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All Journal Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) Journal of System and Computer Engineering
First Wanita, First
Program Studi Teknik Informatika STMIK AKBA
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management

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SISTEM PAKAR DETEKSI DINI PENYAKIT DENGAN GEJALA SESAK NAFAS MENGGUNAKAN METODE FORWARD CHAINING Wanita, First; Ashari, Ashari
Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) Vol 2, No 2 (2017): Vol 2, No 2 (2017): Prosiding Seminar Nasional Ilmu Komputer dan Teknologi Infor
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (441.879 KB)

Abstract

Penelitian ini dilakukan berdasarkan kebutuhan akan adanya alat bantu bagi masyarakat dalam deteksi dini penyakit dengan gejala sesak nafas pada manusia. Alat bantu tersebut berupa sistem pakar dengan memanfaatkan PHP dan Macromedia Dreamweaver, system pakar ini sebagai alat bantu untuk mendiagnosis dan juga memberikan solusi pengobatannya. Sistem pakar ini bisa dijalankan melalui konsultasi dengan menjawab setiap pertanyaan dengan ya atau tidak, semua jawaban disesuaikan dengan keluhan yang dirasakan oleh pasien. Metode inferensi yang digunakan adalah forward chaining. Keluaran dari sistem ini berupa nama penyakit, dan saran pengobatan.
Enhancing Intrusion Detection Using Random Forest and SMOTE on the NSL‑KDD Dataset Saputra, Febri Hidayat; Ilham, Ilham; Rizal, Muhammad; Wisda, Wisda; Wanita, First; Mursalim, Mursalim; Fadillah, Arif
Journal of System and Computer Engineering Vol 6 No 3 (2025): JSCE: July 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i3.2056

Abstract

Intrusion Detection Systems (IDS) play a crucial role in identifying suspicious activities on computer networks. However, a major challenge in developing machine learning-based IDS is the issue of class imbalance, where attacks—being minority classes—are often overlooked by classification models. This study aims to construct an intrusion detection system based on the Random Forest algorithm integrated with the Synthetic Minority Over-sampling Technique (SMOTE) to address this problem. The NSL-KDD dataset is used for evaluation, with the data split into 80% for training and 30% for testing. Experiments include Random Forest-based feature selection and performance evaluation using accuracy, precision, recall, and F1-score metrics. The results show that the Random Forest–SMOTE combination achieves an accuracy of 99.78%, precision of 99.70%, recall of 99.88%, and an F1-score of 99.79%. The confusion matrix indicates a very low rate of false positives and false negatives. Additionally, selecting the most influential features such as src_bytes and dst_bytes improves model efficiency. Thus, the integration of Random Forest and SMOTE proves to be effective in enhancing detection sensitivity toward attacks without compromising model precision. This approach offers a significant contribution to the development of adaptive, accurate, and deployable IDS in real-world network environments.
Co-Authors Arif Fadillah Ilham Ilham Mochammad Imron Awalludin MUHAMMAD RIZAL Mursalim Mursalim Saputra, Febri Hidayat Wisda, Wisda