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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Mantik Penusa Simtek : Jurnal Sistem Informasi dan Teknik Komputer Building of Informatics, Technology and Science U-NET Jurnal Teknik Informatika Bulletin of Information Technology (BIT) J-COSCIS : Journal of Computer Science Community Service CSRID
Subagio, Selamat
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Social Sciences

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Sistem Pakar Diagnosa Penyakit DBD Pasien Puskesmas Tanjung Sarang Elang Menggunakan Metode Certainty Factor Ainun, Annisa; Samsir, Samsir; Subagio, Selamat
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 1 (2025): Februari 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.1.2025.118-126

Abstract

Health is valuable for humans, because anyone can experience health problems. Children are very susceptible to germs and lack of sensitivity to symptoms of a disease is a fear for parents. Parents are lay people who do not understand health. If a child has a health problem, they prefer to trust it to experts or specialist doctors who already know more about health, regardless of whether the problem is still at a low or chronic level. With the convenience of having experts or specialist doctors, sometimes there are also disadvantages such as limited working hours (practice) and many patients so they have to wait in line. In the rainy season, almost no area in Indonesia is free from dengue fever attacks. Research shows that dengue fever has been found in all provinces of Indonesia. Two hundred cities reported an Extraordinary Event (KLB). The incidence rate increased from 0.005 per 100,000 people in 1968 and drastically jumped to 627 per 100,000 people. An Expert System is a computer system designed to imitate the problem-solving abilities of an expert in a particular field using the knowledge and analysis methods that have been defined by the expert. The results obtained in using this system are the information system for DHF patients at the Tanjung Sarang Elang Health Center can be completed easily without requiring a lot of energy and time, does not require many files if more than one DHF disease data is needed and the data produced is free from errors. Thus it is expected to be very helpful in diagnosing DHF patients at the Tanjung Sarang Elang Health Center. Macromedia Dreamweaver is an HTML editor for designing, writing code (php) and as a program for processing and building websites, web pages and other web applications.
Sistem Pakar Deteksi Penyakit Culvularia Dengan Metode Forward Chaining Panjaitan, Indra Syahputra; Subagio, Selamat; Samsir, Samsir
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 1 (2025): Februari 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.1.2025.106-117

Abstract

Oil palm diseases generally attack leaves during the seedling phase and can cause major losses if not handled properly. Symptoms that appear include yellow spots that then develop into necrosis, inhibiting seedling growth, and increasing the death rate of plants that during the entire life cycle of plants, from seeds, nurseries, planting, to storage of harvests, plants are never free from disturbances that can inhibit their growth and development. These disturbances can be in the form of pest attacks, infections of disease-causing pathogens, competition with weeds, or unfavorable environmental factors. A researcher from India estimated that crop yield losses were caused by weeds of around 33%, disease-causing pathogens of around 26%, pest attacks of around 7%, rats of around 6%, and damage during storage of around 7%. In other words, biological disturbances such as weeds, pathogens, and pests, as well as abiotic factors such as storage conditions, can cause significant decreases in yields if not managed properly. An expert system is a system that attempts to adopt human knowledge into a computer so that the computer can solve problems as is usually done by experts. While an expert system is a branch of artificial intelligence that uses the special knowledge possessed by an expert to solve certain problems. Forward Chaining is a method of data reasoning that starts from known facts and uses rules (IF-THEN) to reach a conclusion or solution. With this application, it is expected that the community can carry out early treatment and prevention of the disease independently, so as not to worsen the disease in the plant. The expert system is also equipped with a Microsoft Visual Studio solution, a complete application created by Microsoft. In Visual Studio, there are several programming languages ​​that are often used, such as Visual Basic 2008. Visual Studio 2008 Express Edition is very popular as a Windows Application Development Tool.
Expert System for Diagnosing Eye Disorders (Refractive Errors) Using the Certainty Factor (CF) Method at Tanjung Sarang Elang Community Health Center Subagio, Selamat; Harahap, Fauji; Samsir, Samsir
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 2 (2025): Juni 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.2.2025.204-216

Abstract

The medical and technology fields are rapidly advancing, leading many people to use computers to help diagnose, prevent, and treat human diseases. One major issue in the medical world is the imbalance between the number of patients and doctors. Additionally, most people lack medical training, so when experiencing symptoms of a disease, it is often difficult to immediately know the correct steps to take. Eye diseases vary in severity, ranging from mild to severe. One common eye disorder affecting many people is refractive error, which generally falls into two categories: hyperopia (farsightedness) and myopia (nearsightedness). Early detection of symptoms related to refractive errors requires accurate and prompt diagnosis. Therefore, with the rapid development of technology, it is essential to develop systems capable of early detection of eye diseases, especially refractive errors, by using technology that mimics human expert capabilities, such as expert systems. This expert system integrates expert knowledge within two main environments: the development environment and the consultation environment, helping the community diagnose diseases more easily and efficiently. For example, the use of the Certainty Factor method in expert systems enables the calculation of diagnostic certainty levels based on the combination of symptoms reported by patients and expert knowledge, achieving a confidence level of up to 96.7%. This demonstrates that expert system technology can be a valuable tool in addressing the imbalance between patients and doctors while improving access to faster and more accurate diagnoses. To build such systems, Microsoft Visual Studio .NET provides a complete set of tools for developing ASP.NET web applications, XML Web Services, desktop applications, and mobile applications. Within Visual Studio, .NET programming languages such as Visual Basic, Visual C++, Visual C# (CSharp), and Visual J# (JSharp) are used in a unified integrated development environment (IDE), enabling developers to efficiently share tools and resources to create reliable and user-friendly expert system applications. The system was developed and tested using symptom data from 40 patients collected at the Tanjung Sarang Elang Community Health Center. The testing showed a diagnostic accuracy of up to 96.7% in detecting symptoms of both hyperopia and myopia.
Sistem Pakar Diagnosa Penyakit Perokok Menggunakan Metode Backward chaining Subagio, Selamat; Rahmayani, Rahmayani; Samsir, Samsir; Azhar, Wahyu
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 3 (2025): Oktober 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.3.2025.340-353

Abstract

he rapid development of information and computer technology has had a significant impact on various fields, including healthcare. One of its applications is the expert system, a computer-based system utilizing Artificial Intelligence (AI) designed to imitate the reasoning and decision-making abilities of human experts. Expert systems are widely used to assist in diagnosing diseases based on symptoms experienced by patients, providing fast, efficient, and accurate solutions without requiring direct consultation with medical professionals. This study focuses on developing an Expert System for Diagnosing Smoking-Related Diseases among Lecturers at Universitas Al Washliyah Labuhanbatu. The system aims to help users, particularly active smokers, identify potential diseases caused by smoking habits. Based on preliminary studies and interviews conducted with the Health Department of Rantauprapat City, it was found that common diseases suffered by smokers include oral disease, lung disease, respiratory disorders, throat disease, and heart disease. These illnesses often develop unnoticed in the early stages, making early diagnosis essential for prevention and health awareness. The research applies the Backward Chaining inference method, which works by reasoning backward from a possible conclusion (disease) to find supporting facts (symptoms). The relationship between symptoms and diseases is represented through IF–THEN rules derived from expert knowledge. The system was developed using Macromedia Dreamweaver 8 as a web editor and MySQL as the database management system to store information on diseases, symptoms, and diagnostic results. The implementation results show that the system can provide early diagnoses quickly and accurately based on user-input symptoms. Furthermore, the system includes a confidence level feature that presents diagnostic certainty in percentage form. Hence, the developed expert system not only serves as a medical decision-support tool but also as a digital health education medium that promotes awareness of smoking dangers and the importance of maintaining a healthy lifestyle.
AHP-Based Expert System untuk Mengidentifikasi dan Mengklasifikasikan Kesulitan Belajar Anak Samsir, Samsir; Sahmuda, Arjana; Subagio, Selamat
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 3 (2025): Oktober 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.3.2025.311-325

Abstract

The rapid development of information technology has significantly impacted various sectors, including education. One of the common problems encountered in the educational field is learning difficulties in children, which may arise from internal or external factors such as poor concentration, limited reading ability, writing difficulties, or challenges in arithmetic skills. Undetected learning difficulties can hinder a child’s potential development and reduce learning motivation. Therefore, an intelligent system is needed to assist counseling teachers and parents in conducting early and objective identification. This study aims to design and implement an Expert System for Identifying Children’s Learning Difficulties using the Analytic Hierarchy Process (AHP) method. The AHP method was chosen due to its ability to assign priority weights to criteria and alternatives based on their level of importance. The study utilizes four main criteria: concentration (30%), reading ability (40%), writing ability (20%), and numerical ability (10%). The system was developed using a research and development (R&D) approach consisting of stages of requirement analysis, system design, implementation, and testing. The results indicate that the developed expert system can provide accurate and consistent identification results compared to manual AHP calculations. System validation tests achieved an accuracy rate of 99%, demonstrating high reliability in the decision-making process. Furthermore, the system has proven effective in assisting teachers and parents in detecting potential learning difficulties at an early stage, enabling faster and more precise interventions.
Co-Authors Ainun, Annisa Azhar, Wahyu Dalimunthe, Abdul Hakim Harahap, Fauji Has, Syahnila Natasya Khoirul Anwar Misman Panjaitan, Indra Syahputra Rahmad Aditiya Rahmayani Rahmayani Rambe, Shopiah Anggraini Ritonga, Sahbuki Ronal Watrianthos Sahmuda, Arjana Samsir Samsir Samsir Samsir Samsir, Samsir Sangkot Idris Ritonga Saragih, Reagan Surbakti Siagian, Taufiqqurrahman Nur