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All Journal Jurnal Transformatika International Journal of Informatics Engineering and Computing
Mohammad Nur Fawaiq
Universitas Amikom Yogyakarta
Computer Science & IT

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Intrusion Detection System in Network Security Using Naive Bayes and Support Vector Machine Selamet Riadi; Mohammad Nur Fawaiq
International Journal of Informatics Engineering and Computing Vol. 1 No. 1 (2024): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/ijimatic.v1.i1.24

Abstract

An Intrusion Detection System (IDS) is designed to detect suspicious activities or security threats within a network, necessitating continuous advancements in the field. Both implementation techniques and algorithmic research play pivotal roles in enhancing IDS capabilities. This study addresses this need by focusing on the implementation and comparison of two prominent classification models: Naive Bayes and Support Vector Machine (SVM). The study is centered within the domain of Intrusion Detection System (IDS) tailored for network security. In the course of this research, a relevant dataset sourced from Kaggle serves as the foundation for training and testing both classification models. The findings of this study underscore the models' efficacy in intrusion detection. The SVM model, in particular, emerges as a standout performer, showcasing an accuracy rate that approaches 100%, thus exemplifying its potential in real-world scenarios. Meanwhile, the Naive Bayes model delivers commendable accuracy, surpassing 88%. This investigation not only contributes to the advancement of intrusion detection methodologies but also highlights the viability of these classification models for bolstering network security against the ever-evolving threat landscape.
Optimization of the Stemming Technique on Text Preprocessing President 3 Periods Topic M. Ulil Albab; Yohana Karuniawati P.; Mohammad Nur Fawaiq
Jurnal Transformatika Vol. 20 No. 2 (2023): January 2023
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v20i2.5374

Abstract

Stemming merupakan suatu proses untuk menemukan kata dasar dari sebuah kata. Penelitian ini bertujuan untuk melakukan tahapan text pre-processing pada data twitter yang menyebutkan topik Presiden 3 Periode , yaitu sebanyak 797 data yang didapatkan dari crawling twitter mulai tanggal 15 April 2022 sampai dengan 30 April 2022, sekaligus melakukan optimasi salah satu teknik stemming terhadap teks berbahasa Indonesia yang memang belum banyak dilakukan. Banyaknya kata yang diolah sebanyak 9401 kata. Optimasi yang dilakukan yaitu dengan memodifikasi kamus bahasa dan menambahkan kata-kata yang dimasukkan dalam stopword sehingga dapat menghasilkan jumlah kata ter-stemming yang semakin meningkat. Sebelum dilakukan optimasi, prosentase keberhasilan stemming mencapai 95,86%, setelah dilakukan optimasi meningkat menjadi 99,93%.
Co-Authors M. Ulil Albab Selamet Riadi Yohana Karuniawati P.