36 Journal of Multidisciplinary Applied Natural Science Vol. 1 No. 1 (2021) Research Article Received : August 22, 2020 Revised : October 2, 2020 Accepted : November 27, 2020 Online : November 29, 2020 The purpose of this article is to optimization of national rice production with fuzzy logic using Mamdani method. Based on the results of the study, it is known that four parameters need to be considered to maintain the price stability of necessities, namely production; availability; demand and distribution. Optimization of production by producers and optimization of the ordering of goods by distributors are important steps to maintain price stability for necessities. Optimization of production and ordering of sta- ple goods will have a significant impact on the financial sector because it is closely related to the prediction of the number of raw materials used, production costs, storage costs, and also distribution costs of goods. One of the fuzzy inference methods that can be used for this optimization is the Mamdani method. To get the output on the application of the fuzzy logic of the Mamdani method, four stages are needed, formation of fuzzy sets; application of implication functions; composition of rules and defuzzification. Fuzzy logic Mamdani method can be used to predict the amount of national rice that must be produced. If it is known that the need is 21,908,784 tons of rice and the supply is 65,457,456 tons, the amount of national rice that must be produced is 14,624,592 tons. Seen on a global scale, food security remains a serious problem, especially in developing countries where challenges of sustainable food supply are exacerbated by rapid population increases, limited access to food intake, vulnerability, price volatility, protective measures imposed by governments, and so on [1]. Several studies have shown that food in- security is influenced by various factors, including population growth, availability of fertile land and water resources, and climate change [2]. Indonesia is one of the developing countries that have fairly good food security. As an indicator, The people in Indonesia are guaranteed a sustainable supply of food and other necessities. Even so, there is often instability in the prices of necessities, especially before the religious holidays. As a consequence, people's purchasing power decreases and the fulfill- ment of primary needs is limited, even though their availability is abundant This is certainly material for self-evaluation for policymakers in finding solu- tions so that prices of staple goods are always stable so that it has an impact on the welfare of the com- munity [3]. It must be admitted that rice and other staple goods, such as meat, cooking oil, salt, and sugar, are the most important commodities in Indonesia because of their role as a staple food, which the ma- jority of every Indonesian population consumes every day as nutritional intake. Not only that, vari- ous staple goods are also a dominant strategic com- modity in the Indonesian economy because they are closely related to monetary policy and involve socio -political issues. To meet these needs, many things affect the stability of availability and price, starting from climatic conditions, logistics systems, and do- mestic market conditions as well as global market conditions. When examined further, price movements of staple goods, especially rice, are strongly influenced by three factors [4]. The first factor is the factor of the availability of the goods themselves, which comes from the crops of the farmers for rice and corn; the production of meat, eggs, and milk from the farmers and the production from the company for other necessities such as sugar and cooking oil. The second factor is the demand factor from con- sumers. Consumer demand is often influenced by several factors, for example an increase in demand ahead of National Religious Holidays; panic or con- sumer concerns about the scarcity of various com- modities in the market and changes in consumption Copyright Holder: © Wawan, W., Zuniati, M., and Setiawan, A. (2021) First Publication Right: Journal of Multidisciplinary Applied Natural Science This Article is Licensed Under: https://doi.org/10.47352/jmans.v1i1.3 OPEN ACCESS https://creativecommons.org/licenses/by-sa/4.0/deed.id https://doi.org/10.47352/jmans.v1i1.3 https://crossmark.crossref.org/dialog/?doi=10.47352/jmans.v1i1.3&domain=pdf&date_stamp=2021-01-10 J. Multidiscip. Appl. Nat. Sci. 37 patterns, preferences, and diversification of staple foods consumer. The third factor is the distribution factor that can trigger an increase and decrease in the price of necessities. The distribution process incurs several costs, such as the number of distribu- tion costs, the distance from the production center to the consumption center, and the disruption in the distribution process. Indonesia has no longer been able to be rice self -sufficient since 1984. The deficit forced the gov- ernment to import rice from the Philippines and Thailand as a result of great demand. It is difficult to know so many variables that cause rice self- sufficiency. It could be induced, for example, by weak agricultural infrastructure in Indonesia, gov- ernment policies, a lack of land to open up new fields, as well as a lack of labor. Given that Indone- sia is a vast tropical area and also has ample human capital, this is rather ironic. The government is still trying to find a solution so that it is possible to solve the rice self-sufficiency problem and increase the welfare of the citizens of Indonesia. Therefore any policy determined whether it will work in the rice self-sufficiency program must be re-evaluated by the government. The performance of govern- ment policy would have an effect on the increase in the amount of rice production. One way to quantify uncertainty in forecasting using the Fuzzy Logic of the Mamdani method is to calculate the amount of rice production that is uncertain [4]. Taking into account these few things, there are at least four things that need to be considered in main- taining the price stability of staple goods, namely: production; availability; demand and distribution. Furthermore, in this article, we will see how fuzzy logic contributes to controlling the price of staple goods. In this article, we will also provide an exam- ple of how to optimize production using the Mamdani method of fuzzy logic application. This procedure is an important part of providing a refer- ence for producers and distributors of necessities in managing the supply chain to minimize the costs of production and distribution of goods. As a final im- plication, the stability of prices for necessities on the market is maintained. Based on this explanation, the purpose of writing articles are as follows providing knowledge related to the use of the Fuzzy Mamdani method in the optimization of production and ordering of national rice. Fuzzy Logic Fuzzy can be interpreted as fuzzy or vague. Fuzzy logic is a set of mathematical principles for the representation of knowledge based on the de- gree of membership Fuzzy logic is a form of many logical values; relates to the use of reasoning for approximations. Compared to traditional or clas- sical binary sets (where variables can take true or false values), fuzzy logic variables have truth values that range in levels between 0 and 1. Fuzzy logic itself has been extended to deal with the concept of partial truth, where the truth of value can range be- tween completely true and completely false Linguistic Variables If a variable uses words in the colloquial lan- guage as its value, then the variable is called a lin- guistic variable, where words are characterized by a vague set defined in a defined universe of variables Linguistic variables are defined as variables whose value is predictive statements, economic planning, and management, in natural or artificial language J. Multidiscip. Appl. Nat. Sci. 38 ∪ 3. RESULTS AND DISCUSSION It has been mentioned earlier that controlling the prices of basic needs involves four things, namely: (1) the production process, (2) the availability of goods, (3) the amount of market demand or com- J. Multidiscip. Appl. Nat. Sci. 39 munity needs, and (4) the cost of distributing goods. Producers and distributors contribute significantly to the price of necessities in the market. For several important commodities such as rice, the amount of agricultural production, availability in Bulog, and community needs greatly affect the price of rice. For producers providing other staple goods such as cooking oil, sugar, milk, and other staples, the opti- mization of production will have major implications for the selling price in the market. Optimization of the production of staple goods will affect the finan- cial sector because it is closely related to estimates of raw materials, production costs, storage costs, and also distribution costs of goods. As part of con- trolling the prices of necessities, the production op- timization process is important. On the other hand, distributors also play a crucial role in maintaining the price stability of necessities. Distributors must be careful in ordering products from producers. Of course, ordering products must also be adjusted to the availability of goods and market demand. If the distributor is not careful in this matter, it can have implications for the distributor's loss. In this regard, fuzzy logic can be used in opti- mizing various sectors to control prices for necessi- ties in the market. For producers, fuzzy logic can contribute to the optimization of production, while distributors can be used in optimizing orders from producers. The fuzzy approach can be effectively utilized to deal with imprecision and uncertainty [11]. This approach can be used for performance evaluation that allows organizations, producers, or distributors to carry out professional assessments in evaluating the supply chain of staple goods. In real problems in the field, performance evaluation tech- niques with Fuzzy logic can be used to handle cases such as subjectivity, obscurity, and imprecise infor- mation. The application of fuzzy set theory in eval- uation systems can improve evaluation results [12]. When viewed historically, the initial idea of applying fuzzy sets to control problems is presented more explicitly by Chang and Zadeh [13]. In subse- quent developments, research has been carried out on real problems initiated by Mamdani and Assilian [14] and Mamdani [15]. Until now, there have been many studies using Fuzzy logic in the case of con- trolling the prices of necessities, for example, re- search [16] has provided an application for the problem of cryptic production inventory planning using the optimality equation. Many studies have included distortion factors in preparing the optimi- zation of production quantities. For example [17] developed and investigated the Economic Order Quantity (EOQ) model in the fuzzy sense by con- sidering stochastic demand, partial backlogging, and the degree of fuzzy damage. The proposed model is also extended to the case where the partial backlogging factor is assumed to be a fuzzy num- ber. The total minimum cost and optimal order quantity are obtained by defuzzifying the total cost through the signed distance method. Nagoor Gani and Maheswari [18] discussed the retailer's ordering policy of two levels of late payment taking into ac- count the selling price per item being higher than the purchase cost per item. The demand and selling price per item is taken as triangular fuzzy numbers. The function principle is used to calculate the opti- mal cycle times and the economical order quantity. The method of representation of the tiered average integration is used for defuzzification in this case. Another study, Lee and Yaou [19] developed the Economic Production Quantity (EPQ) model in which demand and production quantity are assumed to be vague. Then Lo et al. [20] presented the EPQ model which includes uncertain factors such as un- reliable machines, defective products, or imperfect goods. They have used the EPQ model to explore the most economical choice of production cost fac- tors based on the uncertainties that may exist. Fur- thermore, Chen and Chang [21] developed an EPQ model with irreparably defective production yields. Chang [22] discusses how to get the economic order quantity, when the quantity demanded is uncertain. While Lee and Yao [19] and Lin and Yao [23] have also written several articles on cryptic production models, they have not yet developed an inventory model with imperfect products. An inventory model with imperfect products was carried out by Chen, et al. [24] who proposed a Fuzzy Economic Produc- tion Quantity (FEPQ) model with imperfect prod- ucts that can be sold at a discount. In this model, costs and quantities are expressed as trapezoidal fuzzy numbers. These various studies have provided important references regarding the contribution of fuzzy logic in controlling the prices of necessities. Optimization of production and ordering of goods by considering various things such as machine reliability, imper- J. Multidiscip. Appl. Nat. Sci. 40 fections in production, availability of imperfect or defective products, and uncertain market demand is urgent considerations in planning the supply of sta- ple goods. Fuzzy logic has made a valuable contri- bution in controlling the prices of necessities. An important part of fuzzy logic is represented by Fuzzy Logic Control (FLC), derived from con- trol theory based on the mathematical model of an open-loop process to control. FLC has been suc- cessfully applied to a variety of practical problems such as control of warm water, robots, heat ex- change, traffic at intersections, car speed, automo- tive engineering, washing machines, and so on [25]. In connection with the optimization of production and ordering of staple goods, several methods can be used in the application of fuzzy logic in this field, for example, the Mamdani method, the Tsuka- moto method, and the Sugeno method. As presented Iancu [25], the most commonly used fuzzy inference technique is the Mamdani method proposed by Mamdani and Assilian [14], as a first attempt to control a steam engine and boiler combination by synthesizing a set of linguistic rule controls obtained from experienced human opera- tors. The work was inspired by the equally influen- tial publication of [8]. Interest in fuzzy control has continued since then, and the literature on the sub- ject has grown rapidly to date. In the Mamdani method, the fuzzy implications are modeled by the Mamdani minimum operator, the conjunction operator is min, the t-norm of the min composition rule, and for rule aggregation, the max operator is used. According to Iancu [25] there are four steps in using the Mamdani method, name- ly: (1) Fuzzification, (2) rule evaluation, (3) aggre- gation of the rule output, and (4) Defuzzification. The first step is to take a sharp input, x_0dany_0, and determine the extent of where these inputs be- long to each of the corresponding fuzzy sets. In the second step, if the given fuzzy rule has multiple antecedents, the fuzzy operator (AND or OR) is used to get a single number that represents the ante- cedent evaluation result. To evaluate the separation of the rule's antecedents, one uses the fuzzy OR operation. Next in step three, the membership func- tions of all consequent rules previously truncated or scaled are combined into a single fuzzy set. As for step four, the most popular defuzzification method is the centroid technique. More easily, [10] said that to get the output from the Mamdani method, four stages are needed, formation of fuzzy sets; applica- tion of implication functions; composition of rules and 4) defuzzification. Furthermore, a simple example will be given related to the use of the Mamdani method to predict the amount of production of one of the basic needs, namely rice, if it is known that the community's rice needs and availability in Bulog. The data used for this example is data from BPS relating to the availa- bility of rice commodities in Indonesia in the period 2006 to 2017 [4]. Based on data from BPS, infor- mation is obtained that the lowest amount of pro- duction in that year was 54,454,947 tons (2006), while the highest production amount occurred in 2017 with a total of 79,175,945 tons [26]. The low- est rice availability occurred in 2006 with 54,506. 436 tons, while the highest rice availability oc- curred in 2017 with a total of 79,682,632 tons. On the other hand, based on BPS data, it is also known that the smallest national demand for rice occurred in 2007 with a total of 20,587,547 tons, while the largest amount occurred in 2017 with the total de- mand for rice reaching 22,847,706 tons. In this case, there are three variables, namely: 2 input variables, including the need variable and the supply variable, while for output there is 1 variable, namely: rice production. The need variable has 2 linguistic values, namely up and down, the invento- ry variable has 2 linguistic values, namely a lot and a little, while the production variable has 2 linguis- tic values, namely increasing and decreasing. Rice production uses the following four fuzzy rules. • [R1] IF demand DOWN and supply A LOT, THEN production is LESS • [R2] IF demand is DOWN and inventory is LITTLE, THEN production is LESS • [R3] IF demand increases and supply is MANY, THEN PRODUCTION INCREASE • [R4] IF demand increases and supply is LIT- TLE, THEN PRODUCTION INCREASE The solution for this example using the Mamdani method of Fuzzy logic is as follows: J. Multidiscip. Appl. Nat. Sci. 41 1. Fuzzy Set Formation The formation of fuzzy sets is the first step taken when using the Mamdani Method. There are three fuzzy variables to be modeled, namely: · Requirement: consists of two fuzzy sets, namely: DOWN and UP. · Inventory: consists of two fuzzy sets, namely: LITTLE and MANY. · Production: consists of two fuzzy sets, namely: REDUCE and ADD. 2. Application Function Implications For example, it is known that the total national rice demand in 2018 is 21,908,784 tons and the total supply is 65,457,456 tons of rice. The application used in this example is the MIN rule. · [R1] IF the need DOWN and supply a LOT, then production is LESS. · [R2] IF demand is DOWN and inventory is LITTLE, then production is LESS. Obtained · [R3] IF supply increases and supplies ARE MANY, THEN INCREASED production · [R4] IF needs UP and supply is LITTLE, THEN PRODUCTION INCREASE. Obtained 3. Composition of Rules The method used to compose all rules is the MAX method. The membership function for the results of this composition is as follows. 4. Defuzzifikasi The defuzzification method used is the Centroid method with a continuous domain, using the following formula. With this formula, the following results are obtained. So for the example given gives results that the amount of national rice that must be produced if it is known that the need is 21,908,784 tons of rice and the supply is 65,457,456 tons is 14,624,592 tons of rice. 4. CONCLUSIONS Optimization of rice production by producers and optimization of the ordering of goods by distribu- tors are important steps to maintain price stability for necessities in the market. Optimization of pro- duction and ordering of staple goods will have a significant impact on the financial sector because it is closely related to the prediction of the number of raw materials used, production costs, storage costs, and also distribution costs of goods. One of the fuzzy inference methods that can be used for this purpose is the Mamdani method. To get the output on the application of the fuzzy logic of the Mamda- ni method, four stages are needed, formation of fuzzy sets; application of implication functions (rules); composition of rules and defuzzification. Fuzzy logic Mamdani method can be used to pre- dict the amount of national rice that must be pro- duced. If it is known that the need is 21,908,784 J. Multidiscip. Appl. Nat. Sci. 42 tons of rice and the supply is 65,457,456 tons, the amount of national rice that must be produced is 14,624,592 tons. AUTHOR INFORMATION Corresponding Author Wawan Wawan — Department of Mathematics Education, Institute for Islamic Studies Ma’arif Nahdlatul Ulama (IAIMNU), Metro-34111 (Indonesia); https://orcid.org/0000-0002-5573-623X Email: awanwawan0215@gmail.com Authors Mai Zuniati — Department of English Educa- tion, Institute for Islamic Studies Ma’arif Nahdlatul Ulama (IAIMNU), Metro-34111 (Indonesia); https://orcid.org/0000-0002-1231-1426 Agus Setiawan — Department of Mathematics Education, Institute for Islamic Studies Ma’arif Nahdlatul Ulama (IAIMNU), Metro-34111 (Indonesia); https://orcid.org/0000-0002-9712-5461 ACKNOWLEDGEMENT This work was supported by Lembaga Pengem- bangan Penelitian dan Pengabdian Kepada Masyarakat (LP3M) Institute for Islamic Studies Ma’arif NU (IAIMNU) Metro Lampung with Grant Number 108/LP3M/IAIM-NU/X/2019 REFERENCES [1] V. Erokhin. 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