: rij: Normalized attribute values xij: The value of each attribute m: The value of the attributes available for each criterion Here is the formula for creating a normalized and weighted decision matrix: 𝑦𝑖𝑗 = π‘Ÿπ‘–π‘— 𝑀𝑗 ........................................................................ (7) Information: yij: Weighted normalization rij: Normalized attribute values wj: Criterion value Here is the formula for determining the distance between the values of each alternative with a matrix of positive and negative ideal solutions: 𝐷+ = βˆšβˆ‘ (𝑦𝑖 βˆ’ 𝑦𝑖𝑗 +)2π‘šπ‘–=1 ............................................... (8) π·βˆ’ = βˆšβˆ‘ (𝑦𝑖 βˆ’ 𝑦𝑖𝑗 βˆ’)2π‘šπ‘–=1 ................................................ (9) Information: D+: Positive ideal D-: Negative ideal Here is the formula for determining the preference value for each alternative: 𝑉𝑖 = 𝐷𝑖 βˆ’ 𝐷𝑖 βˆ’+𝐷𝑖 + . ..................................................................... (9) P-ISSN: 2656-1743 | E-ISSN: 2656-1735 DOI: https://doi.org/10.34288/jri.v5i1.493 JURNAL RISET INFORMATIKA Vol. 5, No. 1 December 2022 Accredited rank 3 (SINTA 3), excerpts from the decision of the Minister of RISTEK-BRIN No. 200/M/KPT/2020 134 Figure 4. TOPSIS flowchart Result and Evaluation Based on the ranking results from the three methods carried out, this stage will combine all the results into one table, and an evaluation will be carried out regarding which method and which results will be a reference for PT. Inipasti Communika. RESULTS AND DISCUSSION Simple Additive Weighting (SAW) Based on the research methods that have been carried out, the following is the result of the match rating of each alternative: Table 4. Result of match Rating of each alternative Value of SAW Alternative C1 C2 C3 C4 C5 A1 A2 A3 A4 A5 A6 A7 30 20 30 10 30 30 20 30 40 20 30 30 20 40 30 20 40 30 20 40 20 20 10 20 10 20 20 10 20 30 40 10 30 20 40 After analyzing the suitability of each alternative, then calculate and make a matrix of its normalization. Table 5. Normalization Matrix of SAW Alternative C1 C2 C3 C4 C5 A1 A2 A3 A4 A5 A6 A7 1 0.66 1 0.33 1 1 0.66 0.75 1 0.5 0.75 0.75 0.5 1 0.66 1 0.5 0.66 1 0.5 1 1 0.5 1 0.5 1 1 0.5 0.5 0.33 0.25 0.1 0.33 0.5 0.25 After creating the normalization matrix, it then determines the preferences of each alternative shown in the following table: Table 6. Preference for Each Alternative of SAW Alternative Total A1 A2 A3 A4 A5 A6 A7 83.33333333 80 71.25 57.5 89.16666667 72.5 79.58333333 Based on the preference results of each alternative above, an alternative with the highest value, namely the A5 alternative, is obtained. Weighted Product (WP) The following is a table of normalized weight values: Table 7. normalization of weight values Criteria Total C1 C2 C3 C4 C5 0.35 0.30 0.20 0.10 0.5 Total 1 JURNAL RISET INFORMATIKA Vol. 5, No. 1 December 2022 P-ISSN: 2656-1743 |E-ISSN: 2656-1735 DOI: https://doi.org/10.34288/jri.v5i1.493 Accredited rank 3 (SINTA 3), excerpts from the decision of the Minister of RISTEK-BRIN No. 200/M/KPT/2020 135 After normalizing the value of the criterion weight, next, calculate the vector S. Table 8 is the result of the value of the vector S: Table 8. Vector value S of WP S Total S1 S2 S3 S4 S5 S6 S7 25.37674499 25.17092464 17.65389941 17.22810213 29.40334233 20.61250884 25.83518789 Total 161.2807102 After determining the value and the total number of S values, next determine the vector value V, whose results are shown in the following Table 9: Table 9. Vector value V of WP V Values of V V1 V2 V3 V4 V5 V6 V7 0.1573451962 0.1560690339 0.1094606998 0.1068205994 0.1823115876 0.1278051715 0.1601877116 Total 1 The results of the vector value V above show the highest value in V5, where V5 represents the alternative A5 with the highest value. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) Based on the value of the criterion weight and alternative values in the research method chapter, the following is the result of the calculation of the divisor value and the calculation of the normalized matrix: Table 10. Divisor value for normalization Criteria Divisor C1 C2 C3 C4 C5 131.2135664 2176.311559 5.385164807 4.358898944 51.88448708 The divisor value in Table 10 above is the result of calculation by summing each alternative value on a criterion that is ranked two, then the result of the summation is rooted. After performing the divisor calculation, calculate the normalization matrix against the alternate data by dividing the alternate data by the divider. Here is Table 11 the results: Table 11. TOPSIS Normalization Matrix Table Alternative C1 C2 C3 C4 C5 A1 A2 A3 A4 A5 A6 A7 0.4267 0.2743 0.4801 0.1829 0.3963 0.4572 0.3353 0.3510 0.5155 0.1571 0.2977 0.4521 0.2288 0.4907 0.3713 0.1856 0.5570 0.3713 0.1856 0.5570 0.1856 0.4588 0.2294 0.4588 0.2294 0.4588 0.4588 0.2294 0.1349 0.2891 0.6938 0.077 0.3083 0.2120 0.5203 Divisor 131.21 2176.3 5.3851 4.3588 51.884 Next, create a weighted normalization matrix with the results shown in Table 12 as follows: Table 12. weighted normalization matrix Alternative C1 C2 C3 C4 C5 A1 A2 A3 A4 A5 A6 A7 14.937 9.6010 16.804 6.4010 13.870 16.004 11.736 10.531 15.464 4.7140 8.9325 13.564 6.8648 14.722 7.4278 3.713 11.141 7.4278 3.7139 11.144 3.7139 4.5883 2.2941 4.5883 2.2941 4.5883 4.5883 2.2941 0.6745 1.4455 3.4692 0.3854 1.5148 1.0600 2.6019 After carrying out the weighted stages of normalization, it follows to determine the ideal positive and negative solutions with the results spelled out in the following Table 13: Table 13. Positive and Negative Ideal Solution Alternative C1 C2 C3 C4 C5 Positive 16.804 15.464 3.7139 4.5883 0.3854 Negative 6.4017 4.7143 11.141 2.2941 3.4692 Based on table 13, the ideal solution on the positive row is calculated by selecting the most significant value of the weighted normalization value on the weighted criteria while the lowest value on the cost attribute. The damaging row is calculated by selecting the smallest value from the weighted normalization value on the criteria that are attributed benefit. In contrast, in the cost attribute, the most significant value is selected on the weighted normalization value on the criteria. P-ISSN: 2656-1743 | E-ISSN: 2656-1735 DOI: https://doi.org/10.34288/jri.v5i1.493 JURNAL RISET INFORMATIKA Vol. 5, No. 1 December 2022 Accredited rank 3 (SINTA 3), excerpts from the decision of the Minister of RISTEK-BRIN No. 200/M/KPT/2020 136 Table 14 calculates the distance between the weighted values on each alternative to the positive and negative ideal solutions. Table 14. Alternative Weighted Value Distance To The Ideal Solution Alternative D+ D- A1 A2 A3 A4 A5 A6 A7 6.4588 7.6325 13.427 13.037 3.6831 11.413 6.0345 11.557 13.605 10.652 6.4105 14.080 10.387 13.584 Furthermore, it calculates the value of the preference for each alternative. Table 15. Preference value Alternative D+ A1 A2 A3 A4 A5 A6 A7 0.6414 0.6406 0.4423 0.3296 0.7926 0.4764 0.6924 Based on the results of the preference values from table 15 above, it can be seen that the alternative with the highest value is the A5 alternative. Result and Evaluation After carrying out a whole series of stages on the three methods using Simple Additive Weighting (SAW), Weighted Product (WP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), it can be concluded that the three methods provide the same decision regarding the alternatives that get the highest score. Here is a comparison table 16 of rankings using these three methods: Table 16. Ranking comparison Alternative SAW WP TOPSIS A1 A2 A3 A4 A5 A6 A7 2 3 6 7 1 5 4 3 4 6 7 1 5 2 3 4 6 7 1 5 2 Based on the comparison of rankings using the three methods above, it can be seen that the three methods give the highest value decision to the same alternative, A5, as the alternative with the highest value produced by the three methods. Using the WP and TOPSIS methods, the resulting ranking is the same. However, it is different from using the SAW method, where there are differences in the characteristics of alternatives A1, A2, and A7 to the WP and TOPSIS methods. CONCLUSIONS AND SUGGESTIONS Conclusion From the research conducted and the results obtained, the three methods carried out succeeded in providing the results of the same decision regarding the expectations of PT. Inipasti Communika and researchers that is to get one of the best alternatives based on the three methods, so the difficulty of PT. Inipasti Communika in assessing their journalists to be resolved using the methods carried out. Although the three methods do not succeed in providing 100% of the same ranking, using these three methods can be a reference and add convenience in deciding on PT. Inipasti Communika in measuring the performance of their journalists. Suggestion Based on this study, although the three methods do not provide the same ranking results, this study suggests continuing to use these three methods so that the results can be compared as in this study, then choosing the final decision by looking at the most significant number of methods with the results of the ranking the same as the final result of the decision. REFERENCES Aisyah, N., & Putra, A. S. (2021). Sistem Pendukung Keputusan Rekomendasi Pemilihan Manajer Terbaik Menggunakan Metode AHP (Analytic Hierarchy Process). Jurnal Esensi Infokom, 5 (2), 7-13. https://doi.org/10.55886/infokom.v5i2.275 Hafiz, A., & Ma’mur, M. (2018). Sistem Pendukung Keputusan Pemilihan Karyawan Terbaik dengan Pendekatan Weighted Product. Jurnal Cendikia, 15(1), 23–28. http://download.garuda.kemdikbud.go.id/art icle.php?article=936421&val=14485&title=si stem%20pendukung%20keputusan%20pem ilihan%20karyawan%20terbaik%20dengan %20pendekatan%20weighted%20product Hermanto, & Izzah, N. (2018). 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