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All Journal Paradigma INTI Nusa Mandiri Jurnal Infortech Infotek : Jurnal Informatika dan Teknologi Jurnal Media Informatika Informatics and Computer Engineering Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Imam Sutoyo
Universitas Bina Sarana Informatika
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Library & Information Science

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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 Prediksi Kualitas Air Konsumsi Machine Learning Menggunakan Algoritma Random Forest Fredyo Eltanin Lumban Raja; Daniel Purba; Syifa Nur Rakhmah; Findi Ayu Sariasih; Imam Sutoyo
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 1 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i1.33035

Abstract

This study aims to design and implement a clean water quality prediction system based on machine learning using the Random Forest algorithm. The background of this research is the limited public access to fast information regarding water feasibility, while laboratory testing requires significant time and cost. The data used is synthetic data constructed based on the value ranges and threshold limits of water quality in SNI 3553:2015, SNI 3553:2023, and the Ministry of Health Regulation No. 2 of 2023, so that it remains representative and aligned with real conditions. This dataset was created because field data is difficult to obtain completely and in a standardized form, but it still imitates real condition variations according to national standards, with feasibility labels determined based on official regulations. The system development uses the Agile method through the stages of dataset creation, preprocessing, training, evaluation, and application implementation. The Random Forest model is used to classify water into suitable, moderately suitable, and unsuitable categories. The test results show that the model with three physical parameters achieved an accuracy of 96.67%, while the model with ten chemical parameters achieved an accuracy of 100%, confirming that adding more parameters can improve prediction accuracy. This system is expected to help the public and environmental officers in conducting an initial assessment of water quality quickly before laboratory testing and can still be further developed to become more applicable in various regions.
Sistem Prediksi Kecelakaan Lalu Lintas Menggunakan Deep Learning Convolutional Neural Network (CNN) untuk Pencegahan Efektif: Indonesia Sausan Faza; Rafika Puteri Wulandari; Findi Ayu Sariasih; Imam Sutoyo; Syifa Nur Rakhmah
Jurnal Media Informatika Vol. 7 No. 1 (2026): Edisi Januari - Februari
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jumin.v7i1.7659

Abstract

Kecelakaan lalu lintas merupakan masalah global yang memerlukan pendekatan inovatif berbasis teknologi visi komputer. Penelitian ini bertujuan mengembangkan aplikasi web yang mampu mengidentifikasi probabilitas terjadinya kecelakaan kendaraan menggunakan citra dashcam dengan pendekatan deep learning berbasis Convolutional Neural Network (CNN). Pengembangan aplikasi dilakukan menggunakan metodologi Feature Driven Development (FDD) untuk memastikan integrasi fitur yang modular dan berorientasi pada kebutuhan pengguna. Dataset yang digunakan bersumber dari Kaggle Car Crash Dataset sebanyak 10.000 citra yang dibagi menjadi data training (7.000), validation (1.500), dan testing (1.500). Hasil penelitian menunjukkan bahwa model CNN berhasil mencapai akurasi pelatihan sebesar 83,66% dan akurasi validasi sebesar 82,13%. Meskipun demikian, terdapat tantangan pada ketidakseimbangan data yang menyebabkan nilai recall untuk kelas kecelakaan berada di angka 37,79%. Implementasi sistem pada antarmuka web memungkinkan pengguna mengunggah citra dan menerima hasil klasifikasi risiko berupa "High Risk" atau "Low Risk" secara real-time. Sistem ini diharapkan dapat menjadi prototipe awal bagi pengembangan teknologi keselamatan berkendara yang lebih responsif di masa depan.
Co-Authors Anisya Septianur Astatia Hulu Bagus Nurrahman Daira Syahfitri Daniel Purba Dian Rahayuningtyas Elsya Bani Aulia Findi Ayu Sariasih Firyal Aufa Nabilah Fredyo Eltanin Lumban Raja Ikhtiar Adli Wicaksono Juan Sebastian Aimar KOMALASARI, YULI Mochamad Wahyudi Muhammad Iqbal Maulana Nugroho Fathul Aziz Rafika Puteri Wulandari Raihano Garcia Sausan Faza Surtika Ayumida Syifa Nur Rakhmah