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Simple additive weighting as decision support system for determining employees salary Article · January 2018

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International Journal of Engineering & Technology, 7 (2.14) (2018) 309-313

International Journal of Engineering & Technology Website: www.sciencepubco.com/index.php/IJET Research Paper

Simple Additive Weighting as Decision Support System for Determining Employees Salary Nashrudin Setiawan1, M D T P Nasution1, Yossie Rossanty1, Anna Riana Suryanti Tambunan2, Martina Girsang3, R T A Agus4, Muhammad Yusuf5, Rian Vebrianto6, Oktaviana Nirmala Purba7, Achmad Fauzi8, Surya Perdana9, Khairun Nisa10 1

Universitas Pembangunan Panca Budi, Medan, Indonesia Department Language Education and English Literature, Universitas Negeri Medan, Medan, Indonesia 3 Universitas Methodist Indonesia, Medan, Indonesia 4 Department of Information System, Sekolah Tinggi Manajemen Informatika dan Komputer Royal, Kisaran, Indonesia 5 Sekolah Tinggi Keguruan dan Ilmu Pendidikan Pelita Bangsa, Binjai, Indonesia 6 Faculty of Tarbiyah and Teaching, UIN Sulthan Syarif Kasim Riau, Riau, Indonesia 7 Department of Mathematics Education, Universitas Asahan, Kisaran, Indonesia 8 Department of Informatics Management, Sekolah Tinggi Manajemen Informatika dan Komputer Kaputama, Binjai, Indonesia 9 Department of Law, Universitas Muhammadiyah Sumatera Utara, Medan, Indonesia 10 Indonesian Language and Literature Education, Universitas Asahan, Kisaran, Indonesia 2

Abstract Employees are seen as one of the important company assets and need to be managed and developed to support the survival and achievement of corporate goals. One form of employee organization that can be done by the company is to provide the appropriate remuneration or salary payments for employees. Increase in salary greatly affects the motivation and productivity of employees in implementing and completing the work. To determine the magnitude of the salary increase, a system is needed that can support the decision making done by the manager. Utilization of decision support system using Simple Additive Weighting (SAW) method helps managers to make quicker and more accurate decision making. This method is chosen because it is able to select the best alternative from a number of alternatives that exist based on the criteria specified. The research is done by finding the weight value for each attribute then done ranking which will determine the optimal alternative. Keywords: Decision Support System, Employees, Simple Additive Weighting

1. Introduction Employees are seen as one of the important company assets and need to be managed and developed to support the survival and achievement of corporate goals [1]–[3]. If employees can be well organized, then the company can run all the business processes well too. Therefore, to facilitate the process, a computerized system is required so that in the implementation, from calculation to payment to employees can run faster and the results obtained will be more accurate. The development of technology and information continuously progressed rapidly[4]–[7] can be utilized to facilitate all activities within the company. Decision Support Systems[8]–[14] are part of computer-based information systems that belong to knowledgebased or knowledge management systems that can be used to support decision-making [15]–[22] within an organization or company[23]. This system can assist decision makers who complement them with information from data that has been processed with relevance and is needed to make decisions about a problem more quickly and accurately[24]–[26]. The rate of salary increase is determined on the basis of performance that must have some problems so that there is a need for a method to solve it. One method that can be used is the method of

Simple Additive Weighting (SAW)[27]–[30]. The basic concept of the SAW method is to find the weighted sum of performance ratings on each alternative and on all attributes that require the process of normalizing the decision matrix (X) to a scale comparable to all existing alternative ratings. This method is chosen because it is able to select the best alternative from a number of alternatives that exist based on the criteria specified. The research is done by finding the weight value for each attribute then done ranking which will determine the optimal alternative.

2. Methodology Decision is an activity or activity or action of choosing one alternative from some alternatives taken as solution of a problem[1], [31], [32]. The type of decision taken to solve a problem can be seen from the type of structure, such as: a. Structured Decision is a decision that is done repeatedly and is routine. The decision-making procedure is very clear. b. Semi structured Decision it is a decision that has two traits. Some decisions can be handled by the computer and others still have to be done by decision makers. c. Unstructured Decision, it is a handling decision that is complicated because it does not happen over and over or is not

Copyright © 2018 Nashrudin Setiawan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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International Journal of Engineering & Technology

always the case. The decision requires experience and external sources. Decision-making includes four interconnected and consecutive stages, including: a. Intelligence This stage is the process of tracking and detecting the scope of problematic and problem recognition process. The input data is obtained, processed, and tested in order to identify the problem. b. Design This stage is the process of discovering, developing, and analyzing alternative actions that can be done. This stage includes a process for understanding the problem, lowering the solution and testing the feasibility of the solution. c. Choice At this stage a selection process is made between various possible action alternatives[33]–[36]. This stage includes the search, evaluation and recommendation of appropriate solutions for the model that has been made. The solution of the model is the specific value for the result variable on the selected alternative. d. Implementation Implementation stage is the implementation stage of the decision that has been taken. At this stage it is necessary to arrange a set of planned actions so that the results of the decisions can be monitored and adjusted as necessary.

Fig. 1: Stages of Decision Maker

Simple Additive Weighting (SAW) method is often also known as the weighted summing method. The basic concept of the SAW method is to find the weighted sum of performance ratings on each alternative on all attributes. The SAW method requires the process of normalizing the decision matrix (X) to a scale comparable to all existing alternative ratings[37], [38].

{

}

Information: rij = normalized performance rating value xij = attribute value owned by each criterion Max xij = the largest value of each criterion Min xij = the smallest value of each criterion Benefit = if the greatest value is best Cost = if the smallest value is best Where rij as the normalized performance rating of alternative Ai on attribute Cj; i = 1,2, ..., m and j = 1,2, ..., n. Preference value for each alternative (Vi), the function as equation below:

∑

Information: Vi = ranking for each alternative Wj = weighted value of each criterion rij = normalized performance rating value A larger value of Vi indicates that Ai's alternatives are preferred

3. Results and Discussion The implementation of Simple Additive Weighting (SAW) method in decision support system requires criteria and weighting of value as well as some alternatives to be calculated in the ranking process and assessment in decision making to determine the level of salary increase, as an experiment there are 62 samples of data that will get a raise. The following is data from 62 samples of data used.

ID A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16 A17 A18 A19 A20 A21 A22 A23 A24 A25 A26 A27 A28 A29 A30 A31 A32 A33 A34 A35 A36 A37 A38 A39 A40 A41 A42 A43 A44 A45 A46 A47 A48 A49 A50 A51 A52

Table.1: Sample Data Alternative Alternative_1 Alternative_2 Alternative_3 Alternative_4 Alternative_5 Alternative_6 Alternative_7 Alternative_8 Alternative_9 Alternative_10 Alternative_11 Alternative_12 Alternative_13 Alternative_14 Alternative_15 Alternative_16 Alternative_17 Alternative_18 Alternative_19 Alternative_20 Alternative_21 Alternative_22 Alternative_23 Alternative_24 Alternative_25 Alternative_26 Alternative_27 Alternative_28 Alternative_29 Alternative_30 Alternative_31 Alternative_32 Alternative_33 Alternative_34 Alternative_35 Alternative_36 Alternative_37 Alternative_38 Alternative_39 Alternative_40 Alternative_41 Alternative_42 Alternative_43 Alternative_44 Alternative_45 Alternative_46 Alternative_47 Alternative_48 Alternative_49 Alternative_50 Alternative_51 Alternative_52

International Journal of Engineering & Technology A53 A54 A55 A56 A57 A58 A59 A60 A61 A62

311

Alternative_53 Alternative_54 Alternative_55 Alternative_56 Alternative_57 Alternative_58 Alternative_59 Alternative_60 Alternative_61 Alternative_62

A30 A31 A32 A33 A34 A35 A36 A37 A38 A39 A40 A41 A42 A43 A44 A45 A46 A47 A48 A49 A50 A51 A52 A53 A54 A55 A56 A57 A58 A59 A60 A61 A62

As for the steps that must be done in the calculation of the salary increase rate in accordance with the above case using Simple Additive Weighting (SAW) method, that is: a. Specifies performance-based criteria and fuzzy numbers on SAW for the weight of values to be used for assessment. These criteria can be seen in table 2, 3 and 4 below. Table.2: Criteria and Weighting Value Criteria Weight Achievement 35% Discipline 25% Attitude 25% Years of service 15% Table.3: Weight Fuzzy Numbers Fuzzy Number Very Bad Bad Enough Good Very Good Table.4: Years of service Time (Month) 3 – 11 12 – 23 24 – 35 36 – 47 48 – 59

b.

Value 1 2 3 4 5

c. Value 1 2 3 4 5

Determine the match rating of each alternative on each of the criteria that can be seen in table 5 below.

Table.5: Rating of Matching Alternative with Criteria Rating result Alternative Years of Achievement Discipline Attitude service A1 4 4 4 4 A2 4 3 4 4 A3 4 4 4 4 A4 3 3 3 4 A5 4 4 4 3 A6 4 2 4 2 A7 3 4 4 2 A8 3 3 3 4 A9 4 2 3 5 A10 4 4 3 5 A11 4 3 3 5 A12 3 4 4 4 A13 4 4 4 5 A14 4 4 4 4 A15 3 4 4 3 A16 4 4 4 3 A17 4 4 4 3 A18 3 3 3 3 A19 3 2 3 2 A20 4 3 3 3 A21 4 3 4 4 A22 4 4 4 4 A23 3 3 3 4 A24 3 3 4 2 A25 3 4 3 4 A26 3 4 4 4 A27 3 3 3 3 A28 4 4 3 3 A29 4 3 3 3

4 4 3 3 3 3 3 4 3 3 3 3 3 3 4 4 4 4 3 4 3 3 4 3 3 3 3 4 4 4 3 3 3

4 3 3 4 3 3 3 4 4 3 4 4 3 4 4 4 5 4 3 3 3 3 5 3 4 4 3 4 4 4 4 3 3

4 4 3 3 4 3 4 4 3 4 4 3 3 4 4 4 4 4 3 4 3 3 4 3 3 4 3 4 4 4 4 3 3

3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1

Calculate the normalization value of each alternative by the formula:

Implementation of formula:

The result of normalization is made in the form of a normalization matrix as below.

312

International Journal of Engineering & Technology = (0.35 x 0.75) + (0.25 x 0.8) + (0.25 x 0.75) + (0.15 x 0.8) = 0.77 = (0.35 x 0.75) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.8) = 0.8325 = (0.35 x 0.75) + (0.25 x 0.6) + (0.25 x 0.75) + (0.15 x 0.6) = 0.69 = (0.35 x 1) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.6) = 0.8275 = (0.35 x 1) + (0.25 x 0.6) + (0.25 x 0.75) + (0.15 x 0.6) = 0.7775 = (0.35 x 1) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.6) = 0.89 = (0.35 x 1) + (0.25 x 0.6) + (0.25 x 0.1) + (0.15 x 0.6) = 0.84 = (0.35 x 0.75) + (0.25 x 0.6) + (0.25 x 0.75) + (0.15 x 0.6) = 0.69 = (0.35 x 0.75) + (0.25 x 0.8) + (0.25 x 0.75) + (0.15 x 0.4) = 0.71 = (0.35 x 0.75) + (0.25 x 0.6) + (0.25 x 1) + (0.15 x 0.4) = 0.7225 = (0.35 x 0.75) + (0.25 x 0.6) + (0.25 x 0.75) + (0.15 x 0.4) = 0.66 = (0.35 x 0.75) + (0.25 x 0.6) + (0.25 x 1) + (0.15 x 0.4) = 0.7225 = (0.35 x 1) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.4) = 0.86 = (0.35 x 0.75) + (0.25 x 0.8) + (0.25 x 0.75) + (0.15 x 0.4) = 0.71 = (0.35 x 0.75) + (0.25 x 0.6) + (0.25 x 1) + (0.15 x 0.4) = 0.7225 = (0.35 x 0.75) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.4) = 0.7725 = (0.35 x 0.75) + (0.25 x 0.8) + (0.25 x 0.75) + (0.15 x 0.4) = 0.71 = (0.35 x 0.75) + (0.25 x 0.6) + (0.25 x 0.75) + (0.15 x 0.4) = 0.66 [

d.

]

[

]

Determine the preferences of each alternative by using the formula:

= (0.35 x 0.75) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.4) = 0.7725 = (0.35 x 1) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.4) = 0.86 = (0.35 x 1) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.4) = 0.86 = (0.35 x 1) + (0.25 x 1) + (0.25 x 1) + (0.15 x 0.4) = 0.91 = (0.35 x 1) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.4) = 0.86

∑

= (0.35 x 0.75) + (0.25 x 0.6) + (0.25 x 0.75) + (0.15 x 0.4) = 0.66 = (0.35 x 1) + (0.25 x 0.6) + (0.25 x 1) + (0.15 x 0.4) = 0.81 [

]

The preference value for determining the ranking result is as follows: = (0.35 x 1) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.8) = 0.92 = (0.35 x 1) + (0.25 x 0.6) + (0.25 x 1) + (0.15 x 0.8) = 0.87 = (0.35 x 1) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.8) = 0.92 = (0.35 x 0.75) + (0.25 x 0.6) + (0.25 x 0.75) + (0.15 x 0.8) = 0.72 = (0.35 x 1) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.6) = 0.89 = (0.35 x 1) + (0.25 x 0.4) + (0.25 x 1) + (0.15 x 0.4) = 0.76 = (0.35 x 0.75) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.4) = 0.7725 = (0.35 x 0.75) + (0.25 x 0.6) + (0.25 x 0.75) + (0.15 x 0.8) = 0.72 = (0.35 x 1) + (0.25 x 0.4) + (0.25 x 0.75) + (0.15 x 1) = 0.7875 = (0.35 x 1) + (0.25 x 0.8) + (0.25 x 0.75) + (0.15 x 1) = 0.8875 = (0.35 x 1) + (0.25 x 0.6) + (0.25 x 0.75) + (0.15 x 1) = 0.8375 = (0.35 x 0.75) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.8) = 0.8325 = (0.35 x 1) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 1) = 0.95 = (0.35 x 1) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.8) = 0.92 = (0.35 x 0.75) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.6) = 0.8025 = (0.35 x 1) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.6) = 0.89 = (0.35 x 1) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.6) = 0.89

= (0.35 x 0.75) + (0.25 x 0.6) + (0.25 x 0.75) + (0.15 x 0.4) = 0.66 = (0.35 x 0.75) + (0.25 x 0.6) + (0.25 x 0.75) + (0.15 x 0.4) = 0.66 = (0.35 x 1) + (0.25 x 1) + (0.25 x 1) + (0.15 x 0.4) = 0.91 = (0.35 x 0.75) + (0.25 x 0.6) + (0.25 x 0.75) + (0.15 x 0.4) = 0.66 (15%) = (0.35 x 0.75) + (0.25 x 0.8) + (0.25 x 0.75) + (0.15 x 0.2) = 0.68 (10%) = (0.35 x 0.75) + (0.25 x 0.8) + (0.25 x 0.75) + (0.15 x 0.2) = 0.68 (15%) = (0.35 x 0.75) + (0.25 x 0.6) + (0.25 x 0.75) + (0.15 x 0.2) = 0.63 (5%) = (0.35 x 1) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.2) = 0.83 (10%) = (0.35 x 1) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.2) = 0.83 (5%) = (0.35 x 1) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.2) = 0.83 (5%) = (0.35 x 0.75) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.2) = 0.7425 (5%) = (0.35 x 0.75) + (0.25 x 0.6) + (0.25 x 0.75) + (0.15 x 0.2) = 0.63 (5%) = (0.35 x 0.75) + (0.25 x 0.6)(10%) + (0.25 x 0.75) + (0.15 x 0.2) = 0.63

4. Conclusion

= (0.35 x 1) + (0.25 x 0.6) + (0.25 x 0.75) + (0.15 x 0.6) = 0.7775 = (0.35 x 1) + (0.25 x 0.6) + (0.25 x 1) + (0.15 x 0.8) = 0.87 = (0.35 x 1) + (0.25 x 0.8) + (0.25 x 1) + (0.15 x 0.8) = 0.92 = (0.35 x 0.75) + (0.25 x 0.6) + (0.25 x 0.75) + (0.15 x 0.8) = 0.72 = (0.35 x 0.75) + (0.25 x 0.6) + (0.25 x 1) + (0.15 x 0.6) = 0.7525

(10%)

(15%) The application of Simple Additive Weighting (SAW) method in decision making of salary (15%) raising level is done by finding weighted sum of criteria at each alternative and at attribute which (10%) need normalization decision matrix, then doing the process of (10%) to determine alternative which ranking up to value of preference get increase salary between 5% - 15% or not at all get a raise. (10%)

= (0.35 x 0.75) + (0.25 x 0.6) + (0.25 x 0.75) + (0.15 x 0.6) = 0.69 = (0.35 x 0.75) + (0.25 x 0.4) + (0.25 x 0.75) + (0.15 x 0.4) = 0.61

(10%)

(0%)

References

(0%)

(5%) [1] R. Nasriyah, Z. Arham, and Q. Aini, “Profile matching and competency based human(10%) resources management approaches for employee placement decision support system (case study),” Asian J. (15%) Appl. Sci., vol. 9, no. 2, pp. 75–86, 2016. [2] C. Truss, A. Shantz, E. (5%) Soane, K. Alfes, and R. Delbridge, “Employee engagement, organisational performance and individual (5%) well-being: Exploring the evidence, developing the theory,”

International Journal of Engineering & Technology International Journal of Human Resource Management, vol. 24, no. 14, pp. 2657–2669, 2013. [3] J. Jasri, D. Siregar, and R. Rahim, “Decision Support System Best Employee Assessments with Technique for Order of Preference by Similarity to Ideal Solution,” Int. J. Recent Trends Eng. Res., vol. 3, no. 3, pp. 6–17, Mar. 2017. [4] R. Rahim, H. Nurdiyanto, A. S. Ahmar, D. Abdullah, D. Hartama, and D. Napitupulu, “Keylogger Application to Monitoring Users Activity with Exact String Matching Algorithm,” J. Phys. Conf. Ser., vol. 954, no. 1, 2018. [5] A. Putera, U. Siahaan, and R. Rahim, “Dynamic Key Matrix of Hill Cipher Using Genetic Algorithm,” Int. J. Secur. Its Appl., vol. 10, no. 8, pp. 173–180, Aug. 2016. [6] R. Rahim, M. Dahria, M. Syahril, and B. Anwar, “Combination of the Blowfish and Lempel-Ziv-Welch algorithms for text compression,” World Trans. Eng. Technol. Educ., vol. 15, no. 3, pp. 292–297, 2017. [7] R. Rahim, “Man-in-the-middle-attack prevention using interlock protocol method,” ARPN J. Eng. Appl. Sci., vol. 12, no. 22, pp. 6483–6487, 2017. [8] D. Siregar, D. Arisandi, A. Usman, D. Irwan, and R. Rahim, “Research of Simple Multi-Attribute Rating Technique for Decision Support,” J. Phys. Conf. Ser., vol. 930, no. 1, p. 012015, Dec. 2017. [9] A. Alesyanti, R. Ramlan, H. Hartono, and R. Rahim, “Ethical decision support system based on hermeneutic view focus on social justice,” Int. J. Eng. Technol., vol. 7, no. 2.9, pp. 74–77, 2018. [10] Y. Rossanty, D. Hasibuan, J. Napitupulu, M. Dharma, and T. Putra, “Composite performance index as decision support method for multi case problem,” Int. J. Eng. Technol., vol. 7, no. 2.9, pp. 33–36, 2018. [11] S. H. Sahir, R. Rosmawati, and R. Rahim, “Fuzzy model tahani as a decision support system for selection computer tablet,” Int. J. Eng. Technol., vol. 7, no. 2.9, pp. 61–65, 2018. [12] T. Simanihuruk et al., “Hesitant Fuzzy Linguistic Term Sets with Fuzzy Grid Partition in Determining the Best Lecturer,” Int. J. Eng. Technol., vol. 7, no. 2.3, pp. 59–62, 2018. [13] M. D. T. P. Nasution et al., “Decision support rating system with Analytical Hierarchy Process method,” Int. J. Eng. Technol., vol. 7, 2018. [14] C. H. Primasari, R. Wardoyo, and A. K. Sari, “Integrated AHP, Profile Matching, and TOPSIS for selecting type of goats based on environmental and financial criteria,” Int. J. Adv. Intell. Informatics, vol. 4, no. 1, pp. 28–39, Mar. 2018. [15] N. Kurniasih, A. S. Ahmar, D. R. Hidayat, H. Agustin, and E. Rizal, “Forecasting Infant Mortality Rate for China: A Comparison Between α-Sutte Indicator, ARIMA, and Holt-Winters,” J. Phys. Conf. Ser., vol. 1028, no. 1, p. 012195, 2018. [16] A. S. Ahmar, “A Comparison of α-Sutte Indicator and ARIMA Methods in Renewable Energy Forecasting in Indonesia,” Int. J. Eng. Technol., vol. 7, no. 1.6, pp. 20–22, 2018. [17] A. Rahman and A. S. Ahmar, “Forecasting of primary energy consumption data in the United States: A comparison between ARIMA and Holter-Winters models,” in AIP Conference Proceedings, 2017, vol. 1885. [18] A. S. Ahmar et al., “Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO),” J. Phys. Conf. Ser., vol. 954, 2018. [19] D. U. Sutiksno, A. S. Ahmar, N. Kurniasih, E. Susanto, and A. Leiwakabessy, “Forecasting Historical Data of Bitcoin using ARIMA and α-Sutte Indicator,” J. Phys. Conf. Ser., vol. 1028, no. 1, p. 012194, 2018. [20] A. S. Ahmar, “A comparison of α-Sutte Indicator and ARIMA methods in renewable energy forecasting in Indonesia,” Int. J. Eng. Technol., vol. 7, 2018. [21] Surahman, A. Viddy, A. F. O. Gaffar, Haviluddin, and A. S. Ahmar, “Selection of the best supply chain strategy using fuzzy based decision model,” Int. J. Eng. Technol., vol. 7, no. 22, pp. 117–121, 2018. [22] Haviluddin, F. Agus, M. Azhari, and A. S. Ahmar, “Artificial Neural Network Optimized Approach for Improving Spatial Cluster Quality of Land Value Zone,” Int. J. Eng. Technol., vol. 7, no. 2.2, pp. 80–83, 2018. [23] A. Indahingwati, M. Barid, N. Wajdi, D. E. Susilo, N. Kurniasih, and R. Rahim, “Comparison Analysis of TOPSIS and Fuzzy Logic Methods On Fertilizer Selection,” Int. J. Eng. Technol., vol. 7, no. 2.3, pp. 109–114, 2018. [24] U. Khair, H. Fahmi, S. Al Hakim, and R. Rahim, “Forecasting Error Calculation with Mean Absolute Deviation and Mean

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[33]

[34]

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[37]

[38]

Absolute Percentage Error,” J. Phys. Conf. Ser., vol. 930, no. 1, p. 012002, Dec. 2017. E. Azimirad and J. Haddadnia, “Target threat assessment using fuzzy sets theory,” Int. J. Adv. Intell. Informatics, vol. 1, no. 2, pp. 57–74, Aug. 2015. H. Hamdani and R. Wardoyo, “A review on fuzzy multi-criteria decision making land clearing for oil palm plantation,” Int. J. Adv. Intell. Informatics, vol. 1, no. 2, pp. 75–83, Jul. 2015. I. Kaliszewski and D. Podkopaev, “Simple additive weighting - A metamodel for multiple criteria decision analysis methods,” Expert Syst. Appl., vol. 54, pp. 155–161, 2016. A. Pranolo and S. M. Widyastuti, “Simple Additive Weighting Method on Intelligent Agent for Urban Forest Health Monitoring,” in 2014 INTERNATIONAL CONFERENCE ON COMPUTER, CONTROL, INFORMATICS AND ITS APPLICATIONS (IC3INA), 2014, pp. 132–135. N. Nurmalini and R. Rahim, “Study Approach of Simple Additive Weighting For Decision Support System,” Int. J. Sci. Res. Sci. Technol., vol. 3, no. 3, pp. 541–544, 2017. I. Tahyudin, R. Rosyidi, A. S. Ahmar, and Haviluddin, “Comparison of the Simple Additive Weighting (SAW) with the Technique for Others Reference by Similarity to Ideal Solution (TOPSIS) methods,” Int. J. Eng. Technol., vol. 7, no. 2.2, pp. 87–89, 2018. A. Aljuaidi, “Decision support system analysis with the graph model on non-cooperative generic water resource conflicts,” Int. J. Eng. Technol., vol. 6, no. 4, p. 145, Oct. 2017. J. Shang, P. R. Tadikamalla, L. J. Kirsch, and L. Brown, “A decision support system for managing inventory at GlaxoSmithKline,” Decis. Support Syst., vol. 46, no. 1, pp. 1–13, 2008. R. Rahim, I. Zulkarnain, and H. Jaya, “A review: search visualization with Knuth Morris Pratt algorithm,” in IOP Conference Series: Materials Science and Engineering, 2017, vol. 237, no. 1, p. 012026. R. Rahim, A. S. Ahmar, A. P. Ardyanti, and D. Nofriansyah, “Visual Approach of Searching Process using Boyer-Moore Algorithm,” J. Phys. Conf. Ser., vol. 930, no. 1, p. 012001, Dec. 2017. R. Ratnadewi, E. M. Sartika, R. Rahim, B. Anwar, M. Syahril, and H. Winata, “Crossing Rivers Problem Solution with Breadth-First Search Approach,” in IOP Conference Series: Materials Science and Engineering, 2018, vol. 288, no. 1. R. Rahim et al., “Block Architecture Problem with Depth First Search Solution and Its Application,” J. Phys. Conf. Ser., vol. 954, no. 1, p. 012006, 2018. F. Haswan, “Decision Support System For Election Of Members Unit Patients Pamong Praja,” Int. J. Artif. Intell. Res., vol. 1, no. 1, p. 21, Jun. 2017. S. H. Zanakis, A. Solomon, N. Wishart, and S. Dublish, “Multiattribute decision making: A simulation comparison of select methods,” Eur. J. Oper. Res., vol. 107, no. 3, pp. 507–529, 1998.