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Typhoid fever is a significant health issue caused by the Salmonella Typhi bacteria, leading to symptoms such as fever, abdominal pain, diarrhea, muscle pain, and serious complications if not treated promptly. A common challenge faced by society is limited access to medical professionals, especially in remote areas, and delays in recognizing symptoms. To address this problem, this study designs and implements a web-based expert system using the Certainty Factor (CF) method, which helps diagnose typhoid fever quickly and accurately. The Certainty Factor method is used to calculate the certainty level of the symptoms experienced by the patient, providing a diagnosis result in the form of early-stage typhoid, mild typhoid, or severe typhoid. The system was developed using PHP programming language and MySQL database, and tested at RSUD Djoelham Binjai City. The research data was obtained from patients at RSUD Djoelham Binjai with a case study on patient number 22. The processing of symptoms through Certainty Factor calculation showed that the patient is most likely to have severe typhoid with a certainty value of 0.9443 or 94.43%. This result proves that the Certainty Factor method can be used to assist in providing an accurate early diagnosis of typhoid fever with a high degree of accuracy.

The Computer-Based National Assessment (ANBK) is an essential instrument designed to comprehensively measure student competence, including literacy, numeracy, and character aspects. However, in practice, many students still face various challenges during preparation, such as cognitive limitations, psychological readiness, and technical barriers, which affect their overall readiness to participate in ANBK. This study aims to analyze the readiness level of students at SMP Negeri 2 Kuala by employing the Rough Set method. The variables examined include digital literacy, subject matter understanding, psychological readiness, and school facility support. Data were collected from 250 ninth-grade students through structured questionnaires and subsequently processed using the Rosetta software to perform attribute reduction and generate decision rules. The findings indicate that digital literacy, subject matter understanding, and psychological readiness are the most influential variables in determining student readiness, while facility support serves only as a complementary factor. The extraction process generated seven decision rules with an accuracy level of 100%, which effectively classified students into three readiness categories: highly ready, ready, and less ready. These results confirm that the Rough Set method is highly effective for identifying dominant factors and producing decision rules that can guide schools in developing targeted strategies to enhance student readiness for ANBK.

Preeclampsia is one of the most serious complications in pregnancy, characterized by hypertension and proteinuria, and it poses a significant risk of maternal and fetal morbidity and mortality if not detected and managed promptly. Early detection is crucial, yet clinical diagnosis often faces challenges due to the variability of symptoms and uncertainty in medical decision-making. To address this issue, this study aims to develop an expert system for diagnosing preeclampsia by employing the Dempster-Shafer method, which is known for its ability to handle uncertainty and incomplete information in complex domains such as healthcare. A case study was conducted at Bidadari General Hospital, where data on clinical symptoms and patient medical records were collected and analyzed. The development process of the expert system followed systematic stages, including knowledge acquisition from obstetrics specialists, designing the knowledge base, constructing inference rules, and integrating the Dempster-Shafer algorithm for decision support. The system was subsequently tested using real-case scenarios of pregnant women suspected of having preeclampsia. Evaluation results demonstrated that the system achieved an accuracy rate of 92% in differentiating between preeclampsia and eclampsia, based on belief and plausibility measures combined with symptom analysis. These findings indicate that the proposed system can effectively support medical personnel by providing diagnostic recommendations with a high degree of reliability. In addition, the system offers efficiency in the clinical workflow by minimizing diagnostic errors and reducing delays in treatment initiation. Therefore, this expert system has the potential to become a valuable clinical decision support tool for early detection, risk assessment, and management of preeclampsia. Future development may focus on expanding the knowledge base, integrating real-time patient monitoring data, and enhancing usability to ensure broader applicability in diverse healthcare settings.

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.

The transition of children from early childhood education to elementary school (SD) is a critical phase in their psychological and academic development. During this phase, children face significant challenges, including changes to a more structured learning environment and increasing academic demands. At SDN 055991 in Langkat Regency, this phenomenon is reflected in the difficulties experienced by some students, particularly with basic skills such as reading, writing, and arithmetic, as well as with socializing with peers. These difficulties can impact children's long-term academic and social development. This study aims to identify the key factors influencing children's learning readiness during this transition period, utilizing artificial intelligence (AI) technology. Specifically, this study uses Artificial Neural Networks (ANN) and Decision Trees as tools to analyze the data obtained. The use of this data-driven approach allows for a more in-depth analysis of the complex patterns and relationships between various variables that influence children's learning readiness, such as family factors, social environment, and students' basic skills. This study also references various previous studies demonstrating the effectiveness of backpropagation and Deep Learning algorithms in the context of education and student performance prediction. This approach is expected to provide more precise solutions for understanding children's learning readiness and provide a more accurate picture of the factors contributing to difficulties experienced by students in the transition to elementary school. The results of this study are expected to provide relevant recommendations for parents, educators, and education policymakers to support children's learning readiness and strengthen basic education policies that are adaptive to the needs of students in this digital era.

This study aims to analyze the correlation between learning models and student achievement at SMA Negeri 1 Kuala by applying the Apriori algorithm in data mining, using Rapid Miner software as the primary tool for analysis. The research is motivated by the shift in educational approaches from conventional teacher-centered methods toward more innovative strategies such as project-based learning and cooperative learning, which are expected to foster higher levels of student engagement and improve academic outcomes. In many schools, particularly at the secondary level, the choice of learning model, availability of facilities, and attendance rates are crucial factors that shape learning effectiveness and student performance. The data collected in this study include student grades, the types of learning models implemented, school facility conditions, and attendance rates for the 2023/2024 academic year, covering a total of 680 students. The Apriori algorithm was employed to discover hidden patterns and associations among these variables, enabling the identification of relationships between learning factors and academic achievement. By applying Rapid Miner software, the research systematically generated association rules that reflect meaningful correlations in the dataset. The results indicated that the use of the Indonesian language subject in combination with a cooperative learning model, adequate and complete school facilities, and good student attendance was strongly associated with the attainment of an A grade. This finding was supported by a support level of 53.33% and a confidence level of 100%, suggesting a robust and reliable relationship between these factors. The implementation of data mining techniques through Rapid Miner not only allowed for efficient data processing but also provided practical recommendations for educators and school administrators in designing effective instructional strategies.

Durian is one of the leading fruit commodities with high economic value in various tropical regions, including Indonesia. However, the durian cultivation process often faces challenges related to unstable environmental conditions, such as temperature fluctuations, soil moisture, and nutritional deficiencies which can affect the level of plant fertility and the quality of the fruit produced. Therefore, an effective and efficient monitoring system is needed to optimize durian plant care. This research aims to develop a fertility monitoring system for durian trees using Internet of Things (IoT) technology which can help farmers manage the environmental conditions of plants in real-time. The designed system uses various sensors, such as soil moisture sensors, air temperature and humidity sensors, light intensity sensors, and soil nutrient sensors, to collect relevant environmental data. The data obtained from these sensors is then processed by a microcontroller and sent via the IoT network to a cloud-based storage platform. The trial results show that this system can monitor environmental conditions with high accuracy and provide appropriate maintenance recommendations, thereby increasing efficiency in managing durian plants.

This research aims to design and build a tool for detecting and repelling pests on kale plants using Passive Infrared (PIR) sensors and Internet of Things (IoT) based pesticide liquid which can be controlled via the Telegram application. This system works by detecting the movement of pests using a PIR sensor, which then activates the mechanism for automatically spraying pesticide liquid to repel pests. Users can monitor and control this tool in real-time via Telegram, providing flexibility in plant monitoring. Data from sensors is sent to the IoT platform and integrated with Telegram to provide remote notifications and control. Test results show that this tool can detect the presence of pests with high accuracy and is effective in repelling pests from the kale plant area. It is hoped that the implementation of this tool can help farmers maintain the quality and quantity of their kale harvest in a more efficient and environmentally friendly manner.

The increasing complexity of public demand for telecommunication services, particularly internet services, has pushed PT. Telkom, as one of the state-owned enterprises (SOEs), to continuously enhance the quality of its services. One of its flagship products, Indihome, offers faster internet connectivity compared to dial-up services. However, Indihome has been frequently criticized by customers due to service disruptions. This indicates a need for developing effective strategies to address customer complaints. The primary issue faced by the public is the lack of knowledge regarding service disruptions, leading to difficulties in explaining the problem to technicians for repair. This research aims to develop a Telkom service disruption diagnosis system that can assist the public in identifying issues early without direct consultation with an expert. The system is developed using an expert system method, where information about service disruptions is processed to generate accurate diagnoses. With this system, customers can identify the type of disruption and provide clearer information to Telkom technicians. The research findings indicate that the most common disruptions are caused by faulty adapters or modems and disconnected configurations, with a density value of 54.49%. This system is expected to improve Telkom’s public service quality, minimize customer complaints, and expedite the repair process for Indihome services.  

: Oyster mushrooms are one of the popular horticultural products in the community, with a significant increase in demand from year to year. However, farmers often face difficulties in identifying and preventing diseases that attack oyster mushroom plants, which have an impact on production stability. To overcome this problem, this study aims to design an expert system that can diagnose diseases in white oyster mushrooms using the Dempster Shafer Method. This system is designed to provide accurate diagnostic information and solutions to deal with diseases in oyster mushrooms, so as to improve the quality and quantity of production. This study also strengthens previous studies using the Certainty Factor method, with an emphasis on the use of expert knowledge to overcome disease problems in oyster mushroom cultivation. The case study was conducted at Omah Jamur Tiram Stabat, where the results of the implementation are expected to increase the effectiveness and efficiency of oyster mushroom cultivation in the area.

Healthy lifestyles are habits of doing something, be it food, healthy behavior so as to avoid the disturbance of all kinds of diseases, both physical and non-physical diseases, as well as birth control users must also strive for a healthy lifestyle, such as managing a healthy diet, rest, exercise, eating vegetables and fruits, doing optimal physical activity, not consuming alcohol, and maintaining a healthy body. In this problem, many family planning users do not pay attention to a healthy lifestyle because they think that the family planning tools used have no risk to health, but the use of family planning has side effects on health such as menstruation is not smooth, the body is obese, the body feels warm or feverish, there are blood clots, nausea, bloating, changes in vision, difficulty in getting back to normal, headaches, and others. To be able to attract the attention of the community in implementing a healthy lifestyle for family planning users, it is very necessary to have a system that can help people in changing their unhealthy lifestyle to a healthier one by grouping family planning user data based on variables that have been determined using the clustering method, to group data on healthy lifestyles for family planning users which later the results of this study can be used as input and guidance for a healthy lifestyle for family planning users, so that family planning users are more careful and have a healthy life. Of the 20 data, there are 3 groups, namely group 1 there are 4 data and group 2 there are 4 data and group 3 there are 12 data from the above results it can be seen that in cluster 3 is a group on family planning users based on a lot with a total of 12 data and is located in the contraceptive type group (X) is injectable birth control, and for the lifestyle group (Y), namely Frequent Night Baths and Risk (Z), namely Decreased Bone Strength.  

In Indonesia, people with disabilities are often overlooked and underestimated because they do not have perfect physical abilities to do certain jobs or activities. The majority of them come from underprivileged families and are often underdeveloped. The unstructured process of distributing assistance can result in the assistance provided is not in accordance with the needs, so it is not optimal in improving the welfare of persons with disabilities. In addition, without a clear grouping, it is difficult for the government to design a more specific and targeted assistance program. Therefore, to overcome this problem, the agency needs to have an additional system to be able to assist in overcoming the problem of disability assistance recipients, namely by using the clustering method to group beneficiary data based on age, type of disability, and type of assistance. Thus, this clustering is expected to provide information and a clearer picture of the needs of each disability group, so that the assistance program provided can be distributed more optimally according to what people with disabilities need. After calculating using the existing cluster formula4, iteration 2 is the same as in iteration 1 and there is no data that moves groups anymore so the calculation can be stopped. So that a cluster graph can be made grouping data on beneficiaries of assistance for disabilities in Binjai City using the K-Means algorithm clustering method.

Nasal hypertrophy is a swelling that occurs in the nasal concha. This condition is caused because the inferior concha has a larger anatomical size when compared to the other concha structures. The process of diagnosing nasal hypertrophy often requires high clinical skills and experience. RSU Putri Bidadari is one of the hospitals that treats Nasal Hypertrophy disease in patients. Nose hypertrophy disease has several symptoms that are felt which are usually caused by several factors such as exposure to certain allergens, chronic sinus infections, or a family history of similar nasal problems, so several diagnostic tests are needed that can confirm the diagnosis, such as nasal endoscopy to see directly the condition inside the nose, medical imaging such as CT scan or MRI to evaluate the structure of the nose in more detail, or allergy tests to identify the causative allergen. From the above problems, patients really need a system that becomes a recommendation in helping provide information about nasal hypertrophy disease that can diagnose early and take further action to prevent nasal hypertrophy disease. By using the certainty factor method, information from the steps above can be systematically analyzed to determine the level of confidence in the diagnosis of nasal hypertrophy. These factors can be assessed based on severity, presence of typical symptoms, correlation with risk factors, and results of physical examination and diagnostic tests. Based on the results of the CF calculation, the highest value is in the type of nasal hypertrophy disease with the type of Septal Deviation disease having a value of 1 or 100%, in the type of Rhinitis disease having a value of 94.24% and in the type of sinusitis disease having a value of 85.60%. From the results obtained, the system identifies that the patient has nasal hypertrophy with Septal Deviation type by 100%.  

Soft skills are non-technical abilities that make a person able to interact and work effectively with others. This study aims to analyze the relationship between student activities of Internships and Certified Independent Study (MSIB) on improving student soft skills using the Apriori method in data mining analysis. this research uses RapidMiner analysis tools to analyze data collected from a total of 539 student data from all over Indonesia, the best association rule has been formed (best rule) which provides information about improving the soft skills of MSIB students. Tests were conducted by determining the minimum support value of 3% (0.03) and the minimum confidence of 30% (0.3). and resulted in 106 association rules. Based on the results of the analysis, it was found that the best rule of 2 itemsets has a support of 39% and a confidence of 67%, the best rule of 3 itemsets has a support of 13% and a confidence of 81%, the best rule of 4 itemsets has a support of 6% and a confidence of 82%, and the best rule of 5 itemsets has a support of 3% and a confidence of 100%.  After analyzing data using the Apriori method and RapidMiner application on 539 MSIB student soft skills data, it was found that there was a significant relationship between MBKM activities followed by students and the improvement of their soft skills and these findings also show that the less frequent value is set, the more data can be processed, as well as the minimum support value and confidence value, where the smaller the value determined, the more association results will be issued.

Cats are one of the beloved animals that require care and development. As beloved animals, cats have their own charm thanks to their different body shapes, eyes and fur colors. Cats whose pedigrees are officially registered as purebred cats. Cats, in Latin Felis silvestris catus, are a type of carnivore. The word cat usually means a domesticated cat but can also refer to large cats such as lions, tigers and leopards. Cats have been mixed with human life for at least 6000 years BC, since cat skeletons were discovered on the island of Cyprus (Arquitectura et al., 2015).  One of the diseases that often appears in cats is feline lower urinary tract disease (FLUTD), also known as feline urological syndrome (FUS), which is a health problem that often occurs in cats, especially male cats. This health problem attacks the cat's bladder and urethra. Urethral disorders are caused by the structure of the male cat's urethra, which is tube-shaped and has a narrow section, which often causes obstruction of urine from the bladder (VU) to the outside of the body. FLUTD includes several diseases that occur in the cat's urinary tract (Indonesia, 2022).  Knowledge about diseases in cats is one of the problems. Many owners do not realize that their cats are suffering from diseases that can cause death. The cause of the cat's death occurred due to the keeper's lack of knowledge regarding the disease and symptoms the cat was experiencing. Apart from that, the problem is that there are so few doctors available that they are difficult to find, and the information obtained is only in accordance with the condition of the cat when it goes to the vet. If you see other symptoms, like it or not, you have to consult the veterinarian again and it will take additional time and money. This problem can be solved with an expert system that can diagnose the disease suffered by the cat based on the selected symptoms.