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All Journal ComEngApp : Computer Engineering and Applications Journal JSI: Jurnal Sistem Informasi (E-Journal)
Septiani Kusuma Ningrum
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science Other

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Analisis Perbandingan Query Response Time MariaDB Pada Sistem Operasi Kali Linux dan Ubuntu Fatimah, Aisyah; Afifah, Nurul; Septiani Kusuma Ningrum
JSI: Jurnal Sistem Informasi (E-Journal) Vol 17 No 1 (2025): Vol 17, No 1 (2025)
Publisher : Jurusan Sistem Informasi Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/jsi.v17i1.230

Abstract

Data is a vital resource for organizations that demand unparalleled access speeds and adequate levels of security. Critical components in the data need to be managed effectively and efficiently to enable timely decision-making. This study proposed a comparative analysis of query execution time using MariaDB Database Management System (DBMS) on Kali Linux and Ubuntu operating systems. Researchers identified response times for select, update, and delete operations on variations in data size. This experiment was carried out three times to ensure the consistency of the results. As a result, Ubuntu exhibits a more stable pattern of increasing response time, although it is slower than Kali Linux. Ubuntu performs better in terms of scalability, especially when handling large datasets. Based on this study, the selection of an operating system for database management not only be based solely on query execution speed, but also consider other important factors, such as the operating system's security level, ease of use, and user preferences.
DDoS Detection in Smart Home IoT Networks: An Experimental Comparison of MLP and AE-MLP with Correlation-Based Feature Selection Nurul Afifah; Septiani Kusuma Ningrum; RA Siti Safa Marefa; Jihan Badiatus Shaliha; Hikmah Maharani; Ilma Sari; Andi Daniella
Computer Engineering and Applications Journal Vol. 15 No. 3 (2026)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v15i3.1422

Abstract

As the use of the Internet of Things (IoT) increases, the number of smart homes is rapidly increasing. But the increase in the number of connected devices increases the attack surface, making them vulnerable to Distributed Denial-of-Service (DDoS) attacks. The Autoencoder-Multilayer Perceptron (AE-MLP) is a hybrid deep learning model for DDoS attack detection in the smart home context. The Multilayer Perceptron (MLP) is proposed as the baseline model. The dataset is collected from a testbed of several IoT devices deployed in smart homes. Flow based features were extracted from network traffic in PCAP format using the CICFlowMeter. Data cleaning, feature selection with the correlation matrix and Min-Max normalization were the steps of data preparation process. We tried different data partition, optimizer, learning rate and epoch settings. Results showed that the performance of both models was greatly improved with feature selection. Before feature selection, the accuracy and F1-Score of the AE-MLP are 99.2% and 99.3% whereas the accuracy and F1-Score of the MLP are 99.5% and approximately 68%. The results indicate that feature selection plays a significant role in reducing noise and enhancing the performance of DDoS detection in smart home environments.
Co-Authors Andi Daniella Fatimah, Aisyah Hikmah Maharani Ilma Sari Jihan Badiatus Shaliha Muhammad Afif RA Siti Safa Marefa