Optimal Dengue Endemic Region Prediction using Fuzzy Simple Additive Weighting based Algorithm

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Volume 118 No. 7 2018, 473-478 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu ijpam.

1 Volume 118 No , ISSN: (printed version); ISSN: (on-line version) url: ijpam.eu Optimal Dengue Endemic Region Prediction using Fuzzy Simple Additive Weighting based Algorithm Fauzi, Nungsiyati, Tri Noviarti, Muhamad Muslihudin, Rita Irviani, Andino Maseleno STMIK Pringsewu, Lampung, Indonesia fauzistmikpsw@gmail.com, nungsiyati@gmail.com, muslihudinstmikpsw@gmail.com, andimaseleno@gmail.com Abstract: Increased the patients of dengue fever in the Tanggamus District due to the spread of endemic Aedesaegypti and Aedesalbopictus are causing deaths. In early responding need to know the distribution of endemic region to anticipated of wider dissemination. Determine the endemic areas of dengue fever by using Simple Additive weighting method will help to know area detected the spread of dengue in the Tanggamus district. By using ten criteria in this study case and test into twenty alternative which is sub district in Tanggamus obtained results that have the highest value alternative criteria Sub district Talang Padang value 1.00 and the sub district Ulubelu1,00 while the lowest value to the sub district Sumber Rejo value Keywords: Endemic, Tanggamus, Fuzzy SAW, Dengue Fever I. INTRODUCTION Dengue fever caused by dengue virus is spread occurs through the bite of Aedesaegypti and Aedesalbopictus. Because the insect is mediated by both, the dengue fever cannot be transmitted from a person to another person directly should the flu. Aedesaegypti and Aedesalbopictus have many breed in densely populated areas and mountains, for example in large cities humid and warm climates. Data from the World Health Organization (WHO) in 2009 recorded Indonesia as the country with the highest dengue cases in Southeast Asia. Where as many as 58 people were infected and 24 fatalities (Mortality (AK): 41.3%). And since then, the disease was spread throughout Indonesia [1]. Ministry of Health noted the number of patients with dengue fever in Indonesia in January and February 2016 as many as 8487 DBD cases with 108 deaths. Most classes are experiencing dengue fever in Indonesia at the age of 5-14 years reached 43.44% and the age of years reached 33.25% [2]. Bandar Lampung city is endemic dengue. Data from Bandar Lampung city health department said in 2010, the number of dengue fever patients in Bandar Lampung reached 763 people and 16 people died. In 2011, the number of dengue fever patients in Bandar Lampung reached 413 people and 7 people died. In 2012, an increasing number of dengue fever patients in Bandar Lampung reach 1111 people and 11 people who died, that amount is the highest compared with other districts [3]. Research conducted [4] resulted in an assessment that climate has a very strong influence on the spread of dengue fever endemic in the region of Bandar Lampung. Development of research to find out endemic dengue fever has also been conducted using information technology that uses expert system as is done with [5] the symptoms of dengue fever test using an application that applied to the 30 samples were obtained the results of the application used 70% accurate. The use of artificial neural networks has also been done by [4] with the results of the study by testing 17 data get 88.23% accurate results and inaccuracies only 11.77% with the conclusion that use of information technology systems is appropriate. Tanggamus is one district in Lampung province has an area of km 2 and a population of as many as inhabitants with a population density of 178 inhabitants / km² possible prone to the spread of Dengue. Based on a review of previous studies, this research will be to design a decision support models to determine the area Endemic dengue demem accordance with the criteria specified, in order to facilitate in determining the policy correctly, quickly, effectively and efficiently. The method used is the Simple Additive weighting method. This method was chosen because it is able to choose the best alternative from a number of alternatives, in this case the alternative question is determining areas demem Endemic dengue based on the criteria specified. To determine the weight of the priority selection relies heavily on the provision of value, the value of the criteria and subcriteria, then the stage of assessment criteria and sub-criteria will generate a priority to determine dengue endemic area. An issue to be resolved is how to build a decision support system to determine the endemic areas of dengue fever in the district Tanggamus. The results of this study were to identify areas Endemic dengue fever, so that the government can easily 473

2 determine the area and early prevention in endemic areas. Produce a decision-making model dengue endemic area is more valid and accurate as well as determine the ranking of the region made in alternative testing. II. RESEARCH METHODS A. Simple Additive WeightMethod Simple Additive Weight commonly known term weighted summation method. The basic concept SAW method is to find the sum of the weighted performance rating for each alternative on all attributes[7][8][9]. SAW method requires a process of normalizing the decision matrix (X) to a scale that can be compared with all the ratings of existing alternatives. Step SAW settlement are as follows: a. Step completion Simple Additive Weighting (SAW): a. Specify the criteria that will be used as a reference in the decision, namely Ci b. Determining suitability rating each alternative on each attribute c. Make decisions based on criteria matrix (Ci), then normalized matrix based on the equations adjusted for the type attribute (attribute or attributes benefit costs) in order to obtain the normalized matrix. d. The final result is obtained from the process of ranking the matrix multiplication is the sum of the normalized R with the weight vector in order to obtain the greatest value is selected as the best alternative (Ai) as a solution. Information: rij = value normalized performance rating Xij = value attribute possessed of every Criteria: Max Xij = largest value of each criterion Min Xij = smallest value of each criterion Benefit = if the largest value is the best value Cost = if the smallest value is the best value Where rij is the normalized rating of alternative Ai on attribute Cj; i = 1,2..., m and j = 1,2..., n. Value preferences will for each alternative (Vi) is given as: (2) Information: Vi = ranking for each alternative Wj = weight value of each criterion rij = value normalized performance rating Vi larger value indicates that the preferred alternative Ai [11] [12]. (1) B. The concept of Flow Research Framework In this research stage using a predetermined flows so that the discussion of the study does not deviate from the initial concept. Chronology of the research can be seen in the following stages: Research problem Collection Criteria DeterminationAlterna tive Di Uji Matrix Fuzzy SAW Value Grafik Conclusion and Decision Support Figure 1 Flow Concept Research III. DISCUSSION A. Determine criteria and Alternative The processes of determining dengue endemic regions require precision time because the data of criteria determined by each sub district will be calculated one by one from the entire sub district. These districts will be selected based on the criteria set by the government. The criteria set in this case study are population density, humidity, a source of clean water, and health facilities. Therefore, not all of these endemic regions, only areas that have received the title of these criteria are endemic. In this study there is weight and the necessary criteria for determining dengue-endemic regions is as follows: Table 1. Weight Criteria Criteria Code Weight Population density C1 10% Humidity C2 10% Water sources C3 10% Health facility C4 10% SPAL C5 10% Sanitation C6 10% Trash can C7 10% Toilet C8 10% PHBS C9 10% Dietary habit C10 10% 474

Determining candidates (alternatives) to determine an alternative candidate, taken 20 sub districts as samples. Here are the names of the District that became alternative testing: Table 2.

Kelumbayan K 12 Kelumbayan Barat L 13 Pugung M 14 Kota Agung Barat N 15 Kota AgungTimur O 16 Limau P 17 PematangSawa Q 18 PulauPanggung R 19 Semaka S 20 Sumberejo T B.

3 Determining candidates (alternatives) to determine an alternative candidate, taken 20 sub districts as samples. Here are the names of the District that became alternative testing: Table 2. Vilage Alternative No Vilage Code 1 Talang Padang A 2 Gisting B 3 Ulubelu C 4 Kota AgungPusat D 5 Wonosobo E 6 Air Naningan F 7 Bandar NegeriSemong G 8 Bulok H 9 CukuhBalak I 10 GunungAlip J 11 Kelumbayan K 12 Kelumbayan Barat L 13 Pugung M 14 Kota Agung Barat N 15 Kota AgungTimur O 16 Limau P 17 PematangSawa Q 18 PulauPanggung R 19 Semaka S 20 Sumberejo T B. Normalization Criteria Normalization is done by dividing the value of each alternative candidate divided the greatest value from the value of each alternative. as the best alternative as a solution. The value of the preference weights are as follows: C1 = 10 C6 = 10 C2 = 10 C7 = 10 C3 = 10 C8 = 10 C4 = 10 C9 = 10 C5 = 10 C10 = 10 W=(0.10, 0.10, 0.10, 0.10, 0.10, 0.10, 0.10, 0.10, 0.10, 0.10,) Then it will do the sums every alternative, by way of multiplying the value of each criterion with a predetermined weight value. With results as follows: Table 3 Total Value No Name of candidate Total Value 1 Talang Padang Gisting Ulubelu Kota AgungPusat Wonosobo Air Naningan Bandar NegeriSemong Bulok CukuhBalak GunungAlip Kelumbayan Kelumbayan Barat Pugung Kota Agung Barat Kota AgungTimur Limau PematangSawa PulauPanggung Semaka Sumberejo C. Results Weighting The final result is obtained from the process of ranking the multiplication is the sum of the matrix normalized R with the weight vector in order to obtain the greatest value is selected 475

4 Figure 2 Total Value graph IV. CONCLUSION From the calculation above can be concluded that the alternative has the highest criterion value A = 1.00 and C = 1.00, and the lowest value is T = Of the 20 samples that have been assessed, there are 5-prone areas endemic dengues. Thus, Decision Support System Use of criteria in determining Dengue Endemic areas in Tanggamus can help make it easier to endemic areas so that these areas receive special attention from the Department of Health and Local Government Tanggamus. REFERENCES [1] Kementerian Kesehatan RI Buletin Jendel Epidemelogi. Vol. 10. ISSN : Jakarta. [2] (di Akses. 10 Oktober 2017). [3] Sukohar A, DemamBerdarah Dengue (DBD). FakultasKedokteran. Universitas Lampung. [4] Apriliana, Pengaruh Iklim terhadap Kejadian Demam Berdarah Dengue di Kota Bandar Lampung, Provinsi Lampung. INAMSC Page Universitas Indonesia. [5] Huda, M., Maseleno, A., Shahrill, M., Jasmi, K. A., Mustari, I., and Basiron, B. (2017). 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5 [32] Muslihudin., Muhamad and MiswanGumanti A System To Support Decision Makings In Selection Of Aid Receivers For Classroom Rehabilitation For Senior/Vocational High Schools By Education Office Of Pringsewu District By Using Simple Additive Weighing Method. 4th International Scholar Conference. 25 Oct Page 94. Klabat University. Makassar. 477

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