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The assessment of students’ academic performance in higher education is generally still dominated by conventional numerical approaches, which are less capable of representing qualitative and subjective variables such as classroom activeness and student participation. These approaches often result in evaluations that are not holistic and do not fully reflect students’ overall academic achievements. Therefore, this study aims to analyze the concept of fuzzy logic as a support tool for assessing students’ academic performance in higher education, with a case study of students at Universitas Asahan. The research method employs a descriptive qualitative and quantitative approach by applying Mamdani fuzzy logic. The input variables consist of exam scores, assignment scores, and classroom activeness, while the system output is the category of academic performance, namely sufficient, good, and very good. The sample data consist of ten active undergraduate students from Universitas Asahan. The data processing stages include fuzzification, the construction of fuzzy rules (rule base), fuzzy inference, and defuzzification using the centroid method. The results indicate that fuzzy logic is able to integrate quantitative and qualitative variables and accommodate uncertainty in academic assessment. The resulting evaluation is more proportional and realistic compared to conventional assessment methods based solely on average scores. Therefore, fuzzy logic can be considered an effective and flexible alternative approach to support student academic performance assessment systems in higher education.

The process of determining eligibility for social assistance recipients is often constrained by subjective assessments and uncertainty in decision-making criteria. This condition can lead to inaccurate targeting and unfair distribution of aid. Therefore, an appropriate decision support method is required to handle data uncertainty effectively. This study aims to apply the Fuzzy Mamdani method to determine the eligibility of social assistance recipients based on several assessment criteria. The criteria used in this study include monthly income, number of dependents, and housing conditions. The research method consists of data collection, fuzzification, formulation of fuzzy rules, inference using the Mamdani approach, and defuzzification to obtain a crisp output value. The results show that the Fuzzy Mamdani method is able to classify recipients into eligible and non-eligible categories more flexibly compared to conventional methods. The generated eligibility values reflect real conditions more accurately by considering degrees of membership for each criterion. The implementation of this method can assist decision-makers in improving the accuracy, objectivity, and fairness of social assistance distribution. This research is expected to contribute to the development of intelligent decision support systems in the social welfare sector.

The process of determining scholarship eligibility often faces challenges related to subjectivity and uncertainty in assessment criteria, which can result in inaccurate and unfair decisions. Scholarship selection generally involves multiple criteria, such as academic achievement, family economic conditions, and supporting factors that are difficult to evaluate using conventional decision-making methods. Therefore, an appropriate decision support approach is required to handle such uncertainty effectively. This study aims to implement the Fuzzy Mamdani method in a decision support system to determine scholarship eligibility more objectively and accurately. The research method consists of data collection, fuzzification of input variables, formulation of fuzzy rules, inference using the Mamdani approach, and defuzzification using the centroid method to obtain a crisp eligibility value. The results show that the Fuzzy Mamdani method is capable of producing flexible eligibility scores by considering the degree of membership of each criterion. The generated output reflects real conditions more comprehensively compared to traditional methods. The implementation of this method can assist decision-makers in improving transparency, consistency, and fairness in scholarship selection. This research is expected to contribute to the development of intelligent decision support systems in the field of educational assessment.

In this study, a device was designed and implemented to control the water pH and nutrient density (concentration) in a hydroponic system using the Mamdani method of fuzzy logic, thus maintaining nutrient solution parameters within an optimal range for plant growth. This system relies on three input values ​​obtained from a water pH sensor, a nutrient TDS sensor, and a flow meter. These three sensors are used to control four peristaltic motors, each of which functions to increase and decrease the pH and nutrient levels in the solution. The speed of the peristaltic pump motor, when the water pH is set at 6.5 and the nutrient concentration is set at 700 ppm, is influenced by the difference between the sensor reading and the set point. The greater the difference, the higher the peristaltic pump motor speed. Conversely, the smaller the difference between the sensor reading and the set point, the lower the peristaltic pump motor speed. Furthermore, the amount of water flowing through the pipe also influences the peristaltic pump motor speed.

This research aims to be able to meet the water supply of lettuce plants automatically by using three sensors such as soil moisture, water level, and water discharge. The goal is to provide water needs to plants automatically and regularly. The developed tool uses YL-96 sensor for soil moisture, HC-SR04 for water level and YF-S201 for water discharge. Sensor data is sent to the arduino to be processed using the fuzzy mamdani method so that these three data values affect the movement of the tap servo motor that flows to the lettuce plant. Fuzzy logic here as a decision maker from the value of 3 sensor data and then processed automatically by arduino using fuzzy mamdani to determine how many degrees the servo motor moves. The result is that the Lettuce Plant Water Needs Analysis System Automation Tool is able to maintain the water supply of lettuce plants and soil moisture ideally at 76% with a servo motor movement system success rate of 100%.

The performance of data mining techniques has been proven accurate in many studies, but each method in data mining techniques has different accuracy depending on the type of data that is the object of research. Methods in data mining techniques are divided into several functions, namely: clustering, association, classification, and prediction, where each data mining technique objective has a superior method. Therefore, in this case the author will compare the performance of the multiple linear regression method, and neural networks with fuzzy mamdani in predicting the income of PLN Unit Takengon. In several studies, the Backpropagation method shows the highest accuracy compared to other methods. Then the prediction model with multiple linear regression also has the highest accuracy as well as the Fuzzy Mamdani method has high accuracy too. Therefore, the purpose of this study is to compare the three methods, so that it can be determined which method has a higher accuracy value. The results of this study indicate that the Back propagation method has the highest accuracy and the lowest average error, namely a MAPE value of 5.9% with an accuracy of 94.1% and an RMSE of 14398.14, followed by the multiple linear regression method obtaining a MAPE value of 6.9% with an accuracy of 93.1% and an RMSE of 15527.41, then for Fuzzy Mamdani obtaining a MAPE value of 7% with an accuracy of 93% and an RMSE of 16077.76.

A tourist attraction needs to measure the level of tourist satisfaction as feedback for the development of tourist attractions. The method that can be used is the Fuzzy Inference System Mamdani method. This method has a flexible approach in processing an input. The output is the average level of tourist satisfaction. The average level of tourist satisfaction of Green Gumuk Candi after the inference process is 73.1%. This result may differ from other Fuzzy Inference methods, so it still requires further research using the Sugeno or Tsukamoto method as a comparison.

Anemia is a condition of reduced red blood cells in the body lower than the normal amount, or what is commonly referred to as erythrocytes in the blood circulation or hemoglobin so that it is unable to fulfill its function as an oxygen carrier to all body tissues. An expert system is a system designed to mimic the experience of an expert in answering questions and solving problems. By using an expert system, a non-expert can solve problems and make decisions that would normally be made by an expert. Expert systems are now being developed in various fields, expert systems in the health sector are widely developed to detect various diseases with various methods. The fuzzy mamdani method is a method that is also often known as the MAX-MIN or MAX-PRODUCT method. The fuzzy mamdani method can help users in terms of detecting diseases based on symptoms that arise and lifestyle and provide diagnostic results for further action. Based on the description above, it can be seen that the fuzzy method is one method that has been widely used in software development for the diagnosis of a disease. Modeling this expert system to determine anemia based on the symptoms felt by patients.

In the digital era, home security has become a crucial aspect with the increasing risk of theft due to the weaknesses of traditional lock systems. This study develops an automatic door security system based on the Internet of Things (IoT) by integrating Radio Frequency Identification (RFID) technology and mobile applications. This system allows users to lock and unlock the door and monitor the door condition and battery power in real-time from a distance. To increase flexibility and security, this system also utilizes the Mamdani Fuzzy logic method in decision making, based on parameters such as battery power, user distance from the door, and environmental security level. With this approach, the system can dynamically adjust access according to the situation. The test results show that the developed system is able to provide a more efficient, secure, and adaptive security solution compared to conventional locks, as well as providing better convenience and control for users in managing home security intelligently and integratedly.  

The need for urban communities to consume vegetables is increasing. This has caused people to start cultivating vegetables using hydroponic techniques. However, due to their busy activities, they do not have enough time to monitor and control hydroponics, which must always be in ideal conditions. This paper designs and implements an Internet of Things-based monitoring system to help hydroponic owners monitor their hydroponics anywhere and anytime. The built system requires a monitoring device assembled using a NodeMCU ESP8266 microcontroller, a pH detection detector sensor, and a DHT22 temperature and humidity sensor. This system uses the Mamdani Fuzzy Logic algorithm to determine warnings to be displayed on the application interface when the water pH, temperature, and humidity are in certain conditions. The Mamdani Fuzzy Logic algorithm can interpret environmental data into a warning that humans can easily understand, even if they lack technical expertise. In addition to being able to help monitor, this system also allows owners to find out what elements need to be added or changed for their hydroponic place. Our evaluation results show that the defuzzification stage in the application has high accuracy, which is 99.92%, compared to Matlab’s results.

Indonesian people mostly tend to ignore health, especially dengue fever because the symptoms that arise are similar to common fever, this causes people with common fever symptoms to be reluctant to see a doctor. This study aims to help the public recognize the symptoms of dengue fever early by using the implementation of the fuzzy method in the dengue fever disease diagnosis system that uses specific datasets as a reference. Mamdani's fuzzy method, which is based on linguistic frameworks and fuzzy concepts, allows for the management of knowledge from experts for intuitive decision-making. The test results using the Confusion Matrix, the system showed an accuracy of 92.8%, an average precision of 90.6%, a recall of 95.8%, and an F1-Score of 92.6%, with an effectiveness value of 100%. This study proves that the system is able to provide information about dengue fever, diagnose based on symptoms, and offer solutions for its treatment.

In the increasingly competitive Industry 4.0 era, companies need to forecast product demand to meet consumer needs and improve operational efficiency. CV Mamifood Sukses Abadi, an MSME that produces milk and cheese-based foods, has faced sales fluctuations in the last two years, thus requiring accurate forecasting to plan production strategies and resource management. This research aims to forecast demand using the Fuzzy Mamdani method and the POM-QM application. Fuzzy Mamdani was chosen for its ability to handle decision-making with multiple criteria and balanced weights, while POM-QM was used to validate predictions through quantitative methods. Product sales data for the years 2022 and 2023 were analyzed to produce accurate forecasts. The methods used include Moving Average for forecasting and evaluation of the results using MAPE. The analysis results show that the Moving Average method with N = 2 produces a MAD value of 402.523 and a MAPE of 22.155%, while the results of Fuzzy Mamdani show that product demand in the next period tends to decrease. This research is expected to provide insight for CV Mamifood Sukses Abadi in planning a more efficient production strategy.

CV Wangun Mandiri is a manufacturing company that produces tapioca flour. In order to achieve maximum profit targets and smooth production activities, the company is faced with problems relating to the amount of tapioca flour products due to the uncertainty of its demand that tends to fluctuate and the imbalance of machine capacity. Therefore, it is required to plan the amount of production using the forecasting and fuzzy inference system approach as an effective method to determine the optimal production level. This research relies on the availability of datasets to determine the appropriate forecasting method and fuzzy method. The Fuzzy Mamdani method concludes that CV Wangun Mandiri can produce 82.9 tons to maximize existing demand and the capacity of its machines. 

Gas Sulfur adalah salah satu polutan berbahaya bagi manusia. Akibat utama polutan (SO2) terhadap manusia adalah terjadinya iritasi pada sistem pernafasan. Terutama kesehatan bagi usia lanjut dan penderita yang mengalami penyakit kronis pada sistem pernafasan dan kardiovaskular yang sangat sensitif jika kontak dengan (SO2). Sehingga pada penelitian ini perlu adanya sebuah rancang sistem monitoring kadar gas sulfur dioksida (SO2), suhu dan kelembaban pada ruangan. Sistem monitoring ini menggunakan sensor MQ-136 sebagai pendeteksi kadar gas sulfur dioksida (SO2) dengan satuan ppm dan sensor DHT11 sebagai pendeteksi suhu dan kelembapan pada ruangan. Hasil pembacaan kedua sensor tersebut akan diproses melalui Modul Wifi ESP32 dengan mempunyai output Aplikasi BLYNK dan Google Spreadsheet. Sementara output Exhaust Fan menyala sesuai dengan ketentuan Fuzzy Logic Mamdani.

Pencucian pakaian merupakan salah satu proses yang penting dalam kehidupan sehari-hari. Meskipun mesin cuci otomatis telah memberikan kemudahan dalam melakukan proses pencucian, penggunaan teknologi terbaru seperti kontrol logika fuzzy dapat meningkatkan kinerja mesin cuci pintar untuk mencapai hasil pencucian yang optimal. Penelitian ini bertujuan untuk mengimplementasikan kontrol logika fuzzy dengan metode Mamdani pada mesin cuci pintar guna meningkatkan efisiensi dan kualitas pencucian. Metode penelitian ini terdiri dari beberapa tahap, pertama, melakukan analisis terhadap variabel-variabel yang mempengaruhi proses pencucian, seperti beban pakaian, ketebalan pakaian, tingkat kekotoran, dan suhu air. Selanjutnya merancang sistem kontrol logika fuzzy Mamdani dengan menentukan fungsi keanggotaan untuk setiap variabel serta membuat aturan-aturan fuzzy untuk menghubungkan variabel input dan output dan yang terakhir melakukan evaluasi hasil pencucian terhadap variabel input yang telah ditentukan. Hasil penelitian menunjukkan bahwa implementasi kontrol logika fuzzy Mamdani pada mesin cuci pintar dapat meningkatkan kualitas pencucian yang signifikan dan adaptif dalam menentukan parameter-parameter pencucian dengan menghasilkan kualitas pencucian yang lebih bersih. Dengan demikian integrasi kontrol logika fuzzy Mamdani pada mesin cuci cerdas memiliki potensi meningkatkan kinerja pencucian secara keseluruhan. Kesimpulannya, penggunaan kontrol logika fuzzy Mamdani pada mesin cuci pintar merupakan langkah yang efektif untuk meningkatkan efisiensi dan efektivitas pencucian pakaian. Penelitian ini memberikan kontribusi dalam pengembangan teknologi mesin cuci pintar yang lebih adaptif dan ramah lingkungan, serta memberikan kontribusi penting dalam pengembangan teknologi dalam bidang kontrol logika fuzzy untuk aplikasi rumah tangga pintar lainnya.

Determining the provision of bonuses or rewards to employees plays a crucial role in maintaining the quality and motivation of employees. One effort that can be made to establish a bonus policy is by implementing the fuzzy Mamdani method as a systematic approach in determining employee bonuses, particularly at PT. ABC. By utilizing the fuzzy Mamdani method to process subjectivity in evaluations, it generates membership levels that allow for a more contextual employee assessment. Through the analysis of data involving various performance variables and bonus criteria, this research aims to present a fuzzy Mamdani system model that can support accurate and adaptive decision-making in determining employee bonuses. The results of this research and the evaluation of this model are expected to contribute significantly to the development of more effective bonus policies in the workplace of PT.

Kondisi pandemi COVID-19 tahun selama kurang lebih 2 tahun menjadi pembelajaran bagi Indonesia khususnya di bidang kesehatan. Problematika di bidang kesehatan semakin terlihat jelas di era pandemi COVID-19 ini. Suhu tubuh normal berkisar antar 36,5⁰C - 37,5⁰C dan bila melebihi 38,5⁰C maka ada indikasi terinfeksi covid-19 yang kemudian akan diarahkan untuk isolasi baik mandiri atau di tempat yang telah disediakan. Tujuan adanya ruang isolasi adalah untuk mengendalikan penyebaran penyakit menular yang bisa mewabah. Ruang isolasi adalah ruangan khusus, orang-orang yang bisa masuk ke ruangan ini juga sangat terbatas. Mengingat di ruang isolasi tidak bisa sembarangan keluar masuk, untuk itu dikembangkanlah sistem monitoring yang mampu memantau beberapa parameter yang dibutuhkan agar ruangan tersebut memenuhi standar operasional prosedur. Salah satunya adalah pengelolaan tata udara yang harus dijaga yaitu suhu ruangan dan kelembapan ruangan. Udara dalam ruangan dan kontaminan dalam ruangan minimal 6x pertukaran udara perjam dengan suhu ruangan 24 - 26 C dan kelembaban relatif 40 - 60%. Berdasarkan permasalahan diatas penulis ingin menerapkan Fuzzy logic metode Mamdani pada sistem monitoring suhu dan kelembapan ruangan pasien isolasi Covid-19 berabsis IOT ( Internet of Things ). Hal ini tentunya sangat membantu berbagai pihak mulai perorangan, hingga lembaga kesehatan utamanya rumah sakit umum. Dari pengujian metode Fuzzy Logic tersebut diambil 10 data yang dibandingkan nilainya dengan Matlab, dapat dihasilkan rata-rata error output PWM sebesar 0.1%. Bedasarkan pengujian metode black box yang dilakukan dapat diketahui tampilan sistem sesuai dengan tampilan yang diharapkan, dengan persentase kesesuaian 100%. Untuk pengujian fungsional dapat diketahui tampilan website dapat berfungsi dengan baik pada browser Opera, Chrome, dan juga Microsoft Edge.

The method fuzzy is one part of the fuzzy inference system that is useful for drawing conclusions or the best decision in uncertain problems. Currently, about 200 countries in the world are experiencing the Covid-19 pandemic. Various policies were carried out to overcome this. In addition to the health protocols that are always implemented, the community must also know the conditions of the area where they live so that they can anticipate any activities carried out. The method is used fuzzy to determine the risk zone for the spread of Covid-19 in North Sumatra Province based on 3 variables, namely positive cases, suspected cases, and dead cases. The data used in this study is weekly Covid-19 data from March 2021 to July 2021 for 4 selected areas, namely Medan City, Pematang Siantar City, Simalungun Regency, and Central Tapanuli Regency. All variables are represented using triangular curves and their membership functions are determined. Then the implication function of the min is used to determine the rules used. From the results of the implication function, the composition between all the rules obtained is carried out by taking the maximum rule then that value is used to modify the fuzzy area so that a new membership function is obtained. The method is used centroid to obtain a crisp or the final result is the value of the risk zone. This research also utilizes the Matlab to calculate the results. The results from the use of the Mamdani method will be compared with the real risk zone data so as to produce a 75% average percentage similarity for the data for 4 regions in North Sumatra so it can be concluded that the model made is good enough to determine the risk zone for the spread of Covid-19 in North Sumatra Province.

Rice is the most important staple food for humans to have energy to carry out activities. The purpose of this study is to provide information to the public in the form of a reference for selecting rice based on consumer interest, so that it can be used as a decision making in buying rice. The Mamdani method is a method that is able to solve problems in the case of rice selection based on consumer references. The work process of the Sugeno method consists of four parts, namely fuzification, inference engine, implication function amplification and the last one is defuzzification. The final result, the Mamdani method has the characteristics of using the AND operator and using the min-max value. This research is in the form of a decision-making system in rice selection based on references from consumers by looking at four aspects of criteria such as price, quality, taste, and shape variables, these four aspects can be used as a reference in rice selection. Sugeno fuzzy logic to get the final value.The Mamdani method is a very effective method in selecting rice according to the needs and interests of consumers so that potential consumers can easily choose rice according to their interests and desired criteria.

Smart City adalah skonsep tata kota yang mengoptimalkan teknologi informasi dan digital untuk meningkatkan kesejahteraan dan kebahagiaan masyarakat, serta meningkatkan layanan Pemerintah. Kota Pekalongan sedang berupaya untuk mempersiapkan diri dalam proses implementasi Smart City. Dalam referensi diketahui bahwa salah satu indikator kesiapan implementasi Smart City adalah Smart Governance, yang terdiri atasi empat indikator utama yaitu Participation in decision-making, public and social services, Transparent Governance, political strategies and perspectives. Dari keempat indikator tersebut diperjelas ke dalam indikator operasional yang lebih mudah diukur secara kuantitatif. Oleh sebab itu metode penelitian dipilih mix research methods karena data yang diperoleh dilakukan melalui cara kualitatif dengan wawancara kepada narasumber. Hasil data dikelola dan diolah menggunakan cara kuantitatif. Cara kuantitatif tersebut adalah metode Fuzzy Inference System (FIS) Mamdani. Dari keempat indikator utama diturunkan menjadi 21 variabel input Hasil yang diperoleh adalah tingkat kesiapan Kota Pekalongan dalam mengimplementasikan Smart City dari Perspektif Smart Governance adalah 1,5 (Sedang).