Sistem Pakar Identifikasi Jurusan Yang Sesuai Dengan Minat Bakat Siswa - Nella Novrina Doni; S Sumijan; Billy Hendrik (Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen), 2024)
The Certainty Factor method can help overcome this complexity by providing a level of confidence regarding major recommendations. Certainty Factor allows an expert system to measure the level of confidence or uncertainty associated with each knowledge rule or fact used. Each rule contributes to the overall confidence level, and this can provide an idea of the extent to which the system is confident in the recommendations it produces. In complex decision making, various knowledge rules can influence each other. The Certainty Factor method allows the integration of belief values from interrelated rules, providing a holistic picture of the extent to which evidence supports a conclusion. In some cases, knowledge rules may provide contradictory or conflicting information. Certainty Factor can be used to handle information conflicts by assigning a weight or level of confidence to each piece of information, so that the system can produce more accurate recommendations. Certainty Factor provides a mechanism for measuring the level of uncertainty in a decision. This is important when the available information is incomplete or there is uncertainty in the values used in the knowledge rules. By taking into account the level of uncertainty, the system can provide recommendations that are more realistic and appropriate to complex situations. The Certainty Factor method can be applied dynamically, allowing the system to adjust confidence levels over time or with the addition of new information. This is useful when students experience changes in interest or talent, so the system can provide more accurate and relevant recommendations..