Jurnal SINTA 1
Hepatitis Diagnosis Using Case-Based Reasoning with Gradient Descent as Feature Weighting Method
Journal of Information Systems Engineering and Business Intelligence · Vol. 4 No. 1 (2018) · pp. 25–31
- ISSN
- 2443-2555, 2598-6333
- Published
- 2018-04-28
- Publisher
- Universitas Airlangga
- Language
- eng
Abstract
Retrieval is one of the stages in case-based reasoning system which find a solution to new problem or case by measuring the similarity between the new case and old cases in the case base. Some of the similarity measurement techniques are involving feature weights that show the importance of the feature in a case. Feature weights can be obtained from a domain expert or by using a feature weighting method either locally or globally. Gradient descent is the feature weighting method which computes global weights for each feature. This research implemented gradient descent to obtain feature weights in case-based reasoning for hepatitis diagnosis and the similarity measurement using weighted Euclidean distance. There are four variations number of case base and test data that used in this research, those are: the first variation using 50% of data as case base and 50% as test data second variation using 60% of data as case base and 40% as test data, third variation using 70% of data as case base and 30% as test data and fourth variation using 80% of data as case base and 20% as test data. For each variation, using 4 kinds of scenario to mark the test data those are in first scenario the test data mark at the end of data, in second scenario the test data mark at the begin of data, in third scenario the test data mark half at the begin and half at the end of data and in the fourth scenario the test data mark in the middle of data. The result of this research showed that the accuracy of the system reaches 100% at scenario 1 in variation 4. Overall of all four variations and four kinds of scenario, the average accuracy of the system was 77.55%, average recall of system was 69.74%, and the average of precision was 78.39%. In addition, the level of accuracy was also influenced by the number of case base and the scenario of case selection for the case base. This is because more cases in the case base, the chances of a system to finding similar cases will be more.
Keywords
Collaboration network of the authors
20 authors · 31 co-authorship links · 8 across different organisations
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
- Universitas Gadjah Mada
- University of Brawijaya
- National Research and Innovation Agency
- Sumatera Institute of Technology
- Universitas Negeri Surabaya
- Affiliation unknown
- Different organisations
- Same organisation
- Affiliation unknown
- Thicker line = more joint articles · bigger circle = more articles
Show as table
| Co-author | Organisation | Joint articles |
|---|---|---|
| Arief Ichwani | Sumatera Institute of Technology | 1 |
| Windyaning Ustyannie | Universitas Gadjah Mada | 1 |
| Devi Valentino Waas | Universitas Gadjah Mada | 1 |
| Muhammad Faqih Dzulqarnain | Universitas Gadjah Mada | 1 |
| Faizal Makhrus | Universitas Gadjah Mada | 1 |
| Wahyono Wahyono | Universitas Gadjah Mada | 1 |
| Yaqutina Marjani Santosa | Universitas Gadjah Mada | 1 |
| Agung Pambudi | Universitas Gadjah Mada | 1 |
| Nasution Nasution | Universitas Gadjah Mada | 1 |
| Yusi Tyroni Mursityo | University of Brawijaya | 1 |
| Muhammad Ghiffari | University of Brawijaya | 1 |
| Yurizka Melia Sari | Universitas Negeri Surabaya | 1 |
| Janu Arlinwibowo | National Research and Innovation Agency | 1 |
| Gupita Nadindra Fatima | Affiliation unknown | 1 |
| Dwi Purwoko | National Research and Innovation Agency | 1 |
| San Afri Awang | Affiliation unknown | 1 |
| Ahmad Maryudi | Affiliation unknown | 1 |
| Wahyu Wardhana | Affiliation unknown | 1 |
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.