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All Journal Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON)
Naufal Rifqi
Universitas Nasional, Jakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering

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Perbandingan Metode Dempster Shafer Dan Teorema Bayes Dalam Sistem Pakar Mendiagnosa Moyamoya Disease Naufal Rifqi; Agus Iskandar
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 1 (2023): September 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i1.6819

Abstract

The main aim of this research is to compare two analytical approaches, namely the Dempster-Shafer Method and the Bayes Theorem, in the context of a system developed for diagnosing Moyamoya disease. Moyamoya is a rare condition involving the narrowing or blocking of blood vessels in the brain, which can lead to disrupted blood flow and an increased risk of stroke. In the medical field, diagnosing Moyamoya disease is a crucial initial step for appropriate treatment planning. The Dempster-Shafer Method is an approach used to address uncertainty and combine uncertain information into a conclusion. On the other hand, the Bayes Theorem is a statistical principle that connects the probability of a hypothesis before and after new evidence emerges. Both of these approaches are vital in the medical diagnostic process. In this study, both methods are implemented in an expert system specifically developed for diagnosing Moyamoya disease. Data from Moyamoya cases are used to evaluate the performance of both methods. Performance measurement is conducted by observing diagnostic accuracy, computational time, and resource usage. The results of this research provide valuable insights into the effectiveness and performance of the Dempster-Shafer Method and the Bayes Theorem in medical applications, particularly in diagnosing Moyamoya disease. Strengths and weaknesses of each approach are revealed, aiding in understanding situations where each method is most suitable. The Dempster-Shafer Method is effective in dealing with complex uncertainties and combining uncertain evidence. Meanwhile, the Bayes Theorem excels in probability calculations. The implications of this research are important in developing more advanced medical expert systems. In the medical realm, where diagnostic decisions impact patient care, a better understanding of these approaches helps in selecting the most appropriate method for specific situations. The results of comparing both methods indicate that the Dempster-Shafer Method yields a high probability of around 91%, indicating a substantial likelihood that the patient is suffering from this disease. Conversely, the Bayes Theorem yields a low probability of around 22%, suggesting a relatively small likelihood that the patient has Moyamoya Disease.
Prioritas Penanganan Anemia pada Ibu Hamil Menggunakan Metode TOPSIS Naufal Rifqi; Agus Iskandar
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 1 (2023): September 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i1.6820

Abstract

During pregnancy, women experience anemia which can negatively impact maternal health and fetal development. The government has taken various measures to address anemia in pregnant women, but the reduction in anemia rates has not been significant. Therefore, the treatment needs to be focused on individuals with high risk to be more effective. Decision Support System (DSS) is a tool used in complex decision-making processes and one of the methods is TOPSIS. TOPSIS is used to set priorities by comparing each alternative against predetermined positive and negative ideal solutions. In this study, there are 10 alternatives and 5 criteria. Based on the results of calculations with the TOPSIS method, Alternative 3 (A3) with a preference value of 0.246561061 is designated as a pregnant woman who must be prioritized in handling anemia.
Seleksi Pemilihan Staff Account Receivable dengan Penerapan Sistem Pendukung Keputusan Kombinasi Metode WASPAS dan ROC Naufal Rifqi; Agus Iskandar
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4619

Abstract

Appropriate Account Receivable staff must be qualified, have skills, and exhibit traits that are suitable for the duties and responsibilities associated with receivables management. Errors in staff selection can potentially create problems in the management of receivables, result in delays in the collection of funds, and pose a greater risk of loss to the company. Therefore, the selection and selection of qualified Account Receivable Staff is a very key factor in achieving optimal financial performance. Selection in the selection of Account Receivable staff has several criteria, namely Education, Experience, Technical Skills, Communication Skills and Negotiation Skills. In the midst of an increasingly complicated business environment and intense competition, the use of decision support systems (SPK) is becoming more relevant to assist in the optimal employee selection process, given the importance of objectivity and accuracy in the selection process by applying the ROC (Rank Order Centroid) and WASPAS (Weighted Aggregated Sum Product Assesement) methods which are utilized to provide weight values to the selection criteria, while WASPAS is used to verify the results of the selection decision. So that the best Account Receivable Staff selected is alternative A5 with the highest value of 3.8354 as the first rank.
Co-Authors Agus Iskandar