Publication Search

Breast tumors are classified into two types, namely benign and malignant tumors, the latter commonly referred to as breast cancer. Breast cancer is one of the major health problems affecting women worldwide, including in Indonesia. According to WHO data in 2022, there were 2.3 million breast cancer cases with 685,000 deaths globally, while in Indonesia, 396,914 cases and 234,511 deaths were reported. The high incidence rate is exacerbated by low public awareness in recognizing early symptoms and performing early detection, resulting in 60–70% of cases being diagnosed at an advanced stage, supported by findings that 65.6% of female students still have a low level of knowledge. Female students were selected as research subjects because they are in a vulnerable reproductive age group and have an important role in increasing awareness of early detection, yet they still have limited knowledge. Based on this, this study aims to design a web-based early detection system for breast tumor risk using the Forward Chaining method, which functions as a tool to identify early symptoms, assess risk levels, and provide information on prevention and initial management. This study employed the method with the Expert System Development Life Cycle (ESDLC) model, consisting of the stages of assessment, knowledge acquisition, design, testing, and documentation, along with the Forward Chaining inference method.

This study aims to design and develop an expert system to assist in diagnosing diseases in dairy cattle at Cibugary Farm using the Forward Chaining method. The background of this research lies in the limited knowledge of farmers in identifying early symptoms of diseases, which often leads to delays in medical treatment and negatively affects dairy cattle productivity. To address this issue, an expert system was designed to replicate the reasoning process of a human expert through a knowledge base containing diagnostic rules derived from observable symptoms. The Forward Chaining method was chosen because of its capability to trace facts from known symptoms toward a conclusion regarding the type of disease affecting the cattle. The system was developed by incorporating common disease symptoms, inference rules, and a decision-making mechanism that simulates expert analysis. Testing was carried out on several diagnostic scenarios to evaluate the accuracy and efficiency of the system. The results of the study indicate that the expert system can provide an initial diagnosis quickly and accurately, producing outputs consistent with expert assessments. This functionality assists farmers in making timely decisions regarding appropriate medical interventions, thereby reducing treatment delays and minimizing the risk of disease transmission within the herd. Consequently, the Forward Chaining-based expert system is expected to serve as an innovative solution to improve dairy cattle health management and support sustainable livestock productivity at Cibugary Farm.

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.

Chilli farming is an important sector in Ende District, but plant disease problems are often an obstacle that hampers productivity. Farmers often have difficulty recognizing the symptoms of disease in chili plants and determining appropriate treatment steps. To overcome this problem, this research aims to design and build a web-based expert system that can help diagnose chili plant diseases using the Forward Chaining method. This expert system was developed by collecting knowledge from agricultural experts and literature related to chili plant diseases, as well as applying the Forward Chaining method for the reasoning process. Users, especially farmers, can enter the symptoms experienced by chili plants into the system, then the system will produce a disease diagnosis and appropriate treatment recommendations based on these symptoms. This research uses a case study at Maju Tani Agriculture in Ende District to ensure that the expert system developed is relevant to local conditions. This system is implemented in the form of a web-based application so that it can be accessed easily by farmers using devices connected to the internet. The test results show that this expert system can provide accurate and efficient diagnoses, as well as assist farmers in making decisions to overcome chili plant disease problems. It is hoped that this system can increase agricultural productivity and reduce the risk of losses due to plant diseases.

This research aims to develop a web-based expert system with the Forward Chaining method that can diagnose and provide treatment for people with stroke disease. The expert system will be used to help the diagnosis process by analyzing the symptoms reported by the patient. The Forward Chaining method will be used as an inference approach in this expert system. The system will use a knowledge base that contains medical rules obtained from experts. The inference process will be carried out by linking the symptoms reported by the patient with the information in the knowledge base to determine possible diagnoses and appropriate treatments. This expert system will be implemented in the form of a web-based application that can be accessed by the general public. The results of this study show that the expert system to diagnose and provide treatment using the forward chaining method is successfully built and the application can be used to help the general public diagnose by entering the symptoms felt and the expert system will provide diagnostic results and provide treatment.

Kucing adalah hewan peliharaan populer yang sering kali rentan terhadap berbagai penyakit. Ketidakmampuan pemilik untuk mendiagnosa penyakit kucing secara tepat waktu sering kali mengharuskan konsultasi dengan dokter hewan yang sebagian besar hanya tersedia di perkotaan. Penelitian ini bertujuan mengembangkan sistem pakar berbasis web menggunakan metode Forward Chaining dan Certainty Factor untuk membantu mendiagnosa penyakit pada kucing. Metodologi pengembangan sistem ini mencakup tahap persiapan, prototyping, dan penyelesaian. Pada tahap persiapan, masalah dirumuskan dan wawancara dengan dokter hewan dilakukan untuk membangun knowledge base. Tahap prototyping melibatkan pembuatan mesin inferensi dan knowledge base, sementara tahap penyelesaian meliputi implementasi sistem ke dalam antarmuka pengguna berbasis web menggunakan PHP dan framework Laravel. Hasil uji sistem menunjukkan konsistensi tinggi dengan hasil diagnosa manual menggunakan Excel. Sebagai contoh, diagnosa penyakit Virus Panleukopenia memiliki nilai certainty factor sebesar 94,62% pada aplikasi, sementara perhitungan manual menunjukkan 95,21%, dengan perbedaan hanya 0,41%. Hal ini menunjukkan bahwa sistem pakar yang dikembangkan mampu memberikan diagnosa yang cepat dan akurat. Kesimpulannya, sistem pakar berbasis web ini dapat diandalkan untuk mendiagnosa penyakit kucing dan membantu dokter hewan serta pemilik kucing melakukan diagnosa awal dengan lebih efisien.

Abstract -  Corn plants are one of the plants that are easy to grow and relatively fast to get results but also a plant that is vulnerable to pests and diseases. The symptoms that arise require an expert or agricultural expert to be able to diagnose. Limited time and expert labor is one of the causes of delayed diagnosis, causing a decrease in farmers' yields. This is the basis for making this expert system by adopting knowledge from agricultural experts about the symptoms of corn disease using the Forward Chaining method. To support the research conducted, researchers used the Research and Development (R&D) research method and the design used the Object Oriented Programming (OOP) method using the Unified Modeling Language (UML). Software is built using the PHP programming language, MySQL as a database, and Bootstrap as an interface design with a web-based system.    

Kejang demam adalah bangkitan kejang yang terjadi pada kenaikan suhu tubuh (suhu rektal di atas 38 Celcius) yang disebabkan oleh suatu proses ekstrakranium. Memasukkan sendok kedalam mulut, menahan gerakan anak, memberikan selimut tebal, memberikan kopi, merupakan perilaku salah dalam penanganan kejang demam sebelum dibawa ke rumah sakit. Kejang demam pada dasarnya merupakan penyakit yang tidak berbahaya dan tidak berdampak pada sistem neurologi. Namun, kejang demam ini merupakan pengalaman yang menakutkan dan menimbulkan kecemasan pada orang tua. Hal ini disadari sebagai salah satu faktor tingkat kecemasan pada sebagian orang tua bertambah. Tujuan penelitian untuk memberi pengetahuan keluarga tentang kejang demam dengan perilaku penanganan kejang demam sebelum dibawa ke rumah sakit.

Teeth are one of human‘s organs that feeds on food. As an organ, the tooth definitely can be attacked by some disease. There are many cases of dental disease that occur from dental diseases common to people to chronic dental diseases that can be dangerous. The low level of public concern about dental disease is a problem faced today. Evidenced by the small number of people who diligently consult with dentists about the health of their teeth. An alternative option is needed that can make it easier for people to be able to consult or diagnose dental health and disease without having to see a doctor of dental health. Therefore, in this essay an expert system was created to provide alternative choices for people. This expert system was created with the php hypertext preprocessor programming language. Using the forward chaining method as a tracking method. The purpose of making this expert system is to be able to be a substitute for a dental expert so that people can already do the diagnosis of dental disease anywhere and anytime.

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.

The recruitment of new mechanics at Mataram Sakti is no longer through the training process at the head office, but new mechanics are placed at the dealership accompanied by the chief mechanic as a supervisor, with the aim of providing training to new mechanics in order to be able to service or periodically repair Yamaha products especially the latest motorbikes namely Yamaha NMAX, because of the flurry sometimes the mechanical head is not in place so that when the mechanic is experiencing problems it cannot directly ask the mechanical head causing the handling of repairs to be less effective and efficient. One way to overcome this is to create a web-based expert system application with a forward chaining method so that the chief mechanic can transfer the knowledge he has into the computer. Forward chaining method is a search technique that starts with known facts and then matches these facts with the IF part of the IF-Then rules, so that conclusions can be drawn. This expert system was built using supporting software including, Macromedia Dreamweaver MX, PHP script language, MySQL database and WAMP server. The expert system for diagnosing damage to Yamaha NMAX motorcycles using the forward chaining method can be used as a new mechanical learning media in diagnosing damage to NMAX motorcycles and as a means of chief mechanics transferring their knowledge.