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IMPLEMENTATION OF FUZZY MULTIPLE CRITERIA DECISION MAKING FOR RECOMMENDATION PADDY FERTILIZER

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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
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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
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Implementation of Fuzzy Multiple Criteria Decision Making For Recommendation Paddy
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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.
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Lilik Sumaryanti, Lusia Lamalewa and Teddy Istanto
Listing of Main Criteria
Determination Linguistic Variable
for weight of Each Criteria and The
Linguistic Ratings for Alternatives
Drawing Sub-criteria
Construct Normalized Fuzzy
Decision Matrix and the
weighted normalized fuzzy
decision matrix
Analysis of Criteria List
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)
Low (L)
Medium (M)
High (H)
Very High (VH)
(0; 0; 1)
(0; 0.1; 0.3)
(0.3; 0.5; 0.7)
(0.7; 0.9; 1)
(0.9; 1; 1)
Table 2. Linguistic variables for the ratings
Very Poor (VP)
Poor (P)
Fair (F)
Good (G)
Very Good (VG)
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(0; 0; 1)
(0; 0.1; 0.3)
(0.3; 0.5; 0.7)
(0.7; 0.9; 1)
(0.9; 1; 1)
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Implementation of Fuzzy Multiple Criteria Decision Making For Recommendation Paddy
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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
Alternative
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
A11
A12
A13
A14
A15
C1
0.0698
0.1048
0.1746
0.1048
0.3492
0.2445
0.1048
0.0698
0.1746
0.2794
0.3492
0.2445
0.2794
0.1048
0.3492
C2
0.2789
0.1195
0.1992
0.2789
0.1992
0.0797
0.2789
0.1992
0.0398
0.1992
0.2789
0.0398
0.2789
0.3984
0.3984
C3
0.0994
0.0331
0.1657
0.2652
0.0994
0.0994
0.3315
0.1657
0.1657
0.0994
0.1657
0.9945
0.2652
0.0994
0.1657
C4
0.0703
0.1757
0.1054
0.1054
0.1757
0.2460
0.2460
0.1757
0.2460
0.2460
0.1757
0.1757
0.1757
0.3514
0.2460
Criteria
C5
0.2725
0.0779
0.1946
0.1168
0.1168
0.1168
0.1946
0.2725
0.1946
0.1168
0.2725
0.1168
0.1946
0.2725
0.3893
C6
0.2094
0.1257
0.0419
0.2932
0.2094
0.2094
0.1257
0.4188
0.1257
0.2932
0.2932
0.4188
0.4188
0.2932
0.1257
C7
0.2768
0.1384
0.1384
0.1384
0.0461
0.3229
0.1384
0.2306
0.1384
0.0923
0.3229
0.4613
0.1384
0.1384
0.0461
C8
0.0645
0.1936
0.3227
0.4518
0.1936
0.1936
0.0645
0.4518
0.0645
0.3227
0.1936
0.3227
0.1936
0.6455
0.0645
C9
0.3536
0.1768
0.1061
0.3536
0.1768
0.3536
0.1061
0.3536
0.3536
0.1768
0.1061
0.3536
0.3536
0.1768
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
A1
A2
A3
A4
A5
A6
A7
A8
A9
A10
C1
0.0020
0.0030
0.0050
0.0030
0.0100
0.0070
0.0030
0.0020
0.0050
0.0080
C2
0.0418
0.0179
0.0299
0.0418
0.0299
0.0120
0.0418
0.0299
0.0060
0.0299
C3
0.0149
0.0050
0.0249
0.0398
0.0149
0.0149
0.0497
0.0249
0.0249
0.0149
C4
0.0141
0.0351
0.0211
0.0211
0.0351
0.0492
0.0492
0.0351
0.0492
0.0492
Criteria
C5
0.0545
0.0156
0.0389
0.0234
0.0234
0.0234
0.0389
0.0545
0.0389
0.0234
C6
0.0209
0.0126
0.0042
0.0293
0.0209
0.0209
0.0126
0.0419
0.0126
0.0293
C7
0.0138
0.0069
0.0069
0.0069
0.0023
0.0161
0.0069
0.0115
0.0069
0.0046
C8
0.0026
0.0077
0.0129
0.0181
0.0077
0.0077
0.0026
0.0181
0.0026
0.0129
C9
0.0354
0.0177
0.0106
0.0354
0.0177
0.0354
0.0106
0.0354
0.0354
0.0177
A11
0.0100
0.0418
0.0249
0.0351
0.0545
0.0293
0.0161
0.0077
0.0106
A12
A13
A14
A15
0.0070
0.0080
0.0030
0.0100
0.0060
0.0418
0.0598
0.0598
0.1492
0.0398
0.0149
0.0249
0.0351
0.0351
0.0703
0.0492
0.0234
0.0389
0.0545
0.0779
0.0419
0.0419
0.0293
0.0126
0.0231
0.0069
0.0069
0.0023
0.0129
0.0077
0.0258
0.0026
0.0354
0.0354
0.0177
0.0354
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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
+
0.0100 0.0598 0.1492 0.0703 0.0779 0.0419 0.0231 0.0258
A
0.0020 0.0060 0.0050 0.0141 0.0156 0.0042 0.0023 0.0026
A
Y9
0.0354
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
PERCENTAGE
Minimum Accuracy
Maximum Accuracy
Mean Accuracy
120%
100%
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.
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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]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
Hermanto, Luas Panen dan Produksi Padi di Indonesia 2018, Jakarta : Badan Pusat
Statistik.
Apriyantono Anton., Acuan Penetapan Rekomendasi Pemupukan N, P, dan K Pada Lahan
Sawah Spesifik Lokasi, Jakarta , Mentri Pertanian.
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.
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.
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.
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.
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
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.
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]
[11]
[12]
[13]
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.
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.
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.
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
[email protected]
Lilik Sumaryanti, Lusia Lamalewa and Teddy Istanto
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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