Fikri Azhari, . (2025) PERBANDINGAN KINERJA ALGORITMA DALAM KLASIFIKASI SERANGAN DDoS BERDASARKAN DATA CIC IoMT DATASET. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
With the widespread application of the Internet of Things (IoT) in various sectors, including the medical sector with Internet of Medical Things (IoMT) technology, Distributed Denial of Service (DDoS) attacks are a serious threat to the sustainability of the system. This research compares four machine learning algorithms Random Forest, LightGBM, Naïve Bayes, and K-Nearest Neighbors (KNN) to detect DDoS attacks on IoMT. The evaluation is based on accuracy and computation time running in parallel (GPU) using the Weighted Sum Method approach. The results show that Random Forest has the best performance with a score of 0.971578, followed by Naïve Bayes with a score of 0.961235. Although KNN has high accuracy, it is less time efficient, while LightGBM shows the lowest performance in terms of accuracy and efficiency. This research is expected to contribute to the development of a fast and accurate cyber threat detection system in the IoMT environment.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 1910511005] [Pembimbing 1: Bayu Hananto] [Pembimbing 2: Iin Ernawati] [Penguji 1: Didit Widiyanto] [Penguji 2: Hamonangan Kinantan P.] |
| Uncontrolled Keywords: | Internet of Things (IoT), Internet of Medical Things (IoMT), Distributed Denial of Service (DDoS), Machine Learning, Attack Detection. |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Fakultas Ilmu Komputer > Program Studi Informatika (S1) |
| Depositing User: | FIKRI AZHARI |
| Date Deposited: | 20 Aug 2025 06:58 |
| Last Modified: | 20 Aug 2025 06:58 |
| URI: | http://repository.upnvj.ac.id/id/eprint/36841 |
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