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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Ilmiah Sistem Informasi
Imam Sutoyo
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Library & Information Science Other

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Brute-Force Attack Detection on Computer Networks Using Artificial Neural Network Ikhtiar Adli Wicaksono; Muhammad Iqbal Maulana; Bagus Nurrahman; Syifa Nur Rakhmah; Findi Ayu Sariasih; Imam Sutoyo
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1804

Abstract

This research aims to develop a brute-force attack detection system on computer networks using the Artificial Neural Network (ANN) algorithm. This security problem is crucial, especially in the banking sector because it can threaten login systems and sensitive customer data. The research methods include data cleansing, feature selection using the Wrapper method, ANN model training, and performance evaluation using datasets from Kaggle which include four classes of network traffic, namely Normal, Brute-force FTP, Brute-force SSH, and Web Attack Brute-force. The test results showed that the ANN model achieved an accuracy of 95%, precision of 91%, and the best performance in the Brute-force FTP class with an accuracy of 98.3%. This system has proven to be effective in detecting brute-force attack patterns and can improve the security of banking networks adaptively. This research broadens the insights of the application of ANN in network security and provides a basis for the development of systems that are more responsive to cyber threats.
Sistem Klasifikasi Citra AI Dan Human Menggunakan CNN Multi-Modal Berbasis Web Ardiyansyah, Oscar; Muhammad ‘Aziz Hidayatullah; Derrylen Fernanda; Syifa Nur Rakhmah; Findi Ayu Sariasih; Imam Sutoyo
Jurnal Ilmiah Sistem Informasi (JISI) Vol. 5 No. 1 (2026): MARET
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jisi.v5i1.10543

Abstract

Penelitian ini mengembangkan sistem klasifikasi citra berbasis web menggunakan arsitektur Convolutional Neural Network (CNN) multi-modal untuk membedakan citra buatan manusia dan hasil generasi AI. Sistem yang diajukan menggabungkan tiga jenis input, yaitu citra asli, Error Level Analysis (ELA), dan Residual Noise Map (RDM) guna memperkaya representasi fitur pada proses klasifikasi. Model dibangun dengan backbone VGG16 pre-trained dan diuji pada 2.102 data citra yang terbagi seimbang antara dua kelas. Hasil eksperimen menunjukkan akurasi validasi sebesar 91% dan nilai macro F1-score sebesar 0,91, mengungguli pendekatan unimodal pada tugas serupa. Sistem diimplementasikan menggunakan framework Flask yang memungkinkan uji keaslian citra secara real-time, sehingga sangat relevan diterapkan di bidang forensik digital, verifikasi hak cipta, dan mitigasi disinformasi visual.
Co-Authors Ardiyansyah, Oscar Bagus Nurrahman Derrylen Fernanda Findi Ayu Sariasih Ikhtiar Adli Wicaksono Muhammad Iqbal Maulana Muhammad ‘Aziz Hidayatullah Syifa Nur Rakhmah, Syifa Nur