Publication Search

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.

E-Tendering is an electronic-based procurement method that integrates all parties involved in construction projects through a digital platform, simplifying communication, document exchange, and transactions from the initial stage to project completion. The primary objective of implementing E-Tendering in the construction sector is to improve efficiency and transparency in the procurement process of goods and services. However, construction service providers often face obstacles in E-Tendering, such as limited technological proficiency, administrative errors that may disqualify bids, and technical issues like internet connectivity and platform system reliability. Therefore, this research aims to identify the barriers and level of understanding of E-Tendering among construction service providers in Surabaya. A total of 51 respondents were selected using snowball sampling, based on their participation in the E-Tendering system. Data processing in this study employed a quantitative approach using the Fuzzy AHP method to analyze the criteria and sub-criteria related to understanding and obstacles in participating in electronic construction service tenders (E-Tendering). The results of the analysis of service providers’ understanding of the E-Tendering system using the AHP method showed a λmax value of 9.09, a Consistency Index (CI) of 0.01, and a Consistency Ratio (CR) of 0.007. The CR value, which is far below the threshold of 0.10, indicates that the respondents’ assessments are consistent, meaning that the pairwise comparison results of the nine criteria can be considered valid and reliable.

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%.

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.

Academic planning is one form of planning the teaching and learning process in state universities, aimed at achieving educational goals based on the standards set. One important aspect of academic planning is forecasting the number of new students. This study compares two forecasting methods, Fuzzy Time Series (FTS) and Autoregressive Integrated Moving Average (ARIMA), in predicting the number of new students in the Statistics Study Program at Universitas Negeri Gorontalo. Forecasting the number of new students is crucial for determining various policies, such as resource allocation and providing adequate facilities. The results of the study show that the ARIMA method produces more accurate predictions with a Mean Absolute Percentage Error (MAPE) of 0.35%, which is lower than the FTS method. This indicates that ARIMA is more effective in predicting the number of new students in the Statistics Study Program at Universitas Negeri Gorontalo and can serve as a reference to improve academic planning quality in higher education institutions.

The implementation of risk management is an important method that is carried out in order to identify risk factors that may arise during the implementation of the project. However, the implementation of risk management still faces some obstacles in its implementation. Therefore, this study aims to analyze the barriers to the implementation of risk management in construction projects in Surabaya. A total of 80 respondents filled out questionnaires from construction service providers including contractors and consultants. Respondents involved include professional experts such as Project Managers, Site engineers, Site managers, implementers, estimators, General Managers and Company Directors. Data processing using fuzzy AHP method as a data processing tool and decision making. The results of the study revealed that the main factors that can hinder the implementation of cost risk management are inaccurate cost estimates (Y4) with the highest weight of 0.433, lack of quality Control (Qc) supervision criteria (Y5) is ranked second with a weight of 0.288, poor coordination between stakeholders (owner,contractor and consultant) (Y1) is ranked third with a weight of 0.274, lack of risk management training (Y3) is ranked fourth with a weight of 0.005, and some, old age) (Y2), the work can not be done according to the work drawings (Y6), limited skilled human resources (Y7), materials not according to specifications (Y8), improper initial cost estimation (Y9), late progressive payment from the owner (Y10) ranked fifth jointly because it has an equivalent weight value of 0.These findings conclude that accurate cost estimation (Y4) is very important in construction projects because it becomes the main basis in budget planning, decision making, and risk management.

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.  

Wireless Medical Sensor Networks (WMSNs) are a key component of modern Healthcare Internet of Things (IoT) systems, enabling continuous and real-time monitoring of patients’ physiological parameters. These networks support timely medical intervention, improve patient outcomes, and facilitate remote healthcare delivery. However, due to the open and resource-constrained nature of WMSNs, they are highly susceptible to various security threats, particularly during the authentication phase. Existing authentication protocols have been found vulnerable to a range of attacks, including impersonation, session key disclosure, and gateway database compromise, which can lead to severe privacy breaches and potentially life-threatening situations. To address these issues, this paper proposes a secure and lightweight three-factor authentication protocol tailored for WMSNs in healthcare IoT environments. The proposed protocol integrates Elliptic Curve Cryptography (ECC) for strong public key-based security with minimal computational overhead, fuzzy extractors to securely handle biometric information and ensure resistance against biometric template compromise, and session-based randomness to achieve forward secrecy and prevent replay or key-compromise impersonation attacks. Security analysis demonstrates that the proposed protocol successfully mitigates prominent threats such as impersonation attacks, man-in-the-middle attacks, session key leakage, and database compromise. In addition, the protocol ensures mutual authentication between the user, the gateway, and the sensor nodes, while maintaining data confidentiality and integrity. Performance evaluation indicates that the protocol offers significantly reduced computational cost and communication delay compared to existing schemes. Its low energy consumption and minimal storage requirements make it suitable for deployment in resource-constrained medical devices and large-scale IoT healthcare networks. The results highlight the protocol’s scalability, energy efficiency, and robustness, making it a practical and secure solution for safeguarding patient data and ensuring trustworthy communication in WMSNs-based healthcare IoT systems.

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.

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.

Digital image segmentation is essential in image processing, influencing the accuracy of higher-level tasks. Thresholding is widely used, yet identifying optimal threshold values remains challenging. The Firefly Algorithm with Neighbourhood Attraction (FaNA), a metaheuristic approach, is efficient for color image thresholding but underperforms on grayscale images due to suboptimal thresholds. To overcome this, an enhanced version (eFaNA) was developed by integrating a chaotic tent map for population initialization and a Lévy flight-based random walk for improved exploration. eFaNA was compared with FaNA, fuzzy firefly algorithm (FFA), and the standard Firefly Algorithm (FA) in multilevel thresholding of grayscale images. Results demonstrate that eFaNA achieves superior segmentation quality with minimal detail loss, outperforming the others. The average PSNR obtained by eFaNA, FFA, FaNA, and FA was 25.5320 dB, 25.4075 dB, 24.1522 dB, and 24.4506 dB, respectively; average SSIM was 0.8641, 0.8604, 0.8432, and 0.6703; and execution time was 50.5322, 38.7726, 38.7528, and 107.6340 seconds, respectively. This reflects a PSNR improvement of 5.71% over FaNA, 0.49% over FFA, and 4.42% over FA, and an SSIM gain of 2.48% over FaNA, 0.43% over FFA, and 28.92% over FA. While eFaNA lags behind FFA and FaNA in execution time by ~11.8 seconds, it significantly outperforms FA. The performance gain is attributed to the chaotic tent map’s diverse initialization and the Lévy flight’s enhanced search capability. These improvements enable eFaNA to deliver consistently better threshold values and segmentation results. However, its relatively higher computational cost may limit applicability in real-time image processing.

The development of technology in the field of fisheries is very rapid along with facilitating human work in the field. Human work can be done by tools such as feeding catfish in ponds automatically. This job is easy to do, but problems will arise when catfish are not fed at night or are fed too late, then the catfish will eat smaller catfish so that it can be detrimental to catfish farming owners. With this background, the author created a "Catfish Feeding Control System Based on the Internet of Things (IoT) Using the Fuzzy Sugeno Method". This testing and discussion was carried out by functionally testing each component, testing the program using simulation and testing it as a whole. The results of testing the tool and taking data are expected to obtain valid data and the tool works according to its function and purpose as expected. From the results of the analysis and discussion of the research entitled "Internet Of Things (IoT) Based Catfish Feeding Control System Using the Fuzzy Sugeno Method" it can be concluded as a process of automatic fish feeding using the RTC module as a reference for catfish time three times a day with good results and the process of calculating the amount of fish feed 3 to 8% with fish weight from LOW to HIGH using Fuzzy Sugeno has worked well compared to the results of the BLYNK application.

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.

This research deals with a comparison of Kriging's method in predicting ordinary and fuzzy data in order to know the best method for future studies. Ordinary data was used in the field of depth of groundwater wells for 37 locations in Kirkuk city, and these data were fuzzy into fuzzy numbers of the trigonometric type that have a function of belonging, Then the centroid of each fuzzy number was found for the purpose of facilitating the calculations. An unknown point was predicted for both the ordinary and fuzzy data. After comparing the results with a standard of least variance, it was found that the fuzzy data had better results than the ordinary data.