Fuzzy Sugeno Method Research Articles

Development of a System for Determining Loan Eligibility in the Bukit Indah Cooperative, Batunya Village Using the Fuzzy Sugeno Method

This study aims to develop a loan eligibility determination system at the Bukit Indah Cooperative in Batunya Village using the fuzzy sugeno method. The Bukit Indah Cooperative is a savings and business cooperative run independently by the Batunya Village community and carries out efforts for the welfare of the Batunya Village community. Fuzzy Sugeno is one of the fuzzy logic methods used for modeling, control and decision making. Fuzzy Sugeno was developed by Takagi, Sugeno, and Kang in 1985, this method aims to provide more efficient results in the control and decision making system. The research method used in this study is the waterfall method. This study has succeeded in developing and producing a system used to help the Bukit Indah Cooperative in Batunya Village to determine the eligibility of providing loans to customers. In testing the feasibility of the system with 20 sample data, it was found that out of 20 sample data, there were 18 correct data in the system, data comparison was carried out by comparing the data from the evaluation of calculations by cooperative officers with the data produced by the system which produced an accuracy level of 90.00%. Based on these results, it was found that the system developed can be said to work well and is considered sufficient to test the accuracy of the fuzzy Sugeno method.

Optimizing Plant Watering Efficiency via IoT: Fuzzy Sugeno Method with ESP8266 Microcontroller

This research explores automatic plant sprinklers utilizing fuzzy logic with the fuzzy Sugeno method to overcome optimal watering schedules. Developed with ESP8266 microcontroller hardware and Arduino IDE software, the WSN prototype focuses on spinach plants. Watering in the nursery was done twice a day for seedling growth, then reduced to once a day. Soil moisture levels were assessed using a soil analyzer, as well as temperature variations with the HTC-1 device. Python facilitates data analysis, generating fuzzy inference graphs on temperature sensors (10-40°C) and soil moisture values (1-1024 RH). This innovative approach, which integrates fuzzy logic and advanced hardware, offers a promising solution for effective plant care.

Rancang Bangun Alat Pemilah Biji Kopi Berdasarkan Kualitas Buah Menggunakan Sensor TCS3200

Coffee fruit is a trade commodity that has been known for centuries. Coffee fruit can be processed into a delicious drink, but when coffee farmers harvest at the same time, farmers combine immature and ripe coffee fruit in one container, this results in a decrease in the quality of the taste. significant. In this research, the fuzzy Sugeno method was used to determine the classification of ripe, semi-ripe and ripe coffee fruit. This tool uses an Arduino Uno Atmega328 microcontroller to control the TCS3200 color sensor as a reader for the color of the coffee fruit and uses a servo to sort the coffee fruit according to quality. In testing the automatic control system for separating coffee beans based on color using the TCS3200 Color Sensor, it was successful in directing the coffee beans to a predetermined container. Keywords— TCS3200 Sensor, Arduino ATmega328P,Coffee,Color,Quality,Fuzzy sugeno.

Prediksi Tingkat Produksi Bawang Goreng menggunakan Metode K-Means dan Fuzzy Inference System

Shallots are a strategic commodity because they are needed for household consumption as well as the food industry. Shallots are usually used as a cooking spice, or as a topping for food dishes called fried onions. Shallots are easily damaged, one way to prevent damage is to process shallots into fried onions. Sales of fried onions fluctuate every month due to consumer demand, therefore in this research a grouping of production levels and predictions of fried onion production was carried out. The methods used in this research are K-Means and Fuzzy Sugeno. From the results of research using the K-Means method, there are 3 clusters of fried onion production levels, namely high, medium and small production levels. High production levels were found in months 4, 5, 9, and 10; moderate production levels in months 1, 2, 3, 6, 7, 8 and 11; while a small production level was found in the 12th month. Based on system testing using the fuzzy Sugeno method, data was generated that could be processed and produce 9 rules to serve as a reference in predicting fried onion production for the following years. Apart from that, based on the results of the Mean Absolute Percent Error calculation, the capability of the model created is good and accurate because it has a value of 14.2%. Fried onion production levels in the 4th and 12th months have more accurate predictions compared to other months

Optimizing the Diet of Diabetes Mellitus Sufferers Through Food Mapping Using the Fuzzy Sugeno Method

This research focuses on developing optimal strategies for diet management for Diabetes Mellitus (DM) sufferers by utilizing the Fuzzy Sugeno method in food mapping. Diabetes Mellitus is a chronic metabolic disease that requires a careful management approach to diet to control blood sugar levels. In this study, we designed a system that uses Sugeno's fuzzy principles to categorize foods based on glycemic parameters, nutritional content, and individual patient characteristics. This method allows the formation of fuzzy rules that cover the variability and complexity in the preferences and health needs of each patient. The developed model was tested using DM patient data involving detailed information about diet, medical history and blood sugar response. Experimental results show that this approach can provide more personalized diet recommendations that suit individual health conditions. The application of the Fuzzy Sugeno method in food mapping for DM patients is expected to increase patient compliance with the recommended diet, reduce blood sugar levels, and overall, improve quality of life. Additionally, this approach also provides a foundation for the development of adaptive dietary management systems, which can continuously adapt to changing patient health needs over time.

Monitoring Suhu Pada Tanaman Seledri Berbasis Internet of Things Dengan Metode Fuzzy Sugeno

In the process of plant growth is influenced by many factors one of which is the weather climate. Many crop failures occur due to unstable weather. One of them is the celery plant because it is susceptible to weather. Therefore an automatic temperature monitoring system is designed that can be monitored from a smartphone using a fuzzy Sugeno method. Starting from reading the DHT11 sensor which is then processed by ESP32 which then sends the data to the smartphone via the Inventor App, then ESP32 gives commands to the output which then works based on the conditions that occur from the results of sensor readings with PWM fuzzy sugeno. The parameter being monitored is the temperature of the celery plant. The reading results will be displayed on App Inventor as an Internet of Things (IoT) support system

A NEW MODEL FOR HYDROPONIC LETTUCE NUTRITION ADAPTIVE CONTROL SYSTEM BASED ON FUZZY LOGIC SUGENO METHOD USING ESP32

In the last few years, the terms Smart Agriculture, Smart Farming, Urban Farming, or Precision Farming have been increasingly recognized and growing rapidly. Hydroponics is one part that is currently a trend, both in industrial or household scale businesses and hobbies. One of the most important things to consider in maintaining the quality of hydroponic plant growth is the concentration of nutrients in the water. A series of studies have been conducted to improve the quality of hydroponic plants. However, the developments that have been carried out have not focused on optimal nutritional control. The previous hydroponic plant nutrition control system still used conventional methods, namely the use of a rule base with firm values ​​, and did not consider the quantity and quality of water. Therefore, this study proposes a new model for an adaptive control system for hydroponic lettuce nutrition based on the Fuzzy Logic Sugeno method using ESP32. The fuzzy logic Sugeno method is used to create a new model of the inference system for determining the amount of nutrient dosage based on supporting data obtained from sensors installed on hydroponic growing media. Compared with the conventional method, the resulting test results show that the proposed method can adapt the amount of added nutrients, provide optimal nutrient addition output, and prevent excess nutrient additions that can potentially accumulate toxic ions in water that degrade water quality.

Diagnosa Kecanduan Gadget Pada Anak Usia Dini dengan Metode Fuzzy Sugeno dan Fuzzy Mamdani

The Tsukamoto fuzzy logic method is one method for problem solving control systems, which is good to implement with a system method that will use human knowledge that can be entered into the system on a computer to find and solve problems that usually require expertise or expertise. looking for a truth. The purpose of this study is to analyze and find methods that can be used in diagnosing gadget addiction in early childhood, and to compare two fuzzy methods that will be used as methods to diagnose gadget addiction in early childhood. The methods used in this study are expert system methods, namely fuzzy sugeno and fuzzy mamdani. The results showed that many children experience addiction to gadgets with a susceptibility to 1-4 hours of use time in one day. The level of addiction in boys is somewhat higher than in girls, the large number of children who are addicted to gadgets at a very early age to recognize and experience addiction to gadgets is the author's main goal to make this thesis if it can be used to monitor the level of gadget addiction in early childhood, research on the contribution of the method of applying the method the fuzzy sugeno and fuzzy mamdani algorithms in diagnosing gadget addiction in early childhood and the contribution to this method is the application of the fuzzy mamdani method and the fuzzy sugeno method to the fuzzy algorithm and the problem of this research is to determine the results of defuzzification of the data that has been obtained.

Development of nominal rules on the Fuzzy Sugeno method to determine the quality of power transformer insulation oil using Dissolved Gas Analysis data

This paper aims to develop the nominal rules on the Fuzzy Logic Method using the Sugeno-Fuzzy Inference System (FIS) for Dissolved Gas Analysis (DGA) and determine the quality of the power Transformer 1 and Transformer 6 insulating oil at the Buduran 150 kV substation. The nominal number of proposed fuzzy rules is 1920 rules. Implementing the Fuzzy-Sugeno method on Transformers 1 and 6 shows that the six input variables from the DGA test can produce a Total Dissolved Combustible Gas (TDCG) output value of 32.67 and 26.19 ppm, respectively. Both values indicate that the insulating oil of Transformers 1 and 6 are in condition one and, at the same time, indicates that the dissolved gas composition is in Normal status. Furthermore, the TDCG value, condition, and quality status of the insulating oil have the same or 100 % accuracy compared to the DGA test by PLN (UPT Surabaya). Thus, the nominal development of fuzzy rules using the Fuzzy-Sugeno method can perform DGA analysis more accurately to determine the quality of power transformer insulation oil compared to previous studies.

Fuzzy Sugeno Method for Opinion Classification Regarding Policy of PPKM and Covid-19 Vaccination

The Indonesian government has implemented various interventions to overcome the impact of the Covid-19 pandemic, including those written in Minister of Home Affairs Instructions on PPKM (Community Activities Restrictions Enforcement) and Covid-19 vaccination policies. This policy are not at least reaping the pros and cons, so it is necessary to monitor public opinion to be able to provide solutions or become an evaluation of future policies. The aim of this study is to determine the polarity of public opinion regarding PPKM and Covid-19 vaccinations policies on Twitter, as well as to determine the implementation of FIS Sugeno in classifying textual data. There are several stages carried out, i.e. data collection, data pre-processing, data labeling, data weighting, identification of membership functions, determination of fuzzy sets, formation of a classification system, and evaluation of classification results. In this study, the performance of FIS Sugeno in classifying tweets was quite good with an average accuracy of 89.13%. Meanwhile, public opinion regarding the PPKM and Covid-19 vaccination policies tends to be balanced with 36.92% of tweets classified as a positive sentiments, 22.85% being negative sentiments, and another 40.23% belonging to neutral sentiments.

Automatic Plant Watering System Based on Air Temperature and Soil Humidity Using the Fuzzy Sugeno Method

Plants need water to grow well, so watering must follow the plant's needs. Parameters that affect the process of watering plants are air temperature and soil moisture, thus, we need a control system that can make decisions in the form of watering time to meet plant water needs according to their needs, namely an automatic watering system. Fuzzy method can be used for human-like decision making in a control system. This study uses the Sugeno fuzzy method which is integrated into the Microcontroller as the controller. Testing of this system is carried out on eggplant plants which are regulated based on how long the watering time is so that the air temperature and soil moisture are following the plant's needs. The test resulted that the effective watering time interval so that the eggplant soil moisture was maintained at 40-60% was 21 seconds. It can be concluded that the fuzzy inference embedded in the design has been able to overcome the problem of watering eggplant plants, where the test shows that after the watering process, the soil moisture value can be maintained at an average value of 55% so that the soil moisture is following the needs of the plant.

COVID-19 Vaccination and PPKM Policy with the Implementation of the Fuzzy Sugeno Method to Income Classification

This study aims to determine the implementation of Fuzzy Sugeno in classifying textual data obtained from Twitter so as to determine the polarity of public opinion regarding PPKM policies and Covid-19 vaccinations. This study uses primary data via Twitter related to COVID-19 vaccination and PPKM policies in Indonesia starting from February 9, 2021 to January 17, 2022. There are several stages carried out, namely data collection, data pre-processing, data labeling, data weighting. , identification of membership functions, determination of fuzzy sets, formation of classification systems, and evaluation of classification results. The results of this study explain that Fuzzy Sugeno's performance in classifying tweets is quite good with an average accuracy of 89.13%. Meanwhile, public opinion regarding PPKM policies and Covid-19 vaccinations tends to be balanced with 36.92% of tweets classified as positive sentiments, 22.85% negative sentiments, and another 40.23% classified as neutral sentiments. In addition, the fuzzy set that is formed based on the data observation method is very well done because it is able to adjust the frequency of the data in each category. This really helps improve the performance of the built classification system.

Automatic Water Desalination Device using Sugeno Fuzzy Logic (Case Study: Tangkil Coast)

Water desalination is the process of making fresh seawater or the process of making fresh water from salt water. This study uses a thermal desalination process because the salt content produced is less than the results from membrane desalination. This fuzzy method also has several variations in its application. The fuzzy Sugeno method used in this study differs from other methods, that is, with this method the results are in the form of constants or linear equations so that they can automate the desalination process. Tangkil Island is used as the object in this case study because it is a center for family tourism but has difficult access to fresh water. This study tested the results of desalination water by using a salinity measuring instrument with the type of salinity-615 as the standard for reading salt levels. The comparison results have an error value of above 50% due to the difference in the reading digits of the salinity-615 tool, which only has a value of 1 digit behind the comma, so that the results compared with the salt level sensor (embedded), which can read 3 digits behind the comma, have a high error percentage. The results of the tests carried out using Matlab with the actual conditions at the temperature, humidity, and water level observed that the desalination process runs automatically. From the results of these tests, there are very large differences in the calculation of the evaporation rate. The temperature and humidity factors can change at any time, which causes very large differences from the calculation results. In line with the increase in temperature and decrease in humidity, the evaporation process will be faster but will experience a slowdown in the evaporation process if the opposite occurs.

Perbandingan Logika Fuzzy Metode Sugeno dan Metode Mamdani Untuk Deteksi Dini Penyakit Stroke

Stroke is a neurological function disorder caused by disruption of blood flow in the brain that arises suddenly and acutely within a few seconds or more precisely within a few hours that lasts more than 24 hours with symptoms or signs according to the affected area. Early detection of stroke usually takes a long time. With advances in technology, stroke can be prevented by detecting the risk early so that it can be treated quickly and increase the chances of recovery. The discussion of this research is about early detection of stroke risk by comparing using fuzzy logic Sugeno method and Mamdani method and using patient data at Dr. Hospital. H. Isaac Umarella. By using input variables in the form of: blood pressure, age, LDL, and blood sugar levels. Based on the results obtained from the calculation of Error with Mean Absolute Percentage Error (MAPE), the level of truth of the calculation of the Sugeno method is 87%, while the truth level of the Mamdani method is 85% so that it can be said that both methods get good results but Sugeno's fuzzy logic is superior with a value of small MAPE. In conclusion, fuzzy logic with the Sugeno method can be used in early detection of stroke risk.

Application of the Fuzzy Sugeno Method in a Decision Support System for Teacher Performance Assessment

<p class="Abstract">The teacher is a professional educator who has essential duties, functions, and roles in the nation's intellectual life. In order for the functions and duties attached to the functional positions of teachers to be carried out following applicable regulations, it is necessary to assess teacher performance which ensures a quality learning process at all levels of education. Currently, teacher performance evaluation uses a manual assessment system, which causes the evaluation process to be relatively long. For this reason, a decision support system is needed that can take into account all the criteria that support decision making in order to assist, speed up and simplify the decision-making process. In determining teacher performance assessment, the method used is Fuzzy Takagi-Sugeno Kang (Fuzzy Sugeno). This method was chosen because the Fuzzy Sugeno method is a decision support model where the main input uses the basic concept of finding the weighted summation. The Fuzzy Sugeno method for assessing teacher performance uses three stages: the Fuzzification Stage, the Implication Function Stage, and the Defuzzification Stage. The level of validity with Sugeno's Fuzzy Inference Systems (FIS) method for assessing teacher performance is very good.</p>

Analisis Fuzzy Sugeno Dalam Menentukan Pemilihan Motor Honda

Motorcycles are two-wheeled vehicles. Honda is one of the best selling motorcycle brands in Indonesia. Honda is a Japanese company that has been very tested in Indonesia. Honda also has many types, because of the many types of Honda motorcycles, a decision support system (DSS) with the fuzzy Sugeno method is needed in overcoming the selection of Honda motorcycles. At the same time to compare previous research that has been done with the Mamdani method in determining the selection of the Honda motorcycle.

Kinerja Logika Fuzzy Sugeno dalam Menangani Prediksi Kain Tenun dengan Kombinasi Random Tree dalam Membangun Rule

This study describes the performance of Sugeno fuzzy logic in determining the amount of woven fabric production by using a combination of random tree decision trees in forming rules. The criteria used in determining the amount of production, namely, production costs, demand, and stock obtained from woven fabric entrepreneurs in Mlaki Wanarejan Utara Pemalang. The random tree decision tree is used, one of which is to automatically generate rules from the available data without consulting with experts, in addition to introducing random trees in the field of research because there are still few studies using this decision tree. The results of this study, it was found that the accuracy while the prediction results tested obtained an Average Forecasting Error Rate (AFER) of 42% with a value 58% truth after being compared with the actual production data.Keywords : Fuzzy Logic, Fuzzy Sugeno Method, Rule, Random tree, Prediction.

Design of a Prototype of Smoke Exhaust Control System in an Enclosed Room with the Fuzzy Sugeno Method

Cigarette smoke can contaminate indoor air that causes some health risks. Smoke control in enclosed rooms is necessary to reduce the thick smoke in them by controlling the CO content index to the desired point. The purpose of the study, knowing the grouping of the density of smoke and the time in disposing of smoke to the point of a predetermined setpoint value of 60 ppm. This research applied the Fuzzy Logic Sugeno Method and hardware design using a prototype in the form of an acrylic box with a ratio of 1:10 of the actual size as a tool test site; LCD to display information, namely, in the form of smoke density, fan, setpoint value, and smoke value; MQ2 sensors for CO smoke detection; Arduino Nano as a microcontroller; and relay and fan as output. The experiment results resulted in 3 categories of smoke: low, medium, and high. Besides, the results of this study also showed that the time required to emit smoke in a closed room to the point of a setpoint value of 60 ppm with the help of 3 fans on each fan 12v / 0.14A is 2 minutes 05 seconds.

Implementation of Fuzzy Logic on Internet of Things-Based Greenhouse

This research is an internet of things-based monitoring system using LoRaWAN and the IoT Thingspeak application server. This research is focused on helping farmers, or the agriculture sector focused on Pakcoy. Pakcoy is a plant that can live at an altitude of 500-1200 meters above sea level using the Greenhouse concept; Pakcoy plants can be protected from pests. Nutrients for Pakcoy are sprayed through the leaves, accelerating the provision of nutrients for plants. The Fuzzy Sugeno method is used to control the Greenhouse temperature and Soil Moisture for effective plant development, as well as monitoring the Greenhouse temperature, soil moisture, and pH of the growing media based on the Internet of Things that uses the Long Range 915 Mhz, therefore, the users can find out in real-time the data can be accessed by using Thingspeak and can be used in areas where internet access is difficult.

Air Pullution Monitoring and Detection System Design Using Fuzzy Method Based on IoT

The large amount of air pollution that occurs in the community is caused by the increase in the number of motorized vehicles and human activities, as well as the limited sense of sight and smell of humans so that they cannot feel the presence of pollutant gases that are harmful to health, so we need a tool that can detect and monitor pollutant gases so they don't exceed the threshold. In this study, a monitoring system and air pollution detection using the fuzzy Sugeno method based on the Internet of Things (IoT) is designed. In this system the MQ-7, MQ-135 and MQ-131 sensors are used to detect CO, CO2 and Ozone gases, while the Sharp GP2Y1010AU0F sensor functions to detect dust. The results of these sensor readings are processed by the Arduino Uno and NodeMCU microcontrollers to be displayed on the P10 panel and sent to the Antares IoT Cloud server which can be accessed in real time. The results of this study have an accuracy rate of approximately 97% for gas sensors, both CO, CO2, and Ozone gas sensors. As for the dust sensor, the accuracy rate is 93.83%.