Identification of Computer Hardware Damage Using a Website-Based Certainty Factor Method

2024-08-13T12:37:58+08:00

A computer is an electronic device designed to receive input, process it, store processing instructions, and deliver output as information. Hardware refers to the physical components of a computer that are tangible and visible. Due to inadequate knowledge in troubleshooting computer malfunctions, novice users often face challenges in identifying the source of hardware issues. Consequently, not all users are equipped to repair hardware problems, which may lead to unnecessary repair costs for issues that could potentially be resolved independently. Thus, there is a necessity for a system capable of diagnosing computer hardware malfunctions, functioning as an expert system to provide practical consultation and information. This expert system is developed using PHP and Laragon. The dataset utilized in this research comprises 44 symptoms. The system design follows the Waterfall methodology for system analysis and employs the Certainty Factor method for diagnosing types of malfunctions. System testing is conducted using both Whitebox and Blackbox testing methodologies, and accuracy is evaluated using a Confusion Matrix. The system for identifying computer hardware malfunctions achieved an accuracy rate of 75%..

Penerapan Recursive Feature Elimination pada Support Vector Machine untuk Klasifikasi Kanker Payudara

2024-08-13T12:31:39+08:00

Breast cancer is a prevalent type of cancer among women worldwide and can have fatal consequences if not detected early. Errors in breast cancer diagnosis can occur due to the use of irrelevant features or attributes, leading to misclassification. To minimize this possibility, this study applies the Recursive Feature Elimination (RFE) feature selection method to the WDBC (Wisconsin Diagnostic Breast Cancer) dataset to select the most relevant features in distinguishing benign and malignant tumor classes. SVM (Support Vector Machine) algorithm was used as the classification model with a data sharing ratio of 90:10, resulting in an accuracy of 0.98, precision of 1.00, recall of 0.94, and F1-score of 0.97. The implementation of RFE successfully reduced 50% of the features without reducing the performance of the model compared to the use of all features.

LEDGER : Journal Informatic and Information Technology

Identification of Computer Hardware Damage Using a Website-Based Certainty Factor Method

Penerapan Recursive Feature Elimination pada Support Vector Machine untuk Klasifikasi Kanker Payudara