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Paper ini mempresentasikan pengembangan sebuah fuzzy logic controller (FLC) untuk menggantikan automatic voltage regulator (AVR) sebagai fungsi eksitasi. Metode yang digunakan pada paper ini merupakan teknik analisa root locus sistem linier untuk respon regulasi dinamis  mesin sinkron yang berhubungan ke infinite bus. Simulasi dilakukan untuk membandingkan respon stabilitas transien  tegangan terminal (terminal voltage), sudut beban (load angle) serta torsi kelistrikan (electrical torque). Karakteristik kurva yang dihasilkan akan menunjukkan perbaikan yang signifikan dengan menggunakan FLC.

The quality of boiler feedwater is a crucial factor in maintaining the operational reliability of ship machinery systems against the risks of corrosion and scale formation. This research focuses on the design and development of a real-time monitoring system based on the Internet of Things (IoT) to automatically determine water quality. The hardware development utilizes a NodeMCU ESP32 microcontroller that integrates four main sensors: a pH sensor, a DS18B20 temperature sensor, a Total Dissolved Solids (TDS) sensor, and a turbidity sensor. The methodology applied is an experimental systems engineering model, encompassing circuit design, software programming, and the implementation of simple artificial intelligence. The automatic decision-making process in this tool is designed using zero-order Sugeno fuzzy logic with input parameters of turbidity, salinity, and acidity level (pH). The obtained data is transmitted wirelessly to a Firebase database and visualized through a 0.96-inch OLED display. Functional tests on seven fluid characteristics including pure water, hard water, and muddy water show that this prototype can accurately classify water quality into Good, Fair, and Poor categories. The main contribution of this design is the provision of a preventive, efficient, and applicable boiler maintenance solution for the modern shipping industry.

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 determination of scholarship recipients is a very important process in supporting students’ educational success, particularly in providing fair opportunities for high-achieving students who require financial assistance. However, in practice, this process often faces various challenges, such as assessor subjectivity and uncertainty in evaluating the applied criteria. Therefore, a decision support system is needed to assist decision-making in an objective and measurable manner. This study aims to implement the Fuzzy Tsukamoto method as a decision support system for determining scholarship eligibility. The criteria used in this study include Grade Point Average (GPA) as an indicator of academic achievement and parents’ income as an indicator of students’ economic conditions. The Fuzzy Tsukamoto method was selected because it is capable of producing crisp output values based on predefined fuzzy rules. Student data were processed through several stages, namely fuzzification to transform input data into fuzzy values, inference using the minimum operator, and defuzzification using the weighted average method. The results of the study indicate that the application of the Fuzzy Tsukamoto method is able to generate more objective, consistent, and measurable decisions. Based on the calculation results, a scholarship eligibility score of 63.9 was obtained, which falls into the eligible category. Thus, the Fuzzy Tsukamoto method can be considered an effective alternative to support fair, systematic, and transparent decision-making in determining scholarship recipients.

Hospitals often face difficulties in determining patient treatment priorities due to limited medical resources and the uncertainty of patient conditions. Conventional prioritization methods tend to rely on subjective judgment, which can lead to inconsistent decisions and delays in treatment. This study aims to apply fuzzy logic in a decision support system to determine patient priority levels more objectively and systematically. The proposed method utilizes a fuzzy inference system that processes several criteria, including the severity of symptoms, vital signs, patient age, and waiting time. These criteria are represented as fuzzy sets and evaluated using a set of inference rules to generate priority classifications. The results indicate that the fuzzy logic–based system is able to classify patient priorities more consistently and transparently compared to manual assessment. The system provides clear priority categories that can support medical staff in making faster and more accurate decisions. The findings imply that the implementation of fuzzy logic in hospital decision support systems can improve the quality of healthcare services, enhance fairness in patient handling, and optimize the allocation of medical resources, particularly in emergency and high-demand situations.

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.

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.

A wall follower robot is a type of autonomous robot that is designed to move by following a wall at a certain distance. This research aims to design and build a Wall follower robot equipped with a Fuzzy-PID control system to improve navigation performance. The robot uses five HC-SR04 ultrasonic sensors to detect the distance to the wall and the surrounding obstacles. The data from the sensor is then processed by a Fuzzy-PID algorithm that combines the advantages of conventional PID control with fuzzy logic, resulting in a more adaptive response to environmental conditions. The test results showed that the robot with Fuzzy-PID control was able to maintain the stability of the distance to the wall more consistently compared to the pure PID control. In addition, the system exhibits better adaptability to complex environmental conditions, such as sharp turns, uneven wall surfaces, and the presence of resistance variations. The application of Fuzzy-PID control has been shown to improve the stability, response speed, and accuracy of the robot's navigation. These findings are expected to contribute to the development of robotic navigation systems for a wide range of practical applications, including automated cleaning robots, environmental exploration, and industrial systems that require reliable autonomous mobility.

This study discusses the phenomenon of digital entrepreneurship in the era of global climate awareness, focusing on the integration of artificial intelligence (AI) ethics, sustainable technology, and green innovation. The main issues raised are the fragmentation of analysis between digital business ethics, green economic opportunities, and technological challenges such as greenwashing, high AI energy consumption, and the digital divide. The purpose of this study is to formulate an interdisciplinary framework that combines ethical, technological, and sustainability dimensions to strengthen the role of digital entrepreneurs in achieving low-carbon development. The methods used include critical literature analysis, bibliometrics of 200 publications (2018-2025) using VOSviewer, and fuzzy logic-based simulations using the UNESCO AI ethics framework (2021) and the sustainable business model of Bocken et al. (2014). The results show four main research clusters: AI for Sustainable Innovation, Ethical Digital Business, Blockchain for Green Supply Chain, and Circular Digital Economy. The application of AI ethics increases the efficiency of green business decisions by up to 20%, consumer trust by 17%, and MSME participation by 14%. The synthesis of findings confirms that AI ethics acts as a conceptual mediator that strengthens the link between technological innovation and sustainability. In conclusion, ethical digital entrepreneurship has great potential as a driving force for Indonesia's green economy, but it requires digital ethics audit policies and the adoption of low-carbon technologies to address ethical and environmental risks in the AI era.

Production quantity planning is a crucial component in the bottled water industry (AMDK) to ensure that consumer demand is met efficiently. Inaccuracies in determining the amount of production can lead to overproduction and supply shortages, which ultimately leads to increased operational costs and decreased customer satisfaction. This study applies the Sugeno fuzzy logic method to predict the amount of production based on two main variables, namely weekly demand and raw material stock. The analysis stages include the fuzzification process, the preparation of the rule base, inference using the zero-order Sugeno method, and defuzzification using the Weighted Average (WA) method. The data used is synthetic data that represents the operational conditions of the medium-scale bottled water industry. The results show that the Sugeno fuzzy system is able to produce production predictions that are adaptive and responsive to fluctuations in demand and stock availability. This model provides consistent and stable output, so it can help companies in determining the optimal production amount. These findings confirm that Sugino's fuzzy approach can be an effective decision support tool in bottled water production management, especially in the face of uncertainty and variability in market demand.

Pengelolaan air yang efisien merupakan aspek penting dalam mendukung kegiatan operasional di SMK Diponegoro. Tandon air yang selama ini dikelola secara manual sering menimbulkan kendala, seperti tandon meluap atau kosong akibat keterlambatan pengisian. Hal ini menyebabkan pemborosan air dan listrik serta meningkatkan beban kerja staf pengelola. Untuk mengatasi permasalahan tersebut, penelitian ini merancang sistem pendeteksi ketinggian air otomatis berbasis sensor ultrasonik dan Programmable Logic Controller (PLC). Sistem ini menggunakan sensor water level control untuk mendeteksi batas bawah dan atas ketinggian air, yang kemudian diolah oleh PLC untuk mengontrol pompa secara otomatis. Selain itu, data ketinggian air dikirim ke database dan ditampilkan pada web secara real-time. Metode logika fuzzy Tsukamoto digunakan dalam sistem kontrol untuk mengakomodasi ketidakpastian pembacaan sensor, dengan aturan berbasis bahasa alami seperti “air rendah”, “air sedang”, dan “air tinggi”. Hasil implementasi sistem ini diharapkan dapat meningkatkan efisiensi penggunaan air, mengurangi pemborosan, dan meringankan beban pengelola di lingkungan SMK Diponegoro.

Decision Support Systems (DSS) can become inaccurate when used with imprecise, incomplete, or dynamically changing data. Fuzzy logic techniques based on conventional methodologies may be strong at handling vagueness, but are unable to adapt their behavior in response to different data distributions on their own. This paper recommends the creation of an Adaptive Fuzzy Logic Integration Framework that dynamically updates membership functions and rule weights in response to data variation to enhance decision accuracy under uncertainty. The described framework combines Fuzzy Inference Systems (FIS) with learning-based parameter update concepts borrowed from adaptive optimisation. The model was simulated and executed on a hybrid algorithmic platform that included gradient-based parameter tuning and iterative feedback learning. Experimental tests were conducted on uncertainty-generated data sets to compare adaptive and conventional fuzzy models in terms of ISME (Root Mean Square Error), convergence stability, and decision accuracy. Previous results show that the adaptive model achieves a 21.4% increase in accuracy and a 28% improvement in convergence rate compared to non-adaptive fuzzy systems. Moreover, the model ensures stable performance even in the presence of random data perturbations, demonstrating its ability to handle uncertainty. This book incorporates a self-tuning fuzzy decision model that converts static inference structures to dynamic evolving decision engines. The outcomes establish a foundation for next-generation smart DSS for real-time optimization in uncertainty.

The DC motor serves as the main drive of the vessel and is equipped with a rotary encoder that functions to regulate the movement of the sensor in measuring sediment levels. This rotary encoder is also used to monitor and represent the rotational speed of the DC motor. System testing was carried out by implementing a Fuzzy Logic Controller (FLC) algorithm to control the DC motor speed in moving the vessel, ensuring stable motion. This fuzzy logic–based approach is expected to improve accuracy and efficiency in sediment volume calculations, while also reducing potential errors that commonly occur in manual methods. Simulating motor speed control using the fuzzy logic algorithm in MATLAB, the best test results were achieved over several trials, with a rise time of 376.310 ms and an overshoot of 83.33%. Motor speed measurements using both a tachometer and Arduino produced consistent results, with an average relative error of 0.18%.

Ensuring the delivery of high-quality public services within educational institutions is a critical factor for sustaining student satisfaction, institutional reputation, and overall organizational credibility. Service quality in higher education is often multidimensional and subjective, making its evaluation a challenging process. To address this complexity, the present study integrates the traditional Servqual model with fuzzy logic in order to handle the uncertainty and vagueness associated with human perceptions. The Servqual framework emphasizes five core dimensions of service quality, namely tangibles, reliability, responsiveness, assurance, and empathy, which together provide a comprehensive understanding of institutional service performance. The research was conducted at ITESA Muhammadiyah Semarang, where data collection was carried out through structured questionnaires designed to measure both student expectations and actual perceptions regarding institutional services. In order to capture the imprecise nature of these responses, three types of fuzzy numbers—triangular, trapezoidal, and shoulder fuzzy numbers—were applied to convert the linguistic assessments into measurable values. Subsequently, fuzzy gap analysis was performed to evaluate the discrepancy between expectations and perceptions across the five Servqual dimensions. The results of this analysis highlight that the integration of fuzzy logic into the Servqual model provides a more nuanced and flexible framework for assessing service quality compared to the conventional approach. It reduces the ambiguity in interpreting survey responses, thereby yielding more reliable insights into areas where service performance falls short of expectations. Furthermore, the study demonstrates that each dimension contributes differently to overall satisfaction, with responsiveness and assurance emerging as critical aspects requiring attention. In conclusion, the fuzzy-based Servqual model offers a robust methodological advancement in evaluating service quality within educational settings. The findings not only inform institutional leaders about current performance gaps but also provide actionable insights for continuous quality enhancement, policy formulation, and strategic decision-making to strengthen institutional competitiveness and credibility.

Employee performance appraisal is an important process in human resource management that aims to evaluate individual work achievements based on certain criteria set by the organization. This process not only serves to assess the extent to which an employee meets work standards, but also serves as a basis for strategic decision-making, such as job promotions, bonus awards, and career development planning. However, in practice, CV. Surya Perkasa Makassar faces serious obstacles in the form of subjectivity in the assessment process, because the benchmarks used still tend to be based on the likes or dislikes of superiors. This causes the evaluation results to be less objective, inconsistent, and potentially reduce employee work motivation. To overcome these problems, this study aims to develop a decision support system for employee performance appraisal using the Tsukamoto Fuzzy Logic method. This method was chosen because it is able to accommodate uncertainty in the assessment, resulting in more objective, measurable, and consistent decisions. This study uses a Research and Development (R&D) approach with a Black Box Testing method to ensure system functionality. The assessment criteria used include five main aspects, namely work quality, work quantity, discipline, responsibility, and cooperation. Data from these criteria is processed through fuzzification, inference, and defuzzification stages to obtain the final employee performance score. Test results indicate that all system features function as expected. The system is able to prevent data duplication, validate input, and produce accurate final performance scores. The implementation of the Tsukamoto Fuzzy Logic method has proven effective in reducing the level of subjectivity that typically occurs in manual assessments. Therefore, this system can be used as a reliable tool in managerial decision-making, both regarding promotions, bonus awards, and planning employee future career development.  

This research was conducted from June to July 2025 in Binjai City, with the primary focus being analyzing the readiness of the Binjai City Regional Disaster Management Agency (BPBD) to implement a flood early warning system utilizing artificial intelligence (AI). The data collection process was conducted through a literature review, which involved reviewing various theories and previous research results regarding the application of AI and Internet of Things (IoT) technology in the context of disaster mitigation. Based on the results of the study, it was found that the use of technologies such as ultrasonic sensors, microcontrollers, fuzzy logic, and automatic notification systems can provide real-time warnings with a high level of accuracy and a fast response. This system enables early detection of rising river levels through automatic measurements, intelligent data processing, and sending notifications to authorities and affected communities within seconds. By integrating historical data and machine learning-based predictions, this system is also able to depict potential flooding before it occurs, providing a longer response time for evacuation. However, the readiness of the Binjai City BPBD still faces various challenges, such as limited digital infrastructure, the need for human resource training in the technology field, and inadequate budget allocation. Therefore, cross-sector collaboration and ongoing policy support are needed for optimal implementation of this system. The use of AI and IoT in early warning systems is not only technically relevant but also urgent in the face of increasing climate change and flood risks. A strategy involving cross-sector collaboration between government, academia, and the private sector is needed to develop an adaptive and sustainable early warning system.

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%.

Blepharitis or inflammation of the eyelids is one of the common eye diseases, characterized by inflammation of the edges of the eyelids that can cause discomfort, irritation, and even visual disturbances. This disease can be chronic with recurrent symptoms such as red eyes, itching, watering, and the appearance of crust on the eyelashes. Proper and prompt diagnosis is necessary so that medical treatment can be carried out effectively and further complications can be prevented. This study aims to design and build an expert system based on the Fuzzy Logic method in helping diagnose blepharitis. The fuzzy method was chosen because it is able to handle the uncertainty of symptom data that often arises in the medical diagnosis process. This system is developed through the identification of the common symptoms of blepharitis, then processed using the fuzzy membership function to determine the type of disease based on the degree of symptom onset. The output of the system is in the form of the results of the diagnosis of blepharitis along with initial treatment recommendations that can be used as a reference for users. The results of the system test show that the application of fuzzy logic is able to provide diagnosis results that are quite accurate, fast, and easy to understand both medical personnel and the general public. This system is expected to help increase public awareness about the importance of early detection of blepharitis, as well as being a tool in the initial medical decision-making process. However, the limitations of this study lie in the limited amount of data and coverage of the type of blepharitis, so further development is needed, both in expanding the knowledge base, increasing the variety of symptoms, and improving system interaction with users.

Efficient resource allocation is a major challenge in various sectors, especially when faced with limitations in quantity, time, and cost. This study aims to examine the application of linear programming as an optimization method in solving assignment problems, where a number of resources must be optimally allocated to a number of specific tasks. Through a literature study approach, this study examines various relevant previous study results, especially in the Indonesian context. The results of the study indicate that linear programming is able to improve operational efficiency, reduce costs, and produce a more balanced task distribution. However, this model has limitations in dealing with non-linear conditions and data uncertainty, so integration with other methods such as fuzzy logic or genetic algorithms is needed. This study is expected to broaden the understanding of the benefits of linear programming and encourage its wider application in quantitative-based decision making.

This study aims to design a smart home system leveraging the Internet of Things (IoT) concept by utilizing the Wemos D1 microcontroller combined with fuzzy logic to improve home energy management and safety. The hardware development process involves integrating a PIR sensor for motion detection, a DHT11 sensor for measuring temperature and humidity, and actuators including relays for lighting and a solenoid lock for doors. The system is operated remotely using the Blynk platform and supports notification alerts through Telegram. Testing results confirm that the prototype is capable of controlling devices, tracking environmental data in real time, and effectively sending alerts when movement is detected. Overall, the system presents a practical, responsive, and user-friendly smart home solution that enhances user convenience and household security.