Mamdani Fuzzy Logic Research Articles

Optimization of Food Delivery Robot Using the PID Method Ziegler Nichols Type 2 and Fuzzy Logic Mamdani

In this research in this research, the Ziegler-Nichols Type 2 PID method was used in the development of a food delivery robot that has a line follower sensor. The PID control system is used to correct input variable (sensor) measurement errors so that the system output matches the set point value to produce the smallest possible error. Ziegler-Nichols Type 2 is used for path detection optimization. Fuzzy is used to adjust the speed of the robot’s movement based on the type and weight of food against obstacles in the path. In this research, a food delivery robot containing 0% liquid and 100% liquid will be tested. The calculation of this method results in Kp 1.2, Ki 7.038, and Kd 0.051, the overshoot value is 45.2581, and the rise time is 64.2857. The optimization was carried out using the trial-and-error method, and the results obtained were Kp 0.06, Ki 0.032, Kd 0.0001, the overshoot value was 40.7109, and the rise time was 45.2286. In the fuzzy logic test, it was found that the fastest time to reach a distance of 200 cm was 3.74 seconds at the initial PWM 47% with no food load, and the longest time was 17.41 seconds at the initial PWM 31.4% with a food load of 1300gram. This occurred when delivering food that contains 100% liquid, and the success rate in food delivery is 87.5%.

Nutrition control in nutrient film technique hydroponic system using fuzzy method

Hydroponics is a method of growing crops without using soil media, instead, it utilizes water or other porous materials. The automation system is applied to control the nutrient quality of the water. Some sensors were used for measuring nutrient concentration, water level, water supply, and water volume. Temperature, TDS, and ultrasonic sensors are installed on the microcontroller for data measurement. The results of the data are processed using the Mamdani fuzzy logic method. The fuzzy logic results are used in controlling nutrition and water volume. The system can be monitored remotely with the aid of the Blynk app. The results of the automation system concluded that the desired condition reaches an average time of 13 minutes 49 seconds. Finally, the results of the fuzzy logic processing of the system have an accuracy value of 94.24% after being compared with the software simulation.

Development of a simulation model for assessing the technical condition of the magnetic circuit of oil power transformers by measuring the temperature of the tank and the external environment

In the article, the authors analyzed how oil-filled power transformers ensure reliable and stable operation of power transmission systems by providing efficient transmission of electricity of different voltages. In the article, the authors found that the main elements of oil-filled power transformers - the magnetic circuit, the coil and the technical condition of the oil - directly determine the performance of oil-filled power transformers. In this work, a simulation model was developed to evaluate the magnetic circuit of power oil transformers based on the measured temperature of the tank and the external environment during express diagnosis. A simulation model has been developed that allows to calculate the technical condition of oil power transformers operating in city power networks. It is shown that the developed model allows to calculate the value of the technical condition of the oil power transformer with the help of diagnostic parameters. In order to represent the technical state of the magnetic circuit of the oil power transformer, the functions of changing the state value of the output variable in the Mamdani fuzzy logic method are shown and the block diagrams are drawn up. Author’s using the Mamdani fuzzy logic method, express diagnosis of the technical condition of the “Magnetic core of the oil power transformer” was carried out in the Matlab simulink program. During the evaluation, the value of the output parameter is taken from 0 to 100, and each step is divided by 10, the smallest value is 0, and the largest is 100.

Simulation of Determining Outstanding Lecturers Using the Fuzzy Logic

In the learning process, lecturers have quite an important role because a lecturer is required to manage or organize so that the learning process can run well. The objectives of writing this article are Determine the category of outstanding lecturers by applying fuzzy logic with the mamdani method. Produce a mathlab program developed using the mamdani method to determine outstanding lecturers at Graha Nusantara University. Today, information technology, especially soft computing technology, is developing very quickly. One of the most developed software computing technologies is fuzzy logic, as it can be used to measure various ambiguous, camouflaged or fuzzy phenomena. One of the research topics applying fuzzy logic is the evaluation system in the research field. Research conducted by professors from Graha Nusantara Padangsidimpuan University in Simlitabmas data Still in the category of orientation to move to intermediate level, professors from UGN Padangsidimpuan are challenged to be able to develop, contribute and apply knowledge required for research. To do this, we needed an application that could be used to calculate and record teacher performance in outcomes research. The purpose of this study is to apply fuzzy logic with Mamdani method to evaluate the research efficiency of professors at Graha Nusantara Padangsidimpuan University this study uses Mamdani fuzzy logic.

Drip Irrigation Control System based on Mamdani Fuzzy Logic and Internet of Things (IoT)

Drip Irrigation Control System based on Mamdani Fuzzy Logic and Internet of Things (IoT)

AVOCADO STOCK PREDICTION IN BANTUL CITY USING MAMDANI FUZZY LOGIC

This journal discusses the development of an avocado stock prediction model in Bantul City using the Mamdani fuzzy logic approach. In this context, fuzzy logic is used to address the uncertainty and complexity associated with factors affecting avocado supplies, such as stock variability, demand and price variables. The Mamdani approach is applied to formulate fuzzy rules based on a combination of expert knowledge and historical avocado stock data. This method aims to produce avocado stock predictions that are more accurate and adaptive to market dynamics. Through a series of experiments, the results show that the Mamdani fuzzy logic model has a significant level of accuracy, outperforming traditional stock prediction methods. The results obtained show the potential of this model in improving the efficiency of avocado inventory management at the local level. This research makes an important contribution especially in the context of agribusiness, providing a foundation for a more sophisticated and adaptive prediction approach to avocado fruit stock management. The implications are widely applicable in the agribusiness sector and provide a basis for the development of similar prediction systems for other agricultural commodities.

Penerapan Metode Logika Fuzzy Mamdani Untuk Menentukan Jumlah Pendapatan Penjualan Produk Umpan Pancing

Anugerah Jaya Finishing Store 99 is one of the online stores that sells fishing bait. Fishing bait is a crucial factor in improving the efficiency of fish capture, and there are two types of fishing bait : artificial bait and natural bait. Income is the money generated from the sale of goods or services to costumers and is the most important aspect in determining the smooth operation of a business. To determine the amount of income in this study, one of the fuzzy logic methods, namely mamdani fuzzy logic, was used. Mamdani fuzzy logic was chosen because it is the method that is most similar to human logic.

Valve control system on a venturi to control FiO2 a portable ventilator with fuzzy logic method based on microcontroller

<p>The results of several studies for portable ventilators state that it is difficult to<br />regulate oxygen levels of fraction of inspired oxygen (FiO2) that are in<br />accordance with what is needed directly during the implementation of oxygen<br />therapy. Some portable ventilators cannot set the FiO2, so the ventilators work<br />with a fixed FiO2. To overcome this problem, medical oxygen with a level of<br />100% is lowered by mixing with free air with an oxygen content of about 40%.<br />Mixing is carried out using a venturi with a large/wide hole that can be<br />adjusted using a slated disk rotated by a direct current motor. The rotation<br />control method uses Mamdani's fuzzy logic. The results of clinically lab-scale<br />testing show that the fuzzy logic control system is able to control the average<br />error pressure of 10.3%, better when compared to the on-off control method,<br />which is 14.5%. The fuzzy logic method is able to increase the accuracy of<br />FiO2 on a portable ventilator.</p>

Sistem Pemilah Otomatis Tingkat Kematangan Buah Kelapa Sawit Menggunakan Metode Logika Fuzzy Mamdani Dan Sensor TCS3200

The palm oil sector has a strategic impact on the growth of Indonesia's economy, because the fruit of palm oil produces oil which can be used as alternative fuel, food oil and basic materials for various industries. Currently, oil palm fruit is sorted manually based on color, which takes much longer. As a result, a system was created to categorize oil palm fruit according to their state of maturity. This system uses the TCS3200 sensor as the main sensor to detect the color of oil palm fruit and implements the Mamdani fuzzy logic method to classify it. Arduino Uno can control the hardware components used in the system. Data obtained from RGB color values ​​(red, green, blue) obtained by the TCS3200 sensor is used as input in the system. Meanwhile, the outcomes this system produced are in the form of maturity levels of oil palm fruit which are classified into 3 categories, namely unripe, ripe and past ripe. Based on tests carried out with the confusion matrix, the accuracy value obtained was 95.6%.

Klasifikasi Penyakit Gangguan Jiwa menggunakan Metode Logika Fuzzy

Purpose: This research aims to facilitate psychologists in handling individuals with mental disorders by categorizing them based on their symptoms and conditions using fuzzy logic, which mimics the functioning of the human brain.Design/methodology/approach: The categorization is performed by applying Mamdani fuzzy logic, designed in consultation with psychology experts. Ten initial symptoms each have parameters (Mild, Moderate, and Severe) as input variables, and the output variable involves mental health disorders such as Schizophrenia, Bipolar disorder, Eating disorders, and Anxiety. The fuzzy process employs the Mamdani method with IF-THEN rules and AND operators. The implementation of Mamdani fuzzy logic achieves adequate accuracy in classifying individuals with mental disorders, providing a strong foundation for a more targeted psychological approach. In the context of accuracy, fuzzification analysis for each health disorder can offer further insights.Findings/result: Results of the study for Schizophrenia, for instance, show a fuzzy diagram membership of approximately 0.4, indicating a potentially high level of thought impairment and interpersonal skills. Weighting for low, medium, and high is then assessed to categorize patients. A similar process is undertaken for Bipolar disorder, with special attention to the middle value and the strong relationship between two input values. Regarding mental illness, membership analysis indicates an increasing level of membership corresponding to condition groups, suggesting compatibility with existing rules.Originality/value/state of the art: These findings reinforce the Mamdani fuzzy logic implementation as a reliable approach in classifying individuals with mental disorders, with the potential to enhance psychological diagnosis and interventions more effectively

From Uncertainty to Precision: Advancing Industrial Rework Rate Analysis with Fuzzy Logic

The industrial sector plays a crucial role in the global economy by providing products to meet the ever-evolving societal needs. However, the relentless pursuit of quality and efficiency faces challenges, with one of the most significant obstacles being rework. Accordingly, this paper presents the development of a rework rate index in the industrial sector, using Mamdani-type fuzzy logic as the methodology, aiming to overcome the limitations of traditional approaches and capture the complexity and uncertainty of rework data. Seventeen indicators grouped into different categories were analyzed by assessing correlations to obtain the proposed index. The results demonstrated the effectiveness of the Mamdani fuzzy logic approach in evaluating rework rates, offering comprehensive insights and clear categorizations. The analysis of correlations among indicators revealed intricate interdependencies influencing rework rates. The creation of the Rework Reduction Index signifies a significant advancement in quality and efficiency management within the industrial sector. The fuzzy approach provides a comprehensive means to address data uncertainty and subjectivity, enabling a precise and contextual evaluation of rework rates. The results have direct implications for informed decision-making, allowing companies to identify problematic areas, allocate resources efficiently, and monitor progress over time. Furthermore, the proposed approach has the potential to inspire similar practices in other companies, contributing to enhancing efficiency and quality in industrial processes Future studies could extend the application of the Rework Reduction Index to various industrial sectors and explore the relationship between index classifications and traditional performance metrics.

Gastric Diagnosis Expert System using the Fuzzy Mamdani Method

The stomach is one of the important digestive organs in humans. Gastric disease is inflammation of the stomach lining caused by microorganisms, this disease is caused more by Helycobacterpylori bacteria, apart from being caused by bacteria, stomach disease can also be caused by irregular lifestyle and eating patterns. Diseases that attack the stomach are still considered trivial by the general public, so not many people know about stomach diseases and the symptoms that exist. This is what causes people to be reluctant to see a doctor when they suffer from pain that attacks the stomach. When a disease attacks the stomach, people only use experience or intuition to cure it, so it is not treated properly. By using an expert system, patients can save time going to the hospital and can improve service to patients. The results of the Mamdani fuzzy logic calculations require a report on the possibility of stomach disease suffered by the user/patient, examples of possibilities are small, somewhat large and large based on the highest value results. It is hoped that with this system, it will be easier for patients to diagnose gastric diseases to carry out prevention and early diagnosis and treatment.

Application of Mamdani's Fuzzy Logic in Determining the Price of Silk Fabric in Ujung Pandang District

The development of the silk textile industry in Indonesia is supported by several provinces including South Sulawesi. South Sulawesi has several centers for the development of mulberry and silkworm cultivation which are the basic ingredients for making silk cloth. Silk cloth is one of the processed products from silk thread which is produced from cocoons produced by silkworm cultivation and woven into silk cloth. Traditional woven fabrics produced in regions in Indonesia, including South Sulawesi, are not only made for clothing purposes. The purpose of this study is to determine the price of silk cloth by applying the Mamdani fuzzy method to the MATLAB software, so that silk cloth businesses can set prices according to the quality of the goods. Fuzzy logic is considered capable of mapping an input into an output without ignoring the existing factors. Based on this, fuzzy logic will produce a model of a system capable of estimating selling prices.

IoT-based Mamdani Fuzzy Logic System for Monitoring Air Pollution in Sukabumi Regency

Air pollution is currently a serious issue that has a significant impact on health. Unfortunately, information about air pollution in the surrounding environment is limited, and the Air Quality Index (AQI) facilities are also inadequate. In this research, we developed an air pollution monitoring system in Sukabumi Regency using Raspberry Pi, Arduino Uno, and the Mamdani Fuzzy Logic method based on the Internet of Things (IoT) with a Telegram Bot. The Mamdani Fuzzy Logic was employed to make decisions regarding the levels of CO and CO2 gases in the air. Prototype testing results indicated that this system is capable of providing real-time air pollution information to users via a Telegram Bot with an accuracy of approximately 88.8%.

Analyzing Aircraft Landing Decision Making Through Fuzzy Logic Approach: A Comparative Study

Due to the importance of weather in people's lives, various groups have advocated for accurate climate information. However, weather predictions can often be unclear or ambiguous. Weather advice and information are crucial in determining the safety of landing an aircraft in aviation. To address this, Mamdani Fuzzy Logic will be used to compare two scenarios: one with three inputs (wind direction, wind speed, and visibility) and another that includes the pilot's experience to assess its impact on the landing process. A fuzzy logic-based intelligent system generates three decisions: feasible, careful, and not feasible for landing an aircraft on a runway. The difference rate between the two experiments was 68%, indicating that the pilot's experience played a significant role and forced its importance in the results.

Prototype Detection System Of Papaya Murability Using Fuzzy Logic Method Based On Color Sensor

Product selection based on the ripeness quality of papaya fruit is one of the stages in the processing of plantation products. At this time, the process of sorting papaya fruit is still using manual methods, especially in estimating the level of ripeness. Papaya is considered a climacteric fruit where respiration occurs and its ethylene production suddenly increases and then rapidly falls off. Fruit with a low respiration rate has a longer shelf life than fruit with a high respiration rate. To overcome this problem, this research created a prototype of a papaya fruit ripeness detection system using a color sensor-based fuzzy logic method. This fruit maturity detector can provide information related to fruit ripeness (unripe, half ripe, and ripe) which is determined from the color of the papaya fruit which is then processed using the Mamdani fuzzy logic method. The results of the prototype design can measure fruit with an accuracy rate of 93.33% of the 30 data tested by using tools that are in a cool place, with low intensity of incoming sunlight.
 Keywords: Papaya fruit ripeness, TCS3200 Sensor, Mamdani Fuzzy Logic.

A fuzzy based dietary clinical decision support system for patients with multiple chronic conditions (MCCs)

Due to the multifaceted nature of Multiple Chronic Conditions (MCCs), setting a diet for these patients is complicated and time-consuming. In this study, a clinical decision support system based on fuzzy logic was modeled and evaluated to aid dietitians in adjusting the diet for patients with MCCs. Mamdani fuzzy logic with 1144 rules was applied to design the model for MCCs patients over 18 years who suffer from one or more chronic diseases, including obesity, diabetes, hypertension, hyperlipidemia, and kidney disease. One hundred nutrition records from three nutrition clinics were employed to measure the system's performance. The findings showed that the diet set by nutritionists had no statistically significant difference from the diet recommended by the fuzzy model (p > 0.05), and there was a strong correlation close to one between them. In addition, the results indicated a suitable model performance with an accuracy of about 97%. This system could adjust the diet with high accuracy as well as humans. In addition, it could increase dietitians' confidence, precision, and speed in setting the diet for MCCs patients.

A Distributed Particle-Swarm-Optimization-Based Fuzzy Clustering Protocol for Wireless Sensor Networks.

Clustering is considered to be one of the most effective ways for energy preservation and lifetime maximization in wireless sensor networks (WSNs) because the sensor nodes are equipped with limited energy. Thus, energy efficiency and energy balance have always been the main challenges faced by clustering approaches. To overcome these, a distributed particle swarm optimization-based fuzzy clustering protocol called DPFCP is proposed in this paper to reduce and balance energy consumption, to thereby extend the network lifetime as long as possible. To this end, in DPFCP cluster heads (CHs) are nominated by a Mamdani fuzzy logic system with descriptors' residual energy, node degree, distance to the base station (BS), and distance to the centroid. Moreover, a particle swarm optimization (PSO) algorithm is applied to optimize the fuzzy rules, instead of conventional manual design. Thus, the best nodes are ensured to be selected as CHs for energy reduction. Once the CHs are selected, distance to the CH, residual energy, and deviation in the CH's number of members are considered for the non-CH joining cluster in order to form energy-balanced clusters. Finally, an on-demand mechanism, instead of periodic re-clustering, is utilized to maintain clusters locally and globally based on local information, so as to further reduce computation and message overheads, thereby saving energy consumption. Compared with the existing relevant protocols, the performance of DPFCP was verified by extensive simulation experiments. The results show that, on average, DPFCP improves energy consumption by 38.20%, 15.85%, 21.15%, and 13.06% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO, and increases network lifetime by 46.19%, 20.69%, 20.44%, and 10.99% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO, respectively. Moreover, the standard deviation of the residual network was reduced by 61.88%, 55.36%, 54.02%, and 19.39% compared to LEACH, LEACH-SF, FLS-PSO, and KM-PSO. It is thus clear that the proposed DPFCP protocol efficiently balances energy consumption to improve the overall network performance and maximize the network lifetime.

IMPLEMENTASI FUZZY LOGIC DALAM MENENTUKAN HARGA PERBAIKAN ALTERNATOR MENGGUNAKAN METODE MAMDANI

The alternator is the component of the generator that produces electricity. A short circuit in the alternator causes a surge in electric current so that it can cause an explosion of sparks, it is necessary to prevent an electric short circuit so that maintenance is needed to repair the alternator. Problems in making decisions also occur in companies, in transactions that are used by companies that are still done manually, in determining the price of alternator repairs it is still based on estimates and during the price determination process you have to wait for confirmation from the company's leadership so that the process takes longer and the error rate is relatively high. Fuzzy logic mamdani one method that can be used to overcome this problem. This research will get the best price quickly and minimize the error rate in pricing and adapt it to the implementation design in MATLAB. In the process of determining the repair price, several weighted criteria are required as input variables, namely alternator capacity, level of difficulty and warranty period, which will then produce output variables, namely alternator repair prices. This study aims to assist companies in determining the price of alternator repairs.

Fuzzy logic approach for seismic fragility analysis of RC frames with applications to earthquake-induced damage and construction quality

This study presents a fuzzy logic model which is able to consider various uncertainties for predicting the seismic fragility curves of the structure. The fuzzy models are generated based on a database (generated by the Latin Hypercube Sampling technique) which includes structure’s modeling parameters as input variables. Also, the output section of the database comprises the structure’s fragility parameters at three different limit states. Several classification decision tree-based Mamdani fuzzy logic models are constructed by converting the above-mentioned numerical database to qualitative labels. The appropriate number of membership functions and also pruning levels of the related decision tree are detected by automatically introducing the classification tree-based rules to the fuzzy model and performing about two million fuzzy analyses while evaluating the associated error versus the number of rules. On the basis of 200 structural models, a set of interpretable Mamdani fuzzy models are generated to predict the fragility curve parameters at different seismic performance levels. These fuzzy models can effortlessly perform further assessments such as sensitivity analysis and seismic performance assessment while considering the impact of earthquake damage and construction quality.