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All Journal ILKOMNIKA: Journal of Computer Science and Applied Informatics Internet of Things and Artificial Intelligence Journal
Galuh Muhammad Iman Akbar
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Pengukuran Kinerja Sekolah menggunakan Six Sigma Galuh Muhammad Iman Akbar
ILKOMNIKA: Journal of Computer Science and Applied Informatics Vol 5 No 2 (2023): Volume 5, Nomor 2, Agustus 2023
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v5i2.144

Abstract

Sekolah merupakan sarana belajar bagi siswa dan siswi dalam menimba ilmu dalam dunia pendidikan, maka pelu adanya pendukung sesuai dengan PP nomor 19 tahun 2005 tentang SNP. maka dari itu perlunya sebuah rancangan proses pada organisasi sekolah dengan menerapkan implementasi Six Sigma DMAIC (Define Measure Analysis Improve Control) yang digunakan untuk mengukur kinerja sekolah. dengan bagitu hasilnya dapat digunakan untuk meningkatkan tingkat kelulusan siswa dan mengidentifikasi untuk pengembangan kinerja sekolah lebih anjut
Detection of Bruteforce Attacks on the MQTT Protocol Using Random Forest Algorithm Galuh Muhammad Iman Akbar; Mokhamad Amin Hariyadi; Ajib Hanani
Internet of Things and Artificial Intelligence Journal Vol. 3 No. 3 (2023): Vol. 3 No. 3 (2023): Volume 3 Issue 3, 2023 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v3i3.630

Abstract

Bruteforce is a hacking technique that launches an attack by guessing the username and password of the system that is the target of the attack. The Bruteforce attack on the MQTT protocol is an attack that often occurs on the IoT, so it is necessary to detect attacks on the MQTT protocol to find out normal traffic and brute force traffic. Random Forest was chosen because this method can classify a lot of data in a relatively short time, and the results from Random Forest can improve accuracy and prevent overfitting in the data classification process. This study uses two types of data: primary data from the hacking environment lab and secondary data from the IEEE Data Port MQTT-IOT-IDS2020 dataset. Trials on primary data and the results obtained are accuracy of 99.55%, precision of 100%, recall of 99.54%, and f-measure of 99.77%, the duration needed to get these results with 1796 data lines, i.e., for 0 seconds. As for the secondary data, the researcher obtained an accuracy of 99.77%, a precision of 100%, a recall of 99.43%, and an f-measure of 98.71%, the duration required to obtain these results with 85002 data lines, i.e., for 62 seconds.
Co-Authors Ajib Hanani Mokhamad Amin Hariyadi