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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Information Systems and Technology Research
Kartika Sari
STMIK Triguna Dharma, Medan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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 1 
Penerapan Metode SAW (Simple Additive Weighting) Dalam Pemilihan Saham Terbaik Pada Sektor Teknologi Rosma Siregar; Kartika Sari; Siti Julianita Siregar
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3425

Abstract

Stocks are one of the many investments that are favored by all groups because they promise high returns. But in addition to promising high returns, stocks can also provide a high risk of loss, which makes ordinary people afraid to start investing in the stock market. To prevent losses in buying stocks is to choose stocks with good fundamentals. To support this, we need an analysis that can help make decisions in choosing the best stocks in the technology sector. The saw method analysis will be used in this study, where the saw method is able to select alternatives based on predetermined categories. This study will rank the best stocks based on company fundamentals, namely EPS, PER, PBV, ROE, DER and Dividend Yield. The results of this study are EDGE stocks as the best stocks in the technology sector with the highest value of 0.88. The purpose of this research is to help investors choose stocks before investing in technology companies.
Evaluasi Kinerja Karyawan Kontrak Menggunalan Metode Fuzzy Tsukamoto Kartika Sari; Rosma Siregar
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3441

Abstract

Contract employees are employees who work in contact with a certain time agreement. However, there are times when contact employees with good performance will change their status to permanent employees. To determine whether an employee is a permanent employee, an evaluation of whether the employee's performance is worthy of being appointed as a permanent employee is required. However, to carry out this evaluation, a variable is needed to make an assessment. In the performance evaluation it is not easy to determine the value of each variable. To assist an HRD in determining the appointment of a contact employee to become a permanent employee, a decision support system is needed to facilitate HRD work. The decision support system is made using the Tsukamoto fuzzy logic method because the Tsukamoto fuzzy has a tolerance for value data. The result of the research is that the employee can be appointed as a permanent employee with a value of 93.4. The purpose of this decision support system is to determine whether or not contract employees are eligible to become permanent employees based on alternative disciplines, ways of working and behavior.
Analisis Certainty Factor Dalam Mendiagnosa Tipe Diabetes Berbasis Web Kartika Sari; Rosma Siregar; Astri Syahputri
Journal of Information System Research (JOSH) Vol 3 No 4 (2022): Juli 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (299.483 KB) | DOI: 10.47065/josh.v3i4.1793

Abstract

This study discusses certainty factor analysis to diagnose diabetes mellitus (DM) which is implemented in a smart system that aims to make it easier for people to know the type of diabetes. A person's lifestyle is the main thing in maintaining health, but if you have an unhealthy lifestyle, it will cause disease. One of the many diseases caused by an unhealthy lifestyle is diabetes mellitus. The system built in this research will be implemented using the certainty factor (CF) method. Users can choose what symptoms are experienced on the system, where the symptoms are obtained from an expert. The output of this expert system is to diagnose the user as having type 2 diabetes mellitus. This research yielded 84% results. The purpose of this study is to help the community in diagnosing the type of diabetes experienced so that they can get the right treatment.
Sistem Pakar Menggunakan Teorema Bayes Dalam Rekomendasi Penentuan Jenis Anestesi Pada Pasien Siti Julianita Siregar; Kartika Sari
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2226

Abstract

This study discusses the problem, namely the process of determining the type of anesthesia for patients before carrying out surgery. In determining the appropriate type of anesthesia based on the conditions experienced by the patient, generally anesthesiologists or anesthesiologists still use the common method, namely by conducting interviews related to the symptoms experienced by patients before anesthesia is carried out on patients who will be operated on. Then the anesthesiologist will write down the results of the interview in the form of a written report and will adjust the results of the interview related to the symptoms experienced with the existing anesthesia guidelines. And this will certainly take more time in adjusting the results of the patient's symptoms to the type of anesthesia that will be given. Along with the rapid development of technology, determining the type of anesthesia that will be given to the patient before it is carried out can be overcome by building an information system that is able to adopt the process and way of thinking of humans, namely Artificial Intelligence or artificial intelligence which is often called the Expert System. In this case, a smart application in determining the type of anesthesia in android-based patients is designed using the Bayes Theorem calculation method, and it is possible for an anesthesiologist and anesthesiologist to administer anesthesia to a patient before a patient steps into the operation stage. Thus, it can also cause work productivity to increase and the time used to complete the work is getting shorter
Intelligent System for Mental Health Disorder Diagnosis Using Certainty Factor Method Kartika Sari; Hendri Winata; Wahyu Riansah
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1308

Abstract

Mental health issues are increasingly recognized as a global challenge, affecting more than 450 million people worldwide, with a significant treatment gap in developing countries such as Indonesia. The limited number of mental health professionals highlights the need for alternative solutions to support early diagnosis. This study aims to design and implement an intelligent expert system for the diagnosis of mental disorders using the Certainty Factor (CF) method. The CF approach was selected for its ability to handle uncertainty and subjectivity in expert reasoning, particularly in cases where symptoms overlap across different disorders. The research methodology includes problem analysis, data collection, system design, implementation, and evaluation. The system was tested using a dataset of mental disorder symptoms, including depression, anxiety, schizophrenia, and bipolar disorder. The results indicate that the system can diagnose bipolar disorder with the highest CF value (0.951), followed by depression (0.883), anxiety disorder (0.853), and schizophrenia (0.510). These findings demonstrate that the CF-based system can provide accurate and realistic initial diagnoses that approximate expert judgment. This research contributes to the field of health informatics by providing a decision-support tool that can be integrated into telehealth platforms, enabling communities to gain faster access to mental health screening.
Co-Authors Hendri Winata Rosma Siregar Siti Julianita Siregar Syahputri, Astri Wahyu Riansah