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All Journal Jurnal Ekonomi SOROT: Jurnal Ilmu-ilmu Sosial Jurnal Online Mahasiswa (JOM) Bidang Ilmu Ekonomi Jurnal Tepak Manajemen Bisnis Daya Saing : Jurnal Ilmu Manajemen QALAMUNA: Jurnal Pendidikan, Sosial, dan Agama Dinamika Ilmu IJEBA (International Journal of Economic, Business & Applications) Jurnal Penelitian Hukum Legalitas Jurnal Kreativitas PKM JOURNAL OF APPLIED BUSINESS ADMINISTRATION Building of Informatics, Technology and Science Management Studies and Entrepreneurship Journal (MSEJ) Jurnal Disrupsi Bisnis International Journal of Multidisciplinary: Applied Business and Education Research Journal of Applied Sciences, Management and Engineering Technology (JASMET) Jurnal Bisnis Mahasiswa Bulletin of Information Technology (BIT) Healthy Tadulako Journal (Jurnal Kesehatan Tadulako) Indonesian Journal of EFL and Linguistics Journal of Computer Science and Information Systems (JCoInS) Journal of Student Development Information System (JoSDIS) el-Buhuth: Borneo Journal of Islamic Studies Jurnal Economica: Media Komunikasi ISEI Riau Jurnal Bisnis Kompetitif Literasi: Jurnal Pengabdian Masyarakat dan Inovasi West Science Interdisciplinary Studies West Science Interdisciplinary Studies Komunita: Jurnal Pengabdian dan Pemberdayaan Masyarakat JAAMTER : Jurnal Audit Akuntansi Manajemen Terintegrasi Jurnal Manajemen Pendidikan Islam InJEBA : International Journal of Economics, Business and Accounting TAFASIR CSRID Jurnal Manajemen Dayasaing Jurnal Bisantara Informatika
Samsir Samsir, Samsir
Fakultas Ekonomi Universitas Riau
Religion Arts Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Other

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Peringatan Dini Bencana Banjir Berbasis Iot Menggunakan Pendekatan Metode Prediktif Aditya, Rahmad; Samsir, Samsir; Azhar, Wahyu; Rahmad, Iwan Fitrianto
CSRID (Computer Science Research and Its Development Journal) Vol. 16 No. 2 (2024): June 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.16.2.2024.161-173

Abstract

Floods are among the natural disasters that can cause substantial damage, particularly in tourist locations with high visitor traffic. This paper proposes the implementation of an IoT-based predictive method for early flood disaster warnings in tourist areas. The proposed system utilizes IoT sensors to monitor environmental conditions in real-time and employs machine learning-based predictive models to forecast the likelihood of flooding. By continuously collecting and analyzing data such as rainfall, river water levels, and soil moisture, the system can predict potential flood events with a relatively high degree of accuracy. The research involved developing and testing the system in a controlled environment to evaluate its performance. The results demonstrated that the system could provide timely early warnings, allowing tourist site managers to take necessary preventive measures to protect visitors and infrastructure. The implementation of such a system can significantly reduce the impact of floods by providing actionable information well in advance of potential disasters. This early warning capability is crucial in tourist areas where rapid response is necessary to ensure the safety and well-being of visitors. Overall, the study highlights the effectiveness of combining IoT technology with predictive analytics in disaster management and risk mitigation
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 Selecting College Majors Based on Interests and Talents Using the Certainty Factor (CF) Method Siregar, Aldi Sajali; Ritonga, Wahyu Azhar; 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-.17.2.2025.163-175

Abstract

Generally, new prospective students often experience confusion in choosing a major in Computer Science, Informatics Engineering Program at Universitas Al Washliyah Labuhanbatu that truly matches their academic abilities and interests. Often, the decision to choose a major is influenced by the environment, such as following close friends or advice from parents, without considering the students' true potential and talents. However, choosing an inappropriate major can negatively impact their academic future and career. Therefore, it is crucial for prospective students to recognize and understand their academic abilities as well as their special interests and talents. The expert system for major determination developed using the Certainty Factor method is expected to provide an effective solution by combining various indicators of interests, talents, and academic abilities of prospective students. This method utilizes established rules to calculate the certainty level (CF value) for each possible major based on the data and characteristics possessed by the student. By combining CF values from various facts, the system can provide recommendations for the major that best fits the student's greatest potential. This model does not rely solely on academic scores but also considers special interests such as social, creative, and artistic talents possessed by students, making the major selection decision more precise and directed. Thus, this Decision Support System can assist the Faculty of Computer Science, Informatics Engineering Program at Universitas Al Washliyah Labuhanbatu in selecting truly potential new students and providing appropriate recommendations, thereby minimizing the risk of wrong major choices and increasing the likelihood of academic and career success for prospective students.
Expert System for Stroke Diagnosis Using the Forward Chaining Method for Lecturers at UNIVA Labuhanbatu Samsir, Samsir; Syahputra, Andi; Subagio, S.
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-.17.2.2025.176-190

Abstract

Stroke is a health problem that often occurs when the blood supply to the brain lacks oxygen and nutrients. As a result, in a matter of minutes, brain cells begin to die. This condition is classified as a serious disease and can be life-threatening, therefore requiring immediate medical attention. Stroke accounts for 10% of all deaths in the world and is the third leading cause of death after coronary heart disease (13%) and cancer (12%) in developed countries. The prevalence of stroke varies in different parts of the world. The prevalence of stroke in the United States is around 7 million (30%), while in China the prevalence of stroke ranges from 1.8% (rural) to 9.4% (urban). Worldwide, China is the country with the highest death rate from stroke (19.9% ​​of all deaths in China), along with Africa and North America. The incidence of stroke worldwide is 15 million each year, one third of whom die and one third of whom experience permanent disability. The purpose of this study is to help and facilitate lecturers at Labuhanbatu University to diagnose stroke in determining treatment and how to overcome it effectively and efficiently. Lecturers at Univa Labuhanbatu can diagnose in advance what disorders they are experiencing before going to the doctor, so they can save time and money. This system is present as a means to help diagnose patients using the Forward Chaining method. With an expert system, laypeople will be able to solve quite complicated problems that can actually only be solved with the help of experts. For experts, expert systems will also help their activities as very experienced assistants. Microsoft Visual Studio .NET is a complete collection of development tools for building ASP.NET Web applications, XML Web Services, desktop applications, and mobile applications. In Visual Studio, these are the .NET programming languages ​​such as Visual Basic, Visual C++, Visual C# (CSharp), and Visual J# (JSharp). All use the same integrated development environment or IDE so that it is possible to share tools and facilities
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.
Diabetes Diagnosis Expert System Based on Family History Analytic Hierarchy Process (AHP) Method Saragih, Reagan Surbakti; Nufus, Inayah chayatun; 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-.17.2.2025.229-242

Abstract

An expert system is a branch of artificial intelligence (AI) designed to replicate the decision-making abilities of a human expert in a specific domain. It utilizes a rule-based approach by incorporating expert knowledge and experience into a computer system, allowing non-expert users to analyze and solve complex problems efficiently. One of the critical applications of expert systems is in the healthcare sector, especially in supporting early diagnosis of chronic diseases such as Diabetes Mellitus. Diabetes Mellitus is a metabolic disorder characterized by elevated blood glucose levels caused by insufficient insulin production or the body's inability to effectively use insulin. It is classified into two main types: Type 1 Diabetes Mellitus (Insulin Dependent) and Type 2 Diabetes Mellitus (Non-Insulin Dependent). Key factors contributing to the onset of diabetes include genetic predisposition, obesity, and unhealthy lifestyle habits. To assist the public in self-diagnosing the risk of diabetes, a web-based expert system was developed using the Analytic Hierarchy Process (AHP), a structured decision-making method that helps prioritize multiple criteria. In this system, symptoms such as frequent thirst, weight loss, and family history of diabetes are assessed and weighted using AHP to determine a person's risk level. The system is implemented using PHP programming language and MySQL database. Users interact with the system by answering a set of predefined questions, and based on their responses, the system calculates and displays the diagnosis result with corresponding risk categories.This expert system aims to raise public awareness and provide an accessible tool for early detection and prevention of diabetes, especially in regions with limited access to healthcare professionals.
Expert System for Early Detection of Depression Using Psychological Symptoms Certainty Factor Method Rambe, Nisa indriani; Samsir, Samsir; Subagio, S.
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-.17.2.2025.243-255

Abstract

Depressive disorders in the elderly often go undetected due to early symptoms that resemble normal aging processes. The absence of an early detection system becomes a major obstacle to prompt treatment. This study aims to design an expert system for early detection of depression in the elderly using the Certainty Factor (CF) method. The dataset was collected from 60 patient complaint narratives and validated by three professional psychologists with over five years of experience in geriatric psychiatry. The system design process includes symptom extraction using Natural Language Processing (NLP), CF value calculation for each symptom, and classification of depression risk (low, moderate, high). The system architecture consists of a knowledge base, inference engine, and user interface. Validation was conducted through diagnostic accuracy testing and user evaluation using a Focus Group Discussion (FGD). The results showed a validity level of 73%, and 88.6% of respondents agreed that the system can assist in early diagnosis. The novelty of this study lies in the integration of NLP and Certainty Factor tailored to the narrative patterns of the elderly, combined with a user-friendly interface design. This system is expected to serve as a supportive tool for psychologists and families in the early detection of depression in elderly individuals.
Sistem Pakar Diagnosa Penyakit Busuk Kuncup Pada Tanaman Sawit Menggunakan Trend Moment Siddik, Muhammad; Abdullah, Iqbal; Samsir, Samsir; Sirait, Azrai
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-.17.3.2025.408-420

Abstract

Bud rot disease in oil palm is one of the most serious threats that can significantly reduce productivity and even cause plant death if not detected early. To support a faster and more accurate diagnosis process, this study developed a web-based expert system that applies the Trend Moment method. The system is built on a knowledge base containing the main symptoms of the disease, including wilted and rotting young leaves (G001), foul odor from the bud (G002), easily detached young leaves due to decay (G003), and rotting crown with brown mucus (G004). The system is able to identify three types of diseases, namely bud rot, Phytophthora palmivora, and Erwinia spp.. The diagnosis process is carried out by calculating the weight of symptoms selected by the user and determining the most probable disease based on the highest Trend Moment value. Experimental results on 20 test cases showed that the system achieved an accuracy rate of 100% when compared with expert diagnoses. These findings indicate that the developed expert system has strong potential to be an effective tool for farmers and field extension workers in detecting and managing oil palm diseases at an early stage.
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.
Co-Authors A.A. Ketut Agung Cahyawan W Abd. Karim, Abd. Abd. Rasyid Syamsuri Abdullah, Iqbal afred suci, afred Ainun, Annisa Al Fajri, Hikmi Alamsyah - Alfauzan Harahap, Ridho Alvi Furwanti Alwie Amir, Rahmat Dian Andi Syahputra Anggia, Paramitha Ansar Arifin, Ansar ARIF KURNIAWAN Arsyad Arsyad Arwinence, Arwinence Azhar, Wahyu Azmi, Fauzan Azrai Sirait Bambang Arianto Berampu, Lailan Tawila Betti Megawati Botutihe, Fauziah Dalimunthe, Abdul Hakim Dewi Andriyani Dewita Suryati Ningsih Dian, urnamasari Diana Eravia Eko Setia Budi Esti Handayani, Dwi Fadillah, Erwin Fajri, Muh. Nurul Febriwanti, Febriwanti Garnasih, Raden L. Gatot Wijayanto Guidio Leonarde Ginting Hamzah Hamzah Handayani, Tut Harahap, Fauji Hariska, Elvia Hasbullah Hasbullah Hasri, Mulya Helfiyana, Helfiyana I Ketut Gunarta Isyandi, B. Iwan Fitrianto Rahmad Iwan, Daulay N Iwan, Daulay Nauli Jayanti, Elda Jumiati Sasmita Kelvin Kelvin Khalif, Arif Khalilurrahman Khalilurrahman, Khalilurrahman Kusmanto Kusmanto Maisaroh Ritonga Mardiana Mardiana Marpaung, Radinda Tamara Ayu Marpaung, Rio Jonnes Minarti, Melly Minci, Veronika Yurike MITRA LINDA, MITRA Muhammad Yusril Muslimah, Jannatul Nalapraya, Tresna Nasution, Sya’ral Norhidayati Rahmah, Mariyatul Nufus, Inayah chayatun Nur Halimah Nursalimah, Nursalimah Nurwati Nurwati Pakpahan, Donok M Panjaitan, Indra Syahputra Paramitha, Anggia Prima Andreas Siregar Primaroni, Oky Putra, Ryryn Suryaman Prana Putri, Raisa Monica Raden Lestari Ganarsih Raden Lestari Garnasih Rahma, Ismiwiya Rahmad Aditiya Rahmad Aditya Rahmayani Rahmayani Ramadan, Unggul Akbar Rambe, Nisa indriani Recky Riandika Sayandra, Recky Riandika Rezki, Ananda Rio g, Marpaung J.M. Rio, Marpaung Jones Rio, Marpaung Jonnes M Rizqon Jamil Farhas Ronal Watrianthos Rustami, Rustami Ruwaidah, Ruwaidah Ryan, Simatupang Sury Febriansyah Sahid, Aidinal Sahmuda, Arjana Salewe, M Idman Saragih, Reagan Surbakti Septherine, Putri Sharnuke Asrilsyak Shela, Hasm Riaufa Siagian, Taufiqqurrahman Siagian, Taufiqqurrahman Nur Siddik, Muhammad Siregar, Aldi Sajali Siregar, Alisa Yulima Siregar, Eka Maya Putri Sitinjak, Juniar Sri Indarti Sri Restuti Subagio, S. Subagio, Selamat Subagio, Selamet Sulasri, Sulasri Sumarti Sumarti Suntin, Suntin Suseno, Novri Irfan Nur Susi Hendriani Syawalmi, Laily Tahara, Tasrifin Tahir, Tarmizi Tang, Mahmud Tengku Firli Musfar Wahyu Azhar Ritonga Widayatsari, Any Yani, Juli Yani Yanuari, Said ZA, Kasman Arifin Zul, Huzeir Zulfadil, Zulfadil Zulfadil, Zulfadil Zulfadil, Zulfadil Zulkarnain Zulkarnain