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All Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi Innovation in Research of Informatics (INNOVATICS)
Larasati, Harashta Tatimma
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Advanced detection Denial of Service attack in the Internet of Things network based on MQTT protocol using fuzzy logic Budiana, Mochamad Soebagja; Negara, Ridha Muldina; Irawan, Arif Indra; Larasati, Harashta Tatimma
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 7, No 2 (2021): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v7i2.2340

Abstract

Message Queuing Telemetry Transport (MQTT) is one of the popular protocols used on the Internet of Things (IoT) networks because of its lightweight nature. With the increasing number of devices connected to the internet, the number of cybercrimes on IoT networks will increase. One of the most popular attacks is the Denial of Service (DoS) attack. Standard security on MQTT uses SSL/TLS, but SSL/TLS is computationally wasteful for low-powered devices. The use of fuzzy logic algorithms with the Intrusion Detection System (IDS) scheme is suitable for detecting DoS because of its simple nature. This paper uses a fuzzy logic algorithm embedded in a node to detect DoS in the MQTT protocol with feature selection nodes. This paper's contribution is that the nodes feature selection used will monitor SUBSCRIBE and SUBACK traffic and provide this information to fuzzy input nodes to detect DoS attacks. Fuzzy performance evaluation is measured against changes in the number of nodes and attack intervals. The results obtained are that the more the number of nodes and the higher the traffic intensity, the fuzzy performance will decrease, and vice versa. However, the number of nodes and traffic intensity will affect fuzzy performance.
Enhancing Maintenance Efficiency Through K-Means Clustering at PT Semen Indonesia Alviano, Muhammad Fadhil; Alifah, Amalia Nur; Ardhani, Calista Ghea; Raditya, Helga Fadhil; Larasati, Harashta Tatimma
Innovation in Research of Informatics (Innovatics) Vol 6, No 2 (2024): September 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i2.12520

Abstract

PT Semen Indonesia, an industrial company based in Gresik, East Java, is committed to enhancing operational efficiency and managing maintenance costs effectively. By analyzing patterns in maintenance frequency, total costs, and maintenance duration across their various plants, the company can identify work units that require more intensive attention or that can be optimized for greater efficiency. To achieve this, PT Semen Indonesia employs K-Means clustering analysis to gain deeper insights into the maintenance data, identifying patterns that can help improve operational efficiency and develop more targeted maintenance strategies based on the identified clusters. The clustering of planner groups is carried out using variables such as the number of maintenance activities, total costs, and duration of maintenance tasks. As a result of the K-Means clustering, the planner groups have been divided into two clusters: Cluster 1, which consists of planner groups that perform more efficiently, and Cluster 2, which includes those with less efficient performance. Based on these clustering results, PT Semen Indonesia should conduct further evaluation or review of the planner groups in Cluster 2.
Co-Authors Alifah, Amalia Nur Alviano, Muhammad Fadhil Ardhani, Calista Ghea Arif Indra Irawan Budiana, Mochamad Soebagja Raditya, Helga Fadhil Ridha Muldina Negara