Asia-Pacific Journal of Science and Technology

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1 1 Asia-Pacific Journal of Science and Technology: Volume: 22. Issue: 01. Article ID.: APST Research Article Asia-Pacific Journal of Science and Technology Published by the Research and Technology Transfer Affairs Division, Khon Kaen University, Thailand Use of the certainty factor method in diagnosing a disease of plantation crops with economic values Sri Winiarti 1, *, Efi Listiani Dewi 1 1 Unit on System Modeling for Industry, Department of Informatic Engineering, Faculty of Industrial Technology, Universitas Ahmad Dahlan, Yogyakarta, Indonesia *Corresponding author: sri.winiarti@tif.uad.ac.id Received September 2016 Accepted September 2016 Abstract This paper outlines an alternative solution to help Indonesian farmers in diagnosing diseases in plants. One solution is to create an expert system application that can diagnose diseases of plants of economic value by using certainty factor for search solutions. This research uses knowledge base for the diseases that attack crops plantation such as coffee, cocoa, pepper, nutmeg, and coconut. All these types of plants are the most frequent in Indonesian plantation and possess high economic value. This research aims to help improve the knowledge of the farmers in diagnosing diseases in plants with high economic value. From the tests performed to the farmer it is proven that this application can increase farmers knowledge after using the application of use of a certainty factor method in diagnosing disease of plantation crops with economic values. This research resulted in software that can diagnose diseases in plants by 36 diseases that applying the certainty factor method to get certainty in the search for solutions. Based on testing conducted to measure the level of understanding of the farmers in diagnosing diseases in plants, it is shown that traffic increased on average of 21.8 to 35.9 after using expert system applications which are built. Keywords: Certainty factor, Expert system, Plantation crops 1. Introduction Indonesia is a rich in its floras. The climate is very suitable for various types of crops to be grown. One of the prospective plants and has high economic value is a plantation crop. Plantation crops have an important role in economic development in Indonesia. Plantation crops which has economic value is potential of bringing in foreign exchange for the country, creating jobs, and becoming a source of incomes of the population, and to contribute to efforts to preserve the environment. Plantation cultivation is a business activity that its results are to be exported or used as industrial raw materials. More than 100 commodities can be developed in Indonesia, but the plantation species which have high economic value in improving the welfare of Indonesian society is clove, chocolate (cocoa), cotton, cinnamon, rubber, coconut, coconut oil, hazelnut, coffee, pepper, nutmeg, sugar cane, tea, tobacco, and vanilla[1], [2], [3] & [4]. Surveys conducted at Farmers Group Tunggak Semi, a farmer association of coffee and cacao ( chocolate ) in village Pentingsari Umbulharjo, Cangkringan, Sleman, Yogyakarta-Indonesia obtained by the fact that the average farmer's knowledge of the disease in perennial crops covers only the diseases that usually occur as rotten root, twig borers, root fungus and leaf rust. The farmers claimed that the difficulties to identify new diseases in perennial crops is due to limited information from counseling and books only. Moreover the availability of agricultural counseling for each region are also limited to the amount and availability of time. Based on the above problems, it needs to be an alternative solution for farmers in identifying diseases in plants owned plantations to reduce risk of crop failure or to increase crop production. One alternative solution offered is to create a consultation

2 2 media acted as an expert, who called expert systems. This study aims to create an expert system application for diagnosing diseases in plantation crops. 2. Materials and Methods This expert system application to diagnose diseases on plantation crops with high economic value is a web based application, developed using PHP programming language. The data about plantation diseases are retrieved from Department of Agriculture and Forestry in 2015 and 2016 Software development methodology to use is waterfall. The reason for using certainty factor in obtaining certainty in tracking solution, because knowledge related symptoms that can attack plant diseases can intersect between various diseases. By using the concept of CF combination method, it is possible to apply to the case of tracking plant diseases with similar symptoms. To measure the level of understanding of farmers related disease diagnosis in plants using parametric statistical concepts with test T (T Test).The steps are shown in Figure 1. Start Research study: 1. The previous study 2. Survey to government 3. Literature study 4. Documentation Analysis Phase: 1. Determination of the tracking models 2. Acquisition of knowledge 3. Identification of rules End Implementation Phase: 1. Coding with PHP programming 2. Testing using T-Test method 3. Tracking models using Certainty Factor Design Phase: 1. Development of knowledge base 2. create of rule base 3. create of decision tables 4. create of a tracking flow 5. development data base Figure 1 Research methodology 3. Results Solution searching process using forward chaining method is implemented during consultation process. In the consultation process, certainty value to use in two different manner: certainty value from user/farmer, and certainty value from plantation expert, which are stored in the rule base. The process of gaining certainty value is using Certainty Factor (CF). Application of expert systems as a product of this research, it has been tested to the farmers. System testing done in two ways; 1) the farmer is given questionnaires related to knowledge of the disease without the use of an expert system application, 2) farmers are given a questionnaire by using an expert system application. From the test results obtained there is an average difference in farmers' knowledge of 21, 80 into 35,90. Thus the ability of farmers in diagnosing diseases in plants rose by 14,1. This research produces software used as consultation media for farmers in diagnosing diseases on their plants. The developed application uses expert system concepts with forward chaining solution searching model supported by certainty factor value. To use this application, users need to log in. There are three different user accounts in this application, namely Plantation expert, Farmer, and Government (in this case Department of Forestry and Agriculture) these users have different access rights. Business process related to the usage of this application is shown in Figure 2.

3 3 User Flow of business process of expert systems applications diagnose disease in on crops Farmer Login Direct access data base Input Symptom Report Consultation with forward chaining Agricultural experts Login Input & update knowledge Input & update Rule base Display knowledge District agriculture and forestry Login Display knowledge Figure 2 Flow of business process of expert system applications diagnose disease in on crops 4. Discussion Several publications that discuss the certainty factor has been much publicized [6], [7] & [8]. Such as, the implementation of an expert in the lending system using the method of Certainty Factor [6]. This paper reports on the implementation of an expert system, which takes advantage of expertise in the area of granting loans to build a knowledge base. The system helps in making evaluations and expediting activities. Furthermore, using this system, accuracy is improved, allowing managers to trust expert decisions in granting or rejecting loan requests with more confidence. The proposed system uses a certainty factor to determine the characteristics of the case and ultimately help managers make better decisions. In another paper, the implementation of certainty factor used in expert systems in Comparison Model for Uncertain Information [7]. This paper reviews some of the uncertainty management in order to highlight, compare and clarify the differences of these approaches in terms of the application area and target area of problem solving. With this, we propose a comparison model which can serve as a guide in selecting a suitable uncertainty management with the consideration of three uncertain information categories: event, evidence and variable. These three categories are different in terms of their area of problem solving. Before developing the expert system application to diagnose diseases of the plantations, the system has been tested. The testing process is conducted to obtain validation from the user regarding the implementation of the process of tracking the diseases. For the implementation of certainty factor method, case testing has been performed. Illustration for the analysis of cases with certainty factor method as follows. Suppose users input symptoms experienced on the plants as shown in Table 1. Table 1 Sample of diseases and symptoms for the coffee plant using certainty factor Symptom Symptom code MB MD The leaves are splotches pale yellow to dark yellow G The leaves undergo loss G The formation of the fruit is too much G Description of: CF is the certainty factor in the hypothesis H due to evidence E MB is the measure of increased belief in H due to E MD is the measure of increased disbelief in H due to E Using the 1 st formula: CF[h,e₁]=MB[h,e₁] - MD[h,e₁] and the 2 nd formula for CF combination value : MB[h,e₁^e₂] = {MB[h,e₁] + MB h,e₂]*(1- MB[h,e₁])}, CF calculation illustration is as shown in Table 2.[9][10]. Based on Table 2, from the symptoms inputted by user, by using CF calculation, biggest certainty value 0.37 for Leaf Rust Disease (P001 code) is retrieved. Manual calculation and the application show the same result, hence it can be concluded that the application calculation is correct. After the application is tested using responde of farmers by 10 respondents, there are significantly

4 4 different in farmers' knowledge related to diseases in plants. This data is reinforced by tests performed to test the knowledge of farmers before siste expert application is built, and testing after the application is built. the result is the knowledge of farmers increased on average Before using the app farmers average ability - their knowledge average by 21.8, whereas after tested their ability to rise to Detailed test results can be seen in Table 3. Table 2 Illustration of searching using certainty factor method Code of Code of symptom disease Symptom P001 Preliminary calculations for the symptoms G001 [h,e₁] CF[h,e₁]=MB[h,e₁] - MD[h,e₁] G001, G002, G003 P001 P001 = = 0.4 New symptom G002 [h,e₂] Initial calculation for G001 [h,e₂] CF[h,e₂]=MB[h,e₁] - MD[h,e₁] = = 0.2 Calculation of the second symptom (and further) using MB and MD values. Result of calculation on Symptom G001 and G002: MB[h,e₁^e₂]={MB[h,e₁]+MBh,e₂]*(1-MB[h,e₁])} ={0.7 + ((0.6*(1-0.7))} = = 0.88 MD[h,e₁^e₂]={MD[h,e₁]+MDh,e₂]*(1-MD[h,e₁])} ={ ((0.3)*(1-0.3))} = = 0.51 CF[h,e₁^e₂] = MB[h,e₁^e₂] - MD[h,e₁^e₂] = = 0.37 third symptom is inputted G003 [h,e₃] CF[h,e₃]=MB[h,e₃] - MD[h,e₃] = = 0.4 Calculation of the third symptom G001^G002 dan G003 MB[h,(e₁^e₂)^e₃]={MB[h,e₁^e₂]+MB [h,e₃]*(1-mb [h,e₁^e₂])} = { ((0.7*(1-0.88))} = = MD[h,(e₁^e₂)^e₃]={MD[h,e₁^e₂]+MD [h,e₃]*(1-md[h,e₁^e₂])} ={ ((0.3)*(1-0.51))} = = CF[h,(e₁^e₂)^e₃] =MB[h,(e₁^e₂)^e₃]-MD[h,(e₁^e₂)^e₃] = = CF values

5 5 Table 3 The measurement level of understanding farmer in diagnosing diseases by application expert systems are designed Responden (farmers) Results of data collection knowledge of farmers before use application method utilization certainty factor in diagnose disease in economic value crops Results of data collection knowledge of farmers after using application method utilization certainty factor in diagnose disease in economic value crops Total Average 21,80 35,90

6 6 5. Conclusions Based on this study, it can be concluded that the certainty factor method can be implemented on the application of expert systems to diagnose the disease in plantation crops. In this study, it has been tested for five types of plants, namely; Chocolate (cocoa), nutmeg, coffee, pepper and coconut. All these plants have the highest economic value in Indonesia. From the tests performed to the farmer it is proven that this application can increase farmers' knowledge after using the application of use of certainty factor method in diagnosing disease of plantation crops with economic values. This can be seen in the results of the testing system and obtained the results of increasing the understanding of farmers with an average of 35.9, the data is up before their application in understanding of the level of the average Another conclusion of the research, the implementation of tracking methods with certainty factor can be applied to the application of expert system to diagnose diseases in crops of high economic value because the knowledge gained has symptoms that interrelates disease for 5 types of plants, namely; Chocolate (cocoa), nutmeg, coffee, pepper and coconut. 6. Acknowledgements This research has been supported by Lembaga Penelitian dan Pengembangan Universitas Ahmad Dahlan and Department of Informatic Engineering, Faculty of Industrial Technology, Universitas Ahmad Dahlan. 7. References [1] Suhartono D at al. Expert System in Detecting Coffe Plant Diseases.International Journal of Electrical Energy, Volume 1, Issue 3, September [2] Prasad,G.N.R & Babu.Vinaya A. A Study on Various Expert Systems in Agriculture. Georgian Electronic Scientific Journal: Computer Science and Telecommunications, Volume 4, Issue 11, [3] Aweyelu,I.O & Adebisi,R.O. A Predictive Fuzzy Expert System for Diagnosis of Cassava Plat Disease. Global Journal of Science Frontier Research: C Biological Science,Volume 15 Issue 10,2015. Online ISSN: & Print ISSN: [4] Agbonifo, O.C. & Olufolaji, D.B. A Fuzzy Expert System for Diagnosis and Treatment of Maize Plant Disease. International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 12, [5] Suwarto. Top 15 Tanaman perkebunan. Penebar Swadaya : Jakarta [6] Sarlati Y, Naghavi S, Jafari S. Implementation of Expert System for Lending With Certainty Factor. International Journal of Engineering Science Invention ISSN (Online): , ISSN (Print): Volume 3 Issue 4 April 2014, PP [7] Wei Liang, Yeow W & Mahmud R. A Comparison Model for Uncertain Information in Expert System. International Conference on Uncertainty Reasoning and Knowledge Engineering, [8] Munandar,Suherman & Sumiati. The use Certainty Factor with Multiple Rules for Diagnosing Internal Disease. International journal of Application in Engineering & Management (IJAEIM), ISSN ,Volume 1,Issue 1, September [9] Giarratano J & Reley G. Expert Systems: Principles and Programming, Second Edition. Boston: PWS Publishing Company; Chapter 5, Certainty Factor; P [10] Brachman R.J & Leverque Hector J. Knowledge Representation and Reasoning. Elsavier, Morgan Kaufmann Publishers Chapter 12, Vagueness, Uncertainty, and Degrees of Belief: P