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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Journal of Applied Computer Science and Technology (JACOST)
Yulvida, Donata
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Computer Science & IT Education

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Pendekatan Metode Ensemble Learning untuk Deteksi Serangan DDoS menggunakan Soft Voting Classifier Joses, Steven; Quinevera, Stefanie; Mardianto, Ricky; Yulvida, Donata; Shiddiqi, Ary Mazharuddin
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 10, No 1 (2024): Volume 10 No 1
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v10i1.73241

Abstract

Serangan Distributed Denial of Service (DDoS) adalah jenis serangan yang kompleks dan sering melibatkan berbagai pola lalu lintas jaringan yang berbeda. Model soft voting classifier dapat menggabungkan hasil dari beberapa model klasifikasi yang berbeda, sehingga meningkatkan kemampuan untuk mendeteksi dan mengatasi serangan DDoS dengan berbagai pola dan skenario yang berbeda. Dengan memanfaatkan model soft voting classifier berdasarkan fitur-fitur yang mendukung, dapat meningkatkan ketahanan sistem terhadap serangan DDoS dengan lebih efektif, mengurangi dampaknya, dan memastikan ketersediaan sumber daya jaringan dan layanan internet bagi pengguna yang mengaksesnya. Data yang digunakan dalam penelitian ini menggunakan dataset DDoS yang diperoleh dari situs kaggle.com. Dataset ini memiliki 23 atribut termasuk satu variabel output dengan jumlah data sebanyak 104.245 record. Dilakukan preprocessing pada dataset kemudian diklasifikasi menggunakan lima model machine learning dan sepuluh ensemble learning method untuk mendapatkan hasil akurasi tertinggi. Hasil pengujian menunjukkan bahwa ensemble method sangat optimal dalam mendeteksi serangan DDoS baik menggunakan fitur berdasarkan Information Gain maupun menggunakan fitur berdasarkan Gain Ratio dibandingkan dengan metode machine learning tunggal.
Pendekatan Metode Ensemble Learning untuk Prakiraan Cuaca menggunakan Soft Voting Classifier Steven Joses; Yulvida, Donata; Rochimah, Siti
Journal of Applied Computer Science and Technology Vol 5 No 1 (2024): Juni 2024
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v5i1.741

Abstract

Weather conditions are one of the crucial factors that need attention. Changes in weather conditions significantly impact various activities. Weather condition changes are determined by numerous factors, often occurring within a relatively short period in the atmosphere, such as pressure, wind speed, rainfall, temperature, and other atmospheric phenomena. Issues in weather forecasting arise due to several factors, namely the fluctuating atmospheric conditions. This research proposes the development of a weather forecasting model using the ensemble learning method approach. The weather data used consist of 33746 records with attributes used after preprocessing, namely Temperature, Dew Point, Humidity, Wind Speed, Wind Gust, Pressure, Precipitation, and Condition. Testing in this research employs several single-machine learning methods such as K-Nearest Neighbor (KNN), Logistic Regression, Random Forest, Naive Bayes, and Multi-Layer Perceptron. The Naive Bayes method using default parameters achieves a high accuracy of 99.00%. In the ensemble method, combinations of three methods exhibit excellent accuracy for all combinations. The best combination methods are found in the Soft Voting Classifier method (Random Forest, MLP, Naive Bayes), Soft Voting Classifier (Logistic Regression, MLP, Naive Bayes), and Soft Voting Classifier (Random Forest, KNN, Naive Bayes) with an accuracy of 99.03%.
Co-Authors Ary Mazharuddin Shiddiqi Joses, Steven Mardianto, Ricky Quinevera, Stefanie Siti Rochimah Steven Joses