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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA
Hanifah Ekawati
STMIK Widya Cipta Dharma, Samarinda
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Penerapan Metode MOOSRA dalam Rekomendasi Platform Investasi Emas Online Terbaik dengan Pembobotan ROC Hanifah Ekawati; Yunita Yunita
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.6063

Abstract

In the current era, technology is considered more practical in doing all kinds of things, for example, making investments that initially could only be done in person and were considered complicated, but now with technology, investments can be made online, making it more practical. Investments can be made by the younger generation, not only by their parents, the younger generation needs to learn about various things about investing with the aim of preparing for a brighter future. Investments that can be made by the younger generation can be in various forms, one of which is gold investment. Gold investment can be done by buying physical gold such as jewelry or gold bars, or by means of non-physical gold investment such as through the futures market, gold mutual funds, or online gold investment platforms. The online gold investment platform offers competitive gold prices and gold storage services in a secure warehouse to make it easier for investors to buy and sell gold online. However, because there are so many gold investment platforms, young gold investors are confused about determining the best online gold investment platform to use. The application of a decision support system is used in this study to solve problems in the recommendation of the best gold investment platform by applying the MOOSRA (Multi-Objective Optimization on the Basis of Simple Analysis) method and ROC (Rank Order Centroid) weighting. Then several criteria are used in the recommendations for the best gold investment platforms, namely Rating Reviews, Integrated E-commerce, Number of Payment Methods, Number of Partners and Minimum Purchases. By applying the MOOSRA and ROC methods, the best gold investment platform results with the highest preference value are obtained in alternative P2, namely BukaEmas with a value of 1151.88524.
Perbandingan Keefektifan Metode Case-Based Reasoning dan Certainty Factor dalam Sistem Pakar Diagnosis Penyakit Multiple Sclerosis Hanifah Ekawati; Ita Arfyanti; Tommy Bustomi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6574

Abstract

The management of complex neurological diseases such as Multiple Sclerosis (MS) requires accurate and efficient diagnostic approaches. To enhance diagnostic precision, a study has conducted a comparison between two approaches within the framework of an expert system, namely the Case-Based Reasoning (CBR) Method and the Certainty Factor (CF) Method. The primary objective of this study is to evaluate the effectiveness of these two methods in supporting the diagnosis process of Multiple Sclerosis. The Case-Based Reasoning Method is an approach that relies on past experiences to address new issues. Within an expert system, CBR utilizes knowledge from previous cases to identify diagnoses that align with the current situation. On the other hand, the Certainty Factor Method is an approach that measures the confidence level in a statement based on rules and associated confidence factors. This study makes use of a dataset containing information from previous cases related to the diagnosis of Multiple Sclerosis. By employing both of these methods, an expert system is developed to provide diagnostic recommendations based on inputted symptoms and data. The effectiveness of both approaches is evaluated through diagnostic accuracy, computational speed, and confidence levels in the generated results. Research findings indicate that both methods have their respective strengths and weaknesses. The CBR method tends to yield accurate results by referring to similar cases in the past, but it may encounter challenges in unique or rare cases. On the other hand, the Certainty Factor Method has the ability to handle uncertainty and can produce results with measurable confidence levels. However, dependence on predefined rules may limit adaptation to new cases. In conclusion, this study underscores that there is no singular perfect approach within expert systems for diagnosing Multiple Sclerosis. Both the CBR and Certainty Factor methods contribute in their own ways to improving accuracy and confidence in the diagnosis process. Therefore, integrating these two methods could be a promising direction for the development of expert systems in the future.
Co-Authors Ita Arfyanti Tommy Bustomi Yunita Yunita