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Emotional mental health in adolescents is an important focus because this age group is highly vulnerable to academic and social pressures, as well as the demands of personal development. Emotional problems such as anxiety, stress, and depression that are not recognized early on can interfere with concentration, social relationships, and psychological well-being. This study aims to review the literature related to the early detection of emotional mental disorders in adolescents, so that it can serve as a basis for the development of prevention and intervention strategies. The method used is a literature review, analyzing a number of recent research articles discussing risk factors, symptoms, and early screening methods using instruments such as the Self Reporting Questionnaire (SRQ) and the Strengths and Difficulties Questionnaire (SDQ). The results of the study show that the most common symptoms include anxiety, fatigue, concentration disorders, and relationship problems with peers in adolescents, as well as academic pressure in students. Early screening has been proven to help identify individuals at risk so that intervention can be carried out more quickly. The use of technology, such as web-based expert systems, can also speed up the screening process and improve the accuracy of assessments. It is recommended that educational institutions and health services be more active in conducting mental health screening, providing promotional education, and designing preventive programs. Further research should use analytical or experimental designs to explore risk factors and the effectiveness of interventions in greater depth.

This study aims to design and implement a damage analysis system for concrete surfaces by utilizing digital image processing based on the Canny edge detection method. The developed system allows users to upload images of concrete surfaces, which are then processed through several stages: conversion to grayscale, transformation to binary images, and crack edge detection using the Canny operator. This process aims to automatically detect crack patterns on the concrete surface. The detection results, represented as edge lines, are used to calculate the percentage of the damaged area. Based on this percentage value, the system automatically classifies the damage level into light, moderate, or severe categories. System testing shows that the Canny method can accurately identify crack patterns, with sufficient detection levels to be used in monitoring the condition of concrete surfaces. The analysis results are then presented in both visual and numerical forms, providing valuable information for assessing the structural condition of concrete. Thus, this system can serve as an efficient and effective tool for early detection of structural damage in concrete infrastructure, ultimately supporting better maintenance and repair efforts.

Rapid technological advancements have brought convenience to various fields, including healthcare. Osteoarthritis (OA) is a chronic degenerative joint disease that often affects the knees and hips, particularly in the elderly, and is a major cause of pain, joint dysfunction, and reduced quality of life. The prevalence of OA increases with age, with risk factors such as obesity, excessive activity, and muscle weakness. Early and accurate diagnosis is essential for appropriate treatment. This study aims to develop a diagnostic system for inflammatory arthritis, specifically osteoarthritis, using the Dempster-Shafer method. This method was chosen because of its ability to combine various evidence and expert beliefs to produce a more accurate diagnosis. By utilizing mathematical proof theory, this system is expected to assist medical personnel in detecting OA symptoms more efficiently. The research findings are expected to contribute to the healthcare sector, particularly in improving the accuracy of osteoarthritis diagnosis, allowing for earlier and more appropriate treatment. This system can also be a supporting tool for doctors and patients in understanding joint health conditions.

Handling natural disaster victims requires a fast, precise, and fair aid distribution process. In this context, expert systems can be utilized as a decision-making tool in determining the type and amount of aid that should be given to victims. This article develops a desktop-based expert system using the Java programming language, which is able to calculate the type of aid based on the condition of the victim, the level of damage, and the number of affected family members. The method used is a rule-based expert system with if-then logic. The results show that this system can assist field officers in accelerating the calculation and distribution of aid.

This study discusses the analysis of parental assessment and the usefulness of expert systems in detecting child behavioral disorders using the system usability method scales (SUS). This system is designed to help parents identify behavioral disorders in children efficiently and accurately. The research method includes compiling a questionnaire, collecting data, processing SUS scores, interpreting results, and recommending improvements. The results of this study indicate that this system gets an average SUS score of 82.84, which is included in the Grade A category. This shows a high level of acceptance and good user experience, where users do not experience difficulty in operating the system. In addition, this system managed to maintain its search performance, but improvements are needed in some of its services according to suggestions from respondents.

Hypertension is a disease that occurs to arteries that causes the supply of oxygen and nutrition that the body needs to be blocked. Hypertension is often called a silent killer, because it is a kind of disease that is very harmful but comes without awareness to its victim. People with hypertension in average are up to 40 years old and it happened all of his after life . In common hypertension caused by heredity, unhealthy lifestyle, and triggered by the more salty consumption, alcohol and stress. An expert system could be the solution to solve the problem because this system works just like an expert and was created by the naïve Bayes method with the rules and basic system that are the same just like the hyperantion desease. Through this application, users can consult with this system just like usually people consult with the expert to diagnose the sign that happened to the user and find the solution of what happened to themselves.

Health is important for every human being. Health, education and income of each individual are three important factors that greatly influence the quality of human resources. Anxiety disorders are a significant mental health problem and can affect an individual's quality of life. Early detection of anxiety disorders is important to provide appropriate intervention and prevent the development of more serious conditions. This research aims to develop an expert system that is able to detect anxiety disorders based on symptoms reported by penggunas. This system uses a forward chaining method and a knowledge base compiled from medical literature and consultations with mental health experts. Several stages of system creation include collecting data on symptoms of anxiety disorders, preparing a knowledge base, implementing a forward chaining inference algorithm, and kuatating the system using test data and expert consultation. The expert system developed in this research is able to provide accurate initial information regarding the symptoms of anxiety disorders in adolescents based on the symptoms input by the pengguna. By utilizing a knowledge base and appropriate diagnostic rules, the system can identify key symptoms that indicate the presence of an anxiety disorder.

The integration of IT Governance and expert system design offers transformative benefits for enhancing library user services. This research employs the COBIT 5.0 framework to align IT strategies with library objectives while developing an expert system tailored for personalized recommendations. The findings indicate that the expert system significantly improves operational efficiency, service accuracy, and user satisfaction by using user profiles to recommend relevant materials and streamline the borrowing process. Testing revealed high user satisfaction levels, with 96.6% finding the system effective and 100% confirming its efficiency. Additionally, IT Governance ensures strategic integration between technological infrastructure and service quality objectives, enabling data-driven decision-making. The study also highlights challenges, such as the need for robust data management and user training, suggesting areas for future improvement. Recommendations include incorporating machine learning to enhance system intelligence, conducting regular evaluations to maintain system relevance, and testing the scalability of this approach across various types of libraries. By integrating IT Governance with an expert system, this research sets a strong foundation for modernizing library services to better meet user expectations in the digital era.

Collaboration of computer science disciplines with other disciplines has been widely carried out, for example, medical science. Expert systems for disease diagnosis are one of the many computer programs used by doctors to assist them in their work and provide good results. Researchers in the field of artificial intelligence are working to improve existing systems to cover their shortcomings. Expert systems are used in many applications for disease diagnosis. Expert systems have been used in various industries and have had a significant impact. An innovative method for diagnosing diseases is the application of case-based reasoning systems, compared to expert systems. The solution to diagnosing and treating diseases can be found in the expert system for diagnosing pulmonary tuberculosis. Because this web-based system is based on a web application, all pulmonary tuberculosis patients can access it. A rule-based system that applies the CBR method can identify various types of diseases through the use of weighting techniques and offer treatment recommendations.

Expert systems are one type of computer technology that is being used by a used in the medical field to assist physicians in patient examinations is expert systems.The goal of this is to improve patient care both now and in the future.system with expertise  is an application that replicates how an expert would reason to solve a particular problem or acts similarly to an expert due to its understanding of a knowledge base that has to be processed and its ability to solve problems. An expert system's diagnosis of epilepsy leads to the creation of a system that can offer individuals with the condition a consultation service for the purpose of diagnosing the condition and providing information on treatment options. This is demonstrated by the development of numerous technologies that facilitate the work of numerous parties. One of them is computer-related and uses Expert System Science to assist in the diagnosis of epilepsy. The Certainty Factor approach is employed in this study. Thirteen symptoms and three different forms of epilepsy—general, partial, and secondary—were used in this investigation. The study's findings indicate that, based on the chosen symptoms, the most accurate diagnosis is Partial Primary, Partial Secondary, with a confidence level of 74%, and the most accurate diagnosis is Generalized Epilepsy, with a confidence level of 99%.    

Obsessive-Compulsive Disorder (OCD) is a psychiatric disorder characterized by uncontrollable obsessive thoughts and compulsive behaviors. The disorder triggers anxiety in sufferers that often drives them to avoid situations or places that can trigger obsessions, such as shaking hands or using public restrooms. Proper treatment is necessary to prevent further impact on the quality of life of OCD sufferers. However, early diagnosis is often constrained by limited time and access to medical experts. To overcome this, an expert system based on the Certainty Factor method was developed. This system mimics the thought process of a medical expert in diagnosing OCD using symptoms selected by the user. Certainty Factor is used to calculate the certainty level of each diagnosis based on the inputted symptoms. From the analysis, the system is able to provide diagnoses with high accuracy, even reaching 100% for some OCD cases. These results show that expert systems can be an effective tool in detecting OCD early, thus accelerating the process of proper handling and treatment

Vulvodynia is a chronic pain condition affecting the vulva that significantly impacts women’s quality of life. Accurate and early diagnosis poses a challenge due to the often-overlapping symptoms with other conditions and the lack of definitive diagnostic tests. This paper proposes the use of expert system methods as a diagnostic tool for vulvodynia in women. The expert system, integrating medical knowledge with inference algorithms, is designed to analyze symptoms, medical history, and test results to provide accurate diagnoses and treatment recommendations. The study involves the development and evaluation of a computer-based expert system prototype that uses clinical data and medical decision-making to enhance the accuracy of vulvodynia diagnosis. Preliminary results indicate that the expert system can improve diagnostic rates and reduce the time required for identifying this condition, offering a potentially valuable tool for medical professionals in clinical practice.  

Tuberculosis (TB) is one of the infectious diseases caused by Mycobacterium tuberculosis bacteria infection in the human lungs. Tuberculosis is a disease that can be transmitted from people with TB through coughing, sneezing, talking, laughing or singing. Lack of public knowledge about TB and lack of funds for health checks make many people late to be treated. Expert systems are technologies developed based on programs, in accordance with human methods and mindsets. This aims to help people who want to check their health, but are hampered by costs, besides saving time if the examination place is far from the residential environment of the community concerned. Expert systems require a method that can help solve existing problems. In this study, the method used is the Case-Based Reasoning (CBR) method, because the main function of this method is to diagnose the disease. The calculation process of the Case-Based Reasoning (CBR) method which looks for the similarity value or proximity of old cases to new cases of a patient.

In Indonesia, people keep a lot of animals, such as dogs. Dogs are one of the favorite animals commonly kept by humans. Dogs are usually favorite animals that can be used as companions, guarding the house, hunting and so on. Alvi Deiectiones (Diarrhea) disease is caused by eating food errors (such as eating leftover or stale food), changing types of food without a transition period, viral, bacterial, or parasitic infections.One of the problems often encountered at the Binjai Petshop clinic is diarrheal disease in dogs. The lack of knowledge in people who keep dogs about diarrhea disease and the limited existence of veterinary clinics, time and money make many cases of improper treatment that worsen the dog's condition. To help overcome this problem is to create an Expert System that can diagnose diseases based on the symptoms that occur to detect the disease experienced. Expert System can be interpreted as a collection of facts that have been collected to show the symptoms of a disease and can provide an explanation based on consultation with the expert.

The rapid development of information technology affects the way people access information, including in the health sector. Prostate cancer, as one of the most significant types of cancer in men, is often detected late due to lack of information and limited costs. To overcome this problem, a system is needed that is able to diagnose prostate cancer quickly, precisely, and accurately. This study aims to develop a web-based expert system using the Certainty Factor (CF) method to diagnose prostate cancer based on the symptoms that appear. The CF method was chosen because of its ability to determine the level of confidence in the facts or rules used in the diagnosis. This study uses data on symptoms and types of prostate cancer. The results of the study can help the public in recognizing prostate cancer symptoms early, with a high level of accuracy in diagnosis. This study is expected to make it easier for patients to make an early diagnosis and accelerate the treatment of prostate cancer.

Determining majors is one of the obligations for all students who still want to continue their education. Decisions taken often cause problems when differences in abilities and limitations in thinking result in not graduating on time. Also, we often see majors chosen only because they follow the advice of friends or family and because of the development of the need for human resources in the world of work. Determination of majors should be done by considering the capabilities possessed. Therefore, this application was created which aims to design an expert system that can provide an overview for prospective students about what majors are suitable or in accordance with their abilities. The method used in making this expert system is Forward Chaining, where the facts related to determining the direction are stated first, then draw an appropriate conclusion. By making this application, it is expected to provide convenience for prospective students both in terms of time and cost.