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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Teknik Informatika Journal of Computers and Digital Business
Kautsar, Afthar
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Implementation of the Advanced Encryption Standard (AES) Algorithm and Bit Plane Complexity Segmentation (BPCS) Steganography Technique for Enhancing Text File Security Kautsar, Afthar; Ikhsan, Muhammad
Sistemasi: Jurnal Sistem Informasi Vol 14, No 2 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i2.5097

Abstract

In today's digital era, data security has become a crucial aspect due to the vast amount of information being transmitted and stored electronically. The Advanced Encryption Standard (AES) is one of the most widely used encryption algorithms because of its high level of security. However, data that is merely encrypted still risks detection; therefore, the Bit-Plane Complexity Segmentation (BPCS) steganography technique is employed to hide data within digital images, making it more difficult to identify. This study aims to enhance data security by combining AES encryption and BPCS steganography. The process begins by encrypting text data using AES-128, ensuring that the information cannot be accessed without a valid key. Next, the resulting ciphertext is embedded into a digital image using the BPCS technique, which selects bit-planes with high complexity for insertion without significantly altering the visual appearance of the image. Testing is conducted by measuring the accuracy of data extraction and the impact on image quality. The results indicate that this method effectively maintains data security and confidentiality. The data embedded in the image can be extracted with a success rate of 100%, while the image quality remains intact without noticeable changes. Thus, the combination of AES and BPCS provides a dual layer of protection: encryption ensures that the data cannot be read, while steganography conceals the existence of the data from unauthorized parties. This method can be applied in various data security scenarios, both for personal and organizational purposes.
DNA Sequence Classification Using Machine Learning Models Based on k-mer Features Kautsar, Afthar
Journal of Computers and Digital Business Vol. 4 No. 2 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i2.762

Abstract

Cell-free DNA (cfDNA) has emerged as a promising biomarker in various clinical applications, particularly in cancer detection, prenatal diagnostics, and disease monitoring. Accurate classification of cfDNA sequences is crucial for improving diagnostic reliability and enabling timely clinical decisions. This study investigates the application of machine learning models—Decision Tree (DT), Support Vector Machine (SVM), and Deep Neural Network (DNN)—for classifying cfDNA sequences using k-mer-based feature extraction, with k set to 3. A total of 3,000 DNA sequences comprising both normal and tumor-derived samples were transformed into numerical feature vectors based on the frequency of 3-mer patterns. The models were trained and evaluated using standard metrics including accuracy, precision, recall, and F1-score. Experimental results demonstrate that the DNN model achieved the highest classification performance, effectively distinguishing between normal and tumor cfDNA. In contrast, the DT and SVM models exhibited relatively lower performance, particularly in identifying normal sequences. The study also addresses challenges such as class imbalance and limitations of simple k-mer representations. These findings highlight the potential of deep learning approaches in improving cfDNA sequence analysis and open avenues for future research using more complex models, larger datasets, and feature engineering techniques to enhance classification accuracy and clinical applicability.
Penerapan Teknologi Mikrotik Dalam Jaringan Point-To-Point Untuk Meningkatkan Kinerja Infrastruktur Jaringan Kautsar, Afthar; Yulistia , Anita; Ritonga , Meini Syakinah; Armansyah
JEKIN - Jurnal Teknik Informatika Vol. 4 No. 3 (2024)
Publisher : Yayasan Rahmatan Fidunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/jekin.v4i3.729

Abstract

Infrastruktur jaringan yang baik dapat meningkatkan kelancaran operasional dan produktivitas suatu organisasi. Penggunaan perangkat MikroTik pada infrastruktur ini berperan penting dalam memberikan layanan komunikasi dan informasi. Optimalisasi jaringan point-to-point dengan MikroTik memfasilitasi koneksi langsung antara dua lokasi jarak jauh, meningkatkan efisiensi dan kecepatan transfer data. Penelitian ini bertujuan untuk meningkatkan kinerja infrastruktur jaringan. Metode yang digunakan antara lain analisis permasalahan, pengumpulan data, implementasi, pengujian. Hasil penelitian menunjukkan peningkatan kinerja infrastruktur jaringan yang signifikan terlihat dari peningkatan kecepatan transfer data, penurunan latensi, dan stabilitas koneksi pada Dinas Komunikasi dan Informatika Kota Medan. Penelitian ini memberikan solusi efektif untuk mengatasi permasalahan konektivitas dan meningkatkan kinerja jaringan.
Applying Random Forest Algorithm for Phishing URL Identification Kautsar, Afthar; Aida, Maghfira; Yulistia , Anita
Journal of Computers and Digital Business Vol. 4 No. 3 (2025)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v4i3.782

Abstract

Phishing attacks continue to be one of the most pervasive cybersecurity threats, particularly through malicious URLs designed to mimic legitimate websites and steal sensitive user information. To address this challenge, this study employs the Random Forest algorithm for automated phishing URL detection using a publicly available dataset from Kaggle. The dataset contains diverse structural, technical, and popularity-based features that capture behavioral and lexical characteristics of each URL. Following data preprocessing and an 80/20 train–test split, the Random Forest classifier achieved strong predictive performance, attaining an accuracy of 94.94%, a precision of 95.19%, and a recall of 96.94%. The model further demonstrated robust classification capability with an F1-score of 96.06% and an ROC AUC value of 0.985, indicating excellent discrimination between phishing and legitimate URLs. Feature importance analysis shows that factors such as the URL’s presence in Google’s index, page rank metrics, and specific structural patterns significantly influence prediction outcomes. Additionally, performance visualizations including ROC and Precision–Recall curves reinforce the model’s reliability and stability. Overall, the findings suggest that Random Forest provides an effective and efficient solution for phishing URL detection, offering promising potential for integration into real-world cybersecurity systems.
Co-Authors Aida, Maghfira Armansyah Muhammad Ikhsan Ritonga , Meini Syakinah Yulistia , Anita