Implementation of fuzzy multiple criteria decision making for recommendation paddy fertilizer

Chia sẻ: Nguyễn Thảo | Ngày: | Loại File: PDF | Số trang:8

Thêm vào BST

Báo xấu

16
lượt xem 2
download

Download Vui lòng tải xuống để xem tài liệu đầy đủ

This study aims to develop simulation tools to recommend N, P, and K fertilizer doses for lowland rice in Indonesia. Modeling criteria for decision making with the Fuzzy MCDM and TOPSIS methods used to determine the chosen alternative solutions.

Chủ đề:

Bình luận(0) Đăng nhập để gửi bình luận!

Lưu

Nội dung Text: Implementation of fuzzy multiple criteria decision making for recommendation paddy fertilizer

International Journal of Mechanical Engineering and Technology (IJMET) Volume 10, Issue 03, March 2019, pp. 236-243. Article ID: IJMET_10_03_024 Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=3 ISSN Print: 0976-6340 and ISSN Online: 0976-6359 © IAEME Publication Scopus Indexed IMPLEMENTATION OF FUZZY MULTIPLE CRITERIA DECISION MAKING FOR RECOMMENDATION PADDY FERTILIZER Lilik Sumaryanti, Lusia Lamalewa and Teddy Istanto Informatics Engineering Lecturer, Faculty of Engineering, Universitas Musamus, Merauke, Indonesia ABSTRACT Fertilizers have an important role in increasing rice production. Rice plants need nutrients in sufficient quantities, the use of fertilizers in accordance with soil nutrient needs will get good plant growth and adequate yield, but the dosage of its use must be appropriate to reduce the impact on the environment. Determination of Dosage The use of fertilizer N, P, K in lowland rice is based on local location according to soil nutrient status and several other criteria. This study aims to develop simulation tools to recommend N, P, and K fertilizer doses for lowland rice in Indonesia. Modeling criteria for decision making with the Fuzzy MCDM and TOPSIS methods used to determine the chosen alternative solutions. Data testing results based on specific five locations showed accuracy of fertilizer recommendations with expert comparisons, resulting in a minimum accuracy of 75%, a maximum accuracy of 99.5% and an average accuracy of 80%. The application of the Fuzzy TOPSIS method shows that the system can provide alternative solutions based on the criteria used as the basis for determining fertilizer dosages for location-specific lowland rice. Keywords: MCDM, Fuzzy, DSS, TOPSIS, Paddy. Cite this Article Lilik Sumaryanti, Lusia Lamalewa and Teddy Istanto, Implementation of Fuzzy Multiple Criteria Decision Making For Recommendation Paddy Fertilizer, International Journal of Mechanical Engineering and Technology, 10(3), 2019, pp. 236-243. http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=3 1. INTRODUCTION Paddy (Oryza sativa L.) is an important food crop that has become a staple food for more than half of the world's population. In Indonesia, rice is the main commodity in supporting people's food, because 95% of the population consumes rice. The rice harvest area in Indonesia in 2018 is 10.90 million hectares with the production of Dry Grain Paddy (GKG) production of 56.54 million tons of GK, so that rice production is equivalent to 32.42 million tons of rice [1]. Rice paddy is the largest fertilizer consumer in Indonesia, fertilizer use does not only play http://www.iaeme.com/IJMET/index.asp 236 editor@iaeme.com
Lilik Sumaryanti, Lusia Lamalewa and Teddy Istanto an important role in increasing rice crop production, but is also related to the sustainability of the production system, environmental sustainability, and saving energy resources. The need and efficiency of fertilization are determined by three interrelated factors, namely: (a). nutrient availability in the soil, including supply through irrigation water and other sources, (b) plant nutrient needs, and (c) target results to be achieved [2, 22, 23]. The means of production which are very vital in supporting efforts to increase national rice production are fertilizers, especially N, P and K, new superior varieties, and water. To get adequate growth and yield, rice plants need nutrients in sufficient quantities. Fertilizers can be used to meet nutrient requirements and the amount needs to be determined to be more efficient. Balanced fertilization is a basic requirement for success in increasing crop productivity, one of the efforts and by finding the right fertilizer dosage to determine the effect of the combination of N, P, and K fertilizer doses on plant growth and yield [3]. Treatment of cropping patterns with the addition of cow manure provides higher yields than without fertilizer [4]. Giving a combination of organic and inorganic fertilizers in hybrid rice shows effective for plant growth and increasing crop yields [5]. Organic waste that appears as residual harvest can be used to make innovative fertilizers from natural ingredients [6]. The relationship of the use of N fertilizer for various fertilization methods is significant to the net yield at a 5% probability level [7]. The use of N fertilizer shows the best application of fertilization in all caudate growth parameters Amaranthus [8]. N fertilizer does not have a detrimental effect on soil quality or food, but the dosage of its use must be appropriate to reduce the impact on the environment [9]. Phosphorus nanoparticles significantly increase photosynthetic activity and plant weight in response to salt stress [10]. Good soil fertility management can be carried out based on five factors that influence the success of fertilization so that the plants can grow optimally, namely the right type, the right dosage, the right time, the right place, and the right way. The tools for increasing the fertilizing efficiency of N, P, and K for rice paddy plants, among others are Leaf Color Chart (BWD) for N fertilization, Omisi plot and Paddy Soil Test Kit for fertilizing P and K. Decision support systems can be used as a tool to recommend the provision of appropriate dosage fertilizers based on local location. Decision support tools, can be an important part of evidence-based decision making efforts in agriculture [11]. Technology can be used to enrich agricultural potential with the help of computer-based decision support systems in agricultural management [12]. To support evaluation and selection processes in engineering, formal decision-making methods can be used by applying the Multiple Criteria Decision Making (MCDM) method [13]. Agricultural development requires technology and better tools to process data efficiently to translate data into better decisions and actions in the field [14]. Decision making is a recursive process and usually involves several decision criteria, Decision Support Systems (DSS) appear to help decision makers in the decision making process [16]. This study aims as a simulation tool in determining the dosage of N, P, and K fertilizers for lowland rice based on local location, modeling criteria for decision making using the Fuzzy MCDM method. MCDM decision making refers to finding the best alternative of all alternatives. The application of fuzzy logic is used to handle uncertainty and inaccuracy of evaluations where expert comparisons are represented as fuzzy numbers [17]. 2. METHODOLOGY Decision makers need tools that can be used to find the best solution or alternative in the decision making process. Based on this objective, a fuzzy Multiple Criteria Decision Making (MCDM) model and the TOPSIS method are applied to recommend fertilizer use doses on wet rice. Determination of weights is also done for each criterion, to introduce a measure of the relative importance of each criterion felt by the decision maker. Determination of criteria http://www.iaeme.com/IJMET/index.asp 237 editor@iaeme.com
Implementation of Fuzzy Multiple Criteria Decision Making For Recommendation Paddy Fertilizer weight by applying fuzzy method. The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method is one of the MCDM techniques used to rank various alternatives or solutions through numerical evaluation by decision makers. Positive ideal solutions are defined as the sum of all the best values that can be achieved for each criterion, while the ideal negative solution consists of all the worst values achieved for each attribute. TOPSIS considers both distance to positive ideal solutions and distance to negative ideal solutions by taking proximity relative to positive ideal solutions [18]. The basic principle of Fuzzy TOPSIS is that the chosen alternative must have the closest distance from the positive ideal solution and the farthest distance from the negative ideal solution in a geometric sense (eg Euclidean). The steps of this algorithm are explained as follows [17, 19, 20]. Step 1: Select the linguistic variable that is appropriate for the weight of importance of each linguistic criterion and variable for ranking. Step 2: Build a normalization of fuzzy decision matrix before forming a rij element, beginning with building xij. Then calculate ∑xij which functions to find the element rij, then calculate (∑xij) 2 as in Equations 1 and 2. ∑ (1) Finding the value of rij the result of normalizing the decision matrix R is calculated by the Euclidean method. (2) √∑ Where x = decision matrix; i = 1,2, ..., m; and j = 1,2, ..., n Step 3: Build a weighted normalized decision matrix. Ideal positive solutions (A +) and ideal negative solutions (A-) can be determined based on normalized weight rating (Yij) with Equation 3. with i = 1,2, ..., m; and j = 1,2, ..., n (3) Step 4: Determine fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS). The positive ideal solution matrix is determined by Equation 4 and the matrix of negative ideal solutions based on Equation 5. (4) (5) Step 5: Calculate the distance for each alternative from FPIS (A +) and FNIS (A-) The distance between the alternative Ai and FPIS is formulated with Equation 6. √∑ ; i = 1,2, …, m. (6) The distance between the alternative Ai and FNIS with equation 7. √∑ ; i = 1,2, …, m (7) Step 6: Determine the preference value for each alternative (Vi). (8) Step 7: Determine the rank order of all alternatives. http://www.iaeme.com/IJMET/index.asp 238 editor@iaeme.com
Lilik Sumaryanti, Lusia Lamalewa and Teddy Istanto Listing of Main Criteria Determination Linguistic Variable for weight of Each Criteria and The Drawing Sub-criteria Linguistic Ratings for Alternatives Construct Normalized Fuzzy Decision Matrix and the Analysis of Criteria List weighted normalized fuzzy decision matrix Determine FPIS and FNIS Calculate the Distances of Each Alternative Ranking Alternative Solution Using TOPSIS Figure 1. Research methods 3. RESULTS Calculation of fertilizer requirements is based on several criteria used to determine the right dose, including: Location (C1), Level of rice productivity (C2), Use of Leaf Color Chart (C3), Soil nutrient status P (C4), Nutrient status of K (C5), Use of Manure (C6), Use of Organic Straw Material (C7), Use of Compost Fertilizer (C8), Planting Season (C9) [2]. The recommendation for the use of N (urea) fertilizers is based on the level of productivity of paddy rice and the use of Leaf Color Chart, which serves to measure the greenness of leaf color which reflects leaf chlorophyll levels. As for the recommendations of P and K Fertilizers based on Nutrient Status of P and K, Paddy Land specific to each sub-district, and the use of organic matter, both in the form of compost from rice straw and manure. The choice of linguistic variables is the first step to represent the criteria in the fuzzy set. The level of rice productivity is one of the criteria that will be represented using linguistic variables, because it is a criterion that cannot be explained by conventional quantitative expressions. So that linguistic values can be represented by fuzzy numbers. The linguistic variables used in the study are shown in Tables 1 and 2. Table 1. Linguistic variables for the importance weight of each criteria Very Low (VL) (0; 0; 1) Low (L) (0; 0.1; 0.3) Medium (M) (0.3; 0.5; 0.7) High (H) (0.7; 0.9; 1) Very High (VH) (0.9; 1; 1) Table 2. Linguistic variables for the ratings Very Poor (VP) (0; 0; 1) Poor (P) (0; 0.1; 0.3) Fair (F) (0.3; 0.5; 0.7) Good (G) (0.7; 0.9; 1) Very Good (VG) (0.9; 1; 1) http://www.iaeme.com/IJMET/index.asp 239 editor@iaeme.com
Implementation of Fuzzy Multiple Criteria Decision Making For Recommendation Paddy Fertilizer The results obtained in building the normalization of fuzzy decision matrices are shown in Table 3. Alternatives or solutions in the form of recommendations for N (Urea), P fertilizer (SP-36) and K (KCl) fertilizer dosages with recommended dosages of kg / ha. The use of criteria to form the basis of fuzzy rules with linguistic variables and membership functions based on the opinions of experts. The determination of the value range parameters of each criterion is obtained from the decision maker, assuming a range of values to determine matrix fuzzy for each criterion. Table 3. The fuzzy decision matrix normalization Criteria Alternative C1 C2 C3 C4 C5 C6 C7 C8 C9 A1 0.0698 0.2789 0.0994 0.0703 0.2725 0.2094 0.2768 0.0645 0.3536 A2 0.1048 0.1195 0.0331 0.1757 0.0779 0.1257 0.1384 0.1936 0.1768 A3 0.1746 0.1992 0.1657 0.1054 0.1946 0.0419 0.1384 0.3227 0.1061 A4 0.1048 0.2789 0.2652 0.1054 0.1168 0.2932 0.1384 0.4518 0.3536 A5 0.3492 0.1992 0.0994 0.1757 0.1168 0.2094 0.0461 0.1936 0.1768 A6 0.2445 0.0797 0.0994 0.2460 0.1168 0.2094 0.3229 0.1936 0.3536 A7 0.1048 0.2789 0.3315 0.2460 0.1946 0.1257 0.1384 0.0645 0.1061 A8 0.0698 0.1992 0.1657 0.1757 0.2725 0.4188 0.2306 0.4518 0.3536 A9 0.1746 0.0398 0.1657 0.2460 0.1946 0.1257 0.1384 0.0645 0.3536 A10 0.2794 0.1992 0.0994 0.2460 0.1168 0.2932 0.0923 0.3227 0.1768 A11 0.3492 0.2789 0.1657 0.1757 0.2725 0.2932 0.3229 0.1936 0.1061 A12 0.2445 0.0398 0.9945 0.1757 0.1168 0.4188 0.4613 0.3227 0.3536 A13 0.2794 0.2789 0.2652 0.1757 0.1946 0.4188 0.1384 0.1936 0.3536 A14 0.1048 0.3984 0.0994 0.3514 0.2725 0.2932 0.1384 0.6455 0.1768 A15 0.3492 0.3984 0.1657 0.2460 0.3893 0.1257 0.0461 0.0645 0.3536 Weighted normalized decision matrix is obtained by multiplying the normalized fuzzy decision matrix, with the importance of each predetermined criterion. The results of the weighting matrix calculation are shown in Table 4. Table 4. Weighted normalized decision matrix Alternative Criteria C1 C2 C3 C4 C5 C6 C7 C8 C9 A1 0.0020 0.0418 0.0149 0.0141 0.0545 0.0209 0.0138 0.0026 0.0354 A2 0.0030 0.0179 0.0050 0.0351 0.0156 0.0126 0.0069 0.0077 0.0177 A3 0.0050 0.0299 0.0249 0.0211 0.0389 0.0042 0.0069 0.0129 0.0106 A4 0.0030 0.0418 0.0398 0.0211 0.0234 0.0293 0.0069 0.0181 0.0354 A5 0.0100 0.0299 0.0149 0.0351 0.0234 0.0209 0.0023 0.0077 0.0177 A6 0.0070 0.0120 0.0149 0.0492 0.0234 0.0209 0.0161 0.0077 0.0354 A7 0.0030 0.0418 0.0497 0.0492 0.0389 0.0126 0.0069 0.0026 0.0106 A8 0.0020 0.0299 0.0249 0.0351 0.0545 0.0419 0.0115 0.0181 0.0354 A9 0.0050 0.0060 0.0249 0.0492 0.0389 0.0126 0.0069 0.0026 0.0354 A10 0.0080 0.0299 0.0149 0.0492 0.0234 0.0293 0.0046 0.0129 0.0177 A11 0.0100 0.0418 0.0249 0.0351 0.0545 0.0293 0.0161 0.0077 0.0106 A12 0.0070 0.0060 0.1492 0.0351 0.0234 0.0419 0.0231 0.0129 0.0354 A13 0.0080 0.0418 0.0398 0.0351 0.0389 0.0419 0.0069 0.0077 0.0354 A14 0.0030 0.0598 0.0149 0.0703 0.0545 0.0293 0.0069 0.0258 0.0177 A15 0.0100 0.0598 0.0249 0.0492 0.0779 0.0126 0.0023 0.0026 0.0354 http://www.iaeme.com/IJMET/index.asp 240 editor@iaeme.com
Lilik Sumaryanti, Lusia Lamalewa and Teddy Istanto The results of calculation of fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS) solutions are shown in Table 5. Table 5. Result of FPIS and FNIS Alternative Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 + A 0.0100 0.0598 0.1492 0.0703 0.0779 0.0419 0.0231 0.0258 0.0354 - A 0.0020 0.0060 0.0050 0.0141 0.0156 0.0042 0.0023 0.0026 0.0106 After the calculation process is completed the best alternative will be displayed along with the results of TOPSIS preferences and ranking. The alternative is a recommendation for N, P and K fertilizer dosages that have a minimum distance from FPIS and have the highest preference value. The accuracy of testing data for recommendations for N, P and K fertilizers in wetland rice is shown in Figure 2, for the average test with an accuracy of more than 80%. ACCURACY O F DATA T EST I NG Minimum Accuracy Maximum Accuracy Mean Accuracy 120% 100% PERCENTAGE 80% 60% 40% 20% 0% LOCATION 1 LOCATION 2 LOCATION 3 LOCATION 4 LOCATION 5 Figure 2. Results of recommendation paddy fertilizer based on decision techniques 4. DISCUSSION Fuzzy decision making models can be used for group decision making, and various other fields. The use of fuzzy MCDM and fuzzy inference systems (FIS) to determine weights by criteria, which are calculated using the fuzzy AHP method. The results show that system output errors are compared with historical data of less than 5% [21]. The results of using two MCDM methods to determine the smart home alternative, Fuzzy AHP and Fuzzy TOPSIS, show alternative results of similar solutions [17]. TOPSIS is an MCDM tool that is often used to evaluate expert opinions, because it uses the right value to express expert opinion in alternative comparisons. So, to deal with uncertainties and inaccuracies inherent in the decision making process fuzzy set theory is successfully used. Development of recommended simulation tools for dosages of N, P and K fertilizers in lowland rice, by applying the Fuzzy TOPSIS method shows that the system can provide alternative solutions based on the criteria used as a basis for determining fertilizer doses. The use of technology can be used to enrich agricultural potential, with the help of evidence-based decision support systems in agriculture. This is based on the use of balanced fertilization by the concept of "Specific Location Management of Nutrients" which is the concept of establishing fertilizer recommendations. In this case, fertilizer is given to achieve a level of essential nutrient availability that is balanced in the soil and optimum in order to: (a) increase productivity and quality of crop products, (b) increase fertilizer efficiency, (c) increase soil fertility. http://www.iaeme.com/IJMET/index.asp 241 editor@iaeme.com
Implementation of Fuzzy Multiple Criteria Decision Making For Recommendation Paddy Fertilizer 5. CONCLUSION In this study, fuzzy set theory is integrated with TOPSIS to increase flexibility and determine the best alternative solution. In the evaluation process, the use of fuzzy sets brings many advantages to the decision to make a process such as the possibility to evaluate criteria that are not measurable and consider evaluating human judgment. The system test results show the accuracy of fertilizer recommendations with expert comparisons, resulting in a minimum accuracy of 75%, a maximum accuracy of 99.5% and an average accuracy of 80%. REFERENCES [1] Hermanto, Luas Panen dan Produksi Padi di Indonesia 2018, Jakarta : Badan Pusat Statistik. [2] Apriyantono Anton., Acuan Penetapan Rekomendasi Pemupukan N, P, dan K Pada Lahan Sawah Spesifik Lokasi, Jakarta , Mentri Pertanian. [3] Imam Firmansyah., Muhammad Syakir., dan Liferdi Lukman.,The Influence of Dosage Combination Fertilizer N, P, and K on Growth and Yield of Eggplant Crops (Solanum melongena L.) J. Hort. Vol. 27 No. 1, Juni 2017 : 69-78. [4] Sri Hariningsih Pratiwi, Growth and Yield of Rice (Oryza sativa L.) on Various Planting Methods and Addition of Organic Fertilizers, Gontor AGROTECH Science Journal , Vol. 2 No. 2, Juni 2016. 1-19. DOI: 10.21111/agrotech.v2i2.410. [5] Kyi Moe., Kumudra Win Mg., Kyaw Kyaw Win., Takeo Yamakawa., Combined Effect of Organic Manures and Inorganic Fertilizers on the Growth and Yield of Hybrid Rice (Palethwe-1). American Journal of Plant Sciences, 2017, 8, 1022-1042. DOI: 10.4236/ajps.2017.85068. [6] Marcela Calabi-Floody., Jorge Medina., Cornelia Rumpel., Leo M. Condron., Marcela Hernandez., Marc Dumont., Maria de la Luz Mora., Smart Fertilizers as a Strategy for Sustainable Agriculture. Advances in Agronomy, Volume 147, 120-143. 2018 Elsevier Inc. https://doi.org/10.1016/bs.agron.2017.10.003. [7] Sunita Singh Naik, Dr. Jaydev Rana, Dr. Prasanta Nanda, Using TOPSIS Method to Optimize the Process Parameters of D2 Steel on Electro-Discharge Machining, International Journal of Mechanical Engineering and Technology 9(13), 2018, pp. 1083– 1090 [8] Mohammad Reza Bakhtiar., Omid Ghahraei., Desa Ahmad., Ali Reza Yazdanpanah., Ali Mohammad Jafari., Selection of fertilization method and fertilizer application rate on corn yield, Agric Eng Int: CIGR Journal Vol. 16, No.2 June, 2014 10-14. [9] Olowoake Adebayo Abayomi., Ojo James Adebayo., Effect of Fertilizer Types on the Growth and Yield of Amaranthus caudatus in Ilorin, Southern Guinea, Savanna Zone of Nigeria, Advances in Agriculture Volume 2014 1-5. http://dx.doi.org/10.1155/2014/947062. [10] Jaap Jan Schröder, The Position of Mineral Nitrogen Fertilizer in Efficient Use of Nitrogen and Land: A Review, Natural Resources, 2014, 5, 936-948. http://dx.doi.org/10.4236/nr.2014.515080. [11] Zarrin Taj Alipour, The Effect of Phosphorus and Sulfur Nanofertilizers on the Growth and Nutrition of Ocimum basilicum in Response to Salt Stress, Journal of Chemical Health Risks (2016) 6(1), 125–131. [12] David C. Rose., William J. Sutherland., Caroline Parker., MattLobley et.all, Decision support tools for agriculture: Towards effective design and delivery, Agricultural Systems 149 (2016) 165–174. http://dx.doi.org/10.1016/j.agsy.2016.09.009. [13] Rok Rupnik., Matjaž Kukar., Petar Vracar., Domen Košir., Darko Pevec., Zoran Bosnic., A Decision Support System For Agricultu Re And Farming, Computers and Electronics in Agriculture (2018), https://doi.org/10.1016/j.compag.2018.04.001. http://www.iaeme.com/IJMET/index.asp 242 editor@iaeme.com
Lilik Sumaryanti, Lusia Lamalewa and Teddy Istanto [14] E.K. Zavadskas., J. Antucheviciene., Z. Turskis., H. Adeli., Hybrid Multiple-Criteria Decision-Making Methods: A Review Of Applications In Engineering, Scientia Iranica A (2016) 23(1), 1-20. [15] Li Tan., Cloud-based Decision Support and Automation for Precision Agriculture in Orchards, International Federation of Automatic Control (IFAC)-PapersOnLine 49-16 (2016) 330–335. DOI : 10.1016/j.ifacol.2016.10.061. [16] Marco Bartolozzi, Pierfrancesco Bellini, Paolo Nesi, Gianni Pantaleo ., Luca Santi ., A Smart Decision Support System for Smart City, IEEE International Conference on Smart City. DOI 10.1109/SmartCity.2015.57. [17] Chen-Shu Wang, Heng-Li Yang, Shiang-Lin Lin., To Make Good Decision: A Group DSS for Multiple Criteria Alternative Rank and Selection, Mathematical Problems in Engineering Volume 2015, Article ID 186970, 15-15. http://dx.doi.org/10.1155/2015/186970. [18] Ihsan Kaya., Cengiz Kahraman., A Comparison Of Fuzzy Multicriteria Decision Making Methods For Intelligent Building Assessme. Journal Of Civil Engineering And Management. 2014 Volume 20(1): 59–69. Doi:10.3846/13923730.2013.801906. [19] Anisseh, M., Piri, F., Shahraki, M. R., and Agamohamadi, F., Fuzzy Extensions of TOPSIS Model for Group Decision Making under Multi-ple Criteria, Artificial Intelligence Review, 38(4), 2012, pp. 326-338. [20] Aydin, S., Kahraman, C., Kaya, I. 2012. A New Fuzzy Multi Criteria Decision Making Approach: An Application For European Quality Award Assessment, Knowledg Based Systems 37–46. http://dx.doi.org/10.1016/j.knosys.2011.08.022. [21] Baysal, M. E.; Kahraman, C.; Sarucan, A.; Kaya, I.; Engin, O. 2013. A Two-Phased Fuzzy Methodology For Selection Among Municipal Projects, Technological and Economical Development of Economy (in Press). [22] H. Shakouri G., Y. Tavassoli N., Implementation Of A Hybrid Fuzzy System As A Decision Support Process: A FAHP–FMCDM–FIS composition, Expert Systems with Applications 39 (2012) 3682–3691. Elsevier doi:10.1016/j.eswa.2011.09.063. [23] Razif, M., Budiarti, V.E., Mangkoedihardjo, S. 2006. Appropriate fermentation process for tapioca's wastewater in Indonesia. Journal of Applied Sciences, 6(13), 2846-2848. [24] Mangkoedihardjo, S. and April, SAL. 2012. Compost On Evapotranspiration Bed Planted With Yellow Flag For Treatment Of Wastewater Containing Anionic Surfactant. Journal of Applied Sciences Research, 8(3): 1630-1633. [25] Suyadi, Pamuttu, D.L., Hairulla, Betaubun, P. Ant nest (Musamus) as an additional material of engineered soil stabilisation using soil cement. International Journal of Civil Engineering and Technology, 9(12), 2018, pp. 918–925. [26] Untari, Mekiuw, Y., Betaubun, P. Identification of channels and behavior of cassava marketing institution in Merauke district. International Journal of Civil Engineering and Technology, 9(12), 2018, pp. 261–266. [27] Nasra Pratama Putra, Gerzon Jokomen Maulany, Frans Xaverius Manggau and Philipus Betaubun, (2019). Attitude Quadrotor Control System with Optimization of PID Parameters Based On Fast Genetic Algorithm, International Journal of Mechanical Engineering and Technology, 10(1), pp. 335–343. [28] Stanly Hence Dolfi Loppies and Gerzon Jokomen Maulany, (2018). Geographic Information System Location of Pre-Prosperous Family Housing of Merauke District, International Journal of Mechanical Engineering and Technology, 9(12), pp. 177–183. [29] Lusia Lamalewa, Gerzon J. Maulany, (2018). Application of Case Based Reasoning and Nearest Neighbor Algorithm for Positioning Football Players, International Journal of Mechanical Engineering and Technology 9(13), pp. 258–265. [30] Teddy Istanto and Fransiskus Xaverius Manggau, (2018). Analysis of the Power of Wifi Signals on the Informatics Engineering Laboratory of Musamus University Using Insidder, International Journal of Mechanical Engineering and Technology 9(13), pp. 266–272. http://www.iaeme.com/IJMET/index.asp 243 editor@iaeme.com