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All Journal JRIIN :Jurnal Riset Informatika dan Inovasi Journal of Intelligent Systems and Information Technology
Putra, Dika Kurnia
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Teknik Forward Selection untuk Menentukan Kepala Instalasi Terbaik Di Rumah Sakit Dengan Menggunakan Metode Simple Additive Weighting (SAW) Aldo; Putra, Dika Kurnia; Al Banjary, Muhamad Abdullah Maulana Malik; Syahrul, Muhammad; Saifudin, Aries
Jurnal Riset Informatika dan Inovasi Vol 2 No 1 (2024): JRIIN: Jurnal Riset Informatika dan Inovasi
Publisher : shofanah Media Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pemilihan kepala instalasi yang tepat di rumah sakit memiliki dampak yang signifikan terhadap kinerja operasional dan kualitas layanan. Dalam konteks ini, penggunaan teknik seleksi maju menjadi penting untuk memilih kandidat terbaik yang sesuai dengan kebutuhan dan harapan organisasi. Penelitian ini mengusulkan penerapan Teknik Forward Selection dalam menentukan kepala instalasi terbaik di rumah sakit, dengan menggunakan Metode Simple Additive Weighting (SAW) sebagai alat bantu evaluasi. Teknik Forward Selection digunakan untuk memilih kandidat terbaik secara bertahap, dimulai dari kandidat awal dan menambahkan satu per satu kandidat lainnya berdasarkan nilai kriteria yang ditentukan. Metode SAW digunakan untuk menghitung nilai relatif dari setiap kandidat berdasarkan bobot yang telah ditetapkan untuk setiap kriteria. Melalui pendekatan ini, diharapkan dapat diidentifikasi kepala instalasi yang paling sesuai dengan kebutuhan dan prioritas rumah sakit.
Comparative Analysis of Machine Learning Algorithms in Detecting DDoS Attacks on CICIDS2017 Dataset Putra, Dika Kurnia; Pradana, Chandra Ari; Gilardin, Muhammad Hilal; Riyandi, Albert
Journal of Intelligent Systems and Information Technology Vol. 2 No. 2 (2025): July
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v2i2.182

Abstract

The rapid escalation of Distributed Denial of Service (DDoS) attacks has posed significant threats to global cybersecurity. This research presents a comparative analysis of three supervised machine learning algorithms—K-Nearest Neighbor (KNN), Decision Tree (DT), and Random Forest (RF)—in detecting DDoS attacks using the CICIDS2017 dataset. While many studies focus on broader intrusion detection, this study concentrates specifically on binary classification between benign and DDoS traffic. The CICIDS2017 dataset was chosen for its comprehensive and realistic representation of modern network traffic. The methodology involved preprocessing, training, and evaluating the models in Orange Data Mining using 10-fold cross-validation. Evaluation metrics included Accuracy, Precision, Recall, F1-Score, AUC, and Matthews Correlation Coefficient (MCC). Empirical results show that the Random Forest algorithm outperformed both KNN and Decision Tree, achieving perfect scores across all metrics (1.000). These findings highlight the robustness of ensemble learning in intrusion detection. The results have practical implications for the development of more reliable, efficient, and automated Intrusion Detection Systems (IDS), especially in real-world scenarios prone to volumetric DDoS attacks. Future work should explore multiclass classification and real-time implementation.
Co-Authors Al Banjary, Muhamad Abdullah Maulana Malik Aldo Aries Saifudin Gilardin, Muhammad Hilal Pradana, Chandra Ari Riyandi, Albert syahrul, muhammad