Article Per Year (5 Year)
p-Index From 2020 - 2025
2.177
P-Index
This Author published in this journals
All Journal ComEngApp : Computer Engineering and Applications Journal Annual Research Seminar Sriwijaya Journal of Informatics and Applications Jurnal Generic Jurnal Abdimas Maduma
Osvari Arsalan
Universitas Sriwijaya
Humanities Computer Science & IT Education Engineering

Published : 20 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : Sriwijaya Journal of Informatics and Applications

 1 
Comparison of Certainty Factor (CF) and Case Based Reasoning (CBR) to Diagnose Infertility in Women Risky Tama Putri; Yunita Yunita; Osvari Arsalan; Rizki Kurniati
Sriwijaya Journal of Informatics and Applications Vol 3, No 1 (2022)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v3i1.28

Abstract

Infertility has now become a terrible and serious problem for women. Limited information about infertility suffered by women makes it difficult for them to predict the disease they are suffering from. Therefore we need an expert system that can predict infertility in women. The methods used in this research are Certainty Factor (CF) and Case Based Reasoning (CBR) methods. Certainty Factor (CF) is one of the techniques used to overcome uncertainty in decision making. Case Based Reasoning (CBR) is a problem solving method by remembering similar events that happened in the past and then using that knowledge or information to solve new problems. Based on the test results using 25 test data, the accuracy of the expert system for diagnosing infertility in women using the Certainty Factor (CF) method is 92%, while the curation of the expert system for diagnosing infertility in women using the Case Based Reasoning (CBR) method is 76%. 
Text Similarity Detection Between Documents Using Case Based Reasoning Method with Cosine Similarity Measure (Case Study SIMNG LPPM Universitas Sriwijaya) Nabila Febriyanti; Dian Palupi Rini; Osvari Arsalan
Sriwijaya Journal of Informatics and Applications Vol 3, No 2 (2022)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v3i2.47

Abstract

LPPM Universitas Sriwijaya is an institution that coordinates academic research and community service inside Universitas Sriwijaya. In carrying out the duty, LPPM assesses every proposal’s originality which would be impossible to do manually in the future due to massive data growth. Thus, automatization for the proposal's originality check is needed. The Case Based Reasoning method is used in this research because it allows the system to reuse the information that has been obtained to find documents that are similar to the test document. In this study, the data is represented in the form of the Vector Space Model and uses Cosine Similarity to measure document to document similarity. The data is represented by giving weight for each part of the tested documents. In this study, four formulas from previous research will be used for term weighting then the final result will be compared. The process begins by extracting data, separating parts of the document, figuring the similarity value of the test document to the case base utilizing Cosine Similarity Measure, results filtering with a certain threshold, summarizing the calculation results, and finally preserving the results obtained to be reused in the next calculation. The results of this study indicate that the text-similarity detection between documents has been successfully carried out using the proposed method with the best sensitivity level and the fastest computation time achieved in configuration II.
Comparison Of The Results Of The Jaccard Similarity And KNearest Neighbor Algorithms Using The Case Based Reasoning (CBR) Method On An Expert System For Diagnosing Pediatric Diseases Hidayatullah, Altundri Wahyu; Rini, Dian Palupi; Arsalan, Osvari; Miraswan, Kanda Januar
Sriwijaya Journal of Informatics and Applications Vol 5, No 1 (2024)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v5i1.55

Abstract

Health ranks highest in supporting the continuity of every human activity, especially children. The availability of a doctor is still relatively lacking, especially in remote areas. This makes people have difficulty in diagnosing certain diseases so that medical treatment becomes too late and can even be fatal for the patient. So it is necessary to create a system that has the ability to be able to diagnose diseases in children like an expert. The method used in this study is Case Based Reasoning (CBR) with the Jaccard Similarity Algorithm and K-Nearest Neighbor. Jaccard Similarity is one way to calculate the similarity of two objects (items) which are binary. Similarity calculations are used to generate values whether or not there is a similarity between new cases and existing cases in the case base. While the K-Nearest Neighbor (KNN) Algorithm belongs to the instance-based learning group. The KNN algorithm allows the program to find old cases that are most similar to the current case. Based on the test results using 50 sample data, the expert system can provide diagnostic results in accordance with expert diagnoses. The accuracy results for the K-Nearest Neighbor Algorithm are 72% while the accuracy results for the Jaccard Similarity Algorithm are 70%.
Comparison Of Dempster Shafer AND Certainty Factor Methods In Expert System For Early Diagnosis Of Stroke Disease Arsalan, Osvari; Febrivia, Pretty Fujianti; Utami, Alvi Syahrini; Rodiah, Desty
Sriwijaya Journal of Informatics and Applications Vol 5, No 1 (2024)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v5i1.79

Abstract

Stroke is one of endangering disease if not treated properly and could lean to death. Most people unwilling to check their health because of high cost, lack of medical service, medical staff of neurologist and their limited working time. Therefore, we need an expert system that can help in early diagnosis of stroke. The Dempster Shafer and Certainty Factor methods are expert systems methods used in many cases to support uncertainty from the expert. The aim of this study is to compare two methods to determine the best method in the expert system for diagnosing stroke, by calculating symptoms so as to produce CF values in the Certainty Factor method and density values in the Dempster Shafer method. The data used in the study to diagnose stroke consisted of data on eighteen disease symptoms and two types of stroke identified. Based on the results of testing on 105 test data, the accuracy value of the expert system for diagnosing stroke using the Dempster Shafer method is 95.2% and the accuracy value of the expert system for diagnosing stroke with the Certainty factor method is 98.1%.
Co-Authors Aditya Aditya Aditya Putra Perdana Prasetyo Afriansyah, Indra Gifari Agung Juli Anda Ahmad Heryanto Ahmad Rifai Ahmad Rifai Ahmad Zarkasi Ahmad Zarkasi Al Amin Mulya Al Farissi Ali Bardadi Alif Almuqsit Almuqsit, Alif Alvi Syahrini Utami Anna Dwi Marjusalinah Atika Mailasari Bhakti Yudho Suprapto Buchari, Muhammad Ali Deris Stiawan Desty Rodiah Dian Palupi Rini Dian Palupi Rini Elin Darnela Ermatita - Fachrudin Abdau Fathan, Muhammad Rifqi Febrivia, Pretty Fujianti Firdaus Firdaus Hadipurnawan Satria Hidayatullah, Altundri Wahyu Huda Ubaya Huda Ubaya Kanda Januar Miraswan Kemahyanto Exaudi M. Fachrurrozi . Megah Mulya Muhammad Fachrurrozi Muhammad Qurhanul Rizqie Nabila Febriyanti Novi Yusliani Passarela, Rossi Primanita, Anggina Rahmat Fadli Isnanto Risky Tama Putri Rizki Kurniati Rizki Ramadandi Rossi Passarella Sarmayanta Sembiring Siti Nurmaini Vindriani, Marsella Yousnaidi, Rani Silvani Yulinda Ramadhana Yunita Yunita