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All Journal International Journal Software Engineering and Computer Science (IJSECS) INTERNATIONAL JOURNAL OF EDUCATION, INFORMATION TECHNOLOGY, AND OTHERS
Apriansyah, Ferryma Arba
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Computer Science & IT Education Languange, Linguistic, Communication & Media Social Sciences

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Optimization of K Value in KNN Algorithm for Spam and HAM Classification in SMS Texts Apriansyah, Ferryma Arba; Hermawan, Arief; Avianto, Donny
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 2 (2024): AUGUST 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i2.2681

Abstract

Spam refers to the unsolicited and repetitive sending of messages to others via electronic devices without their consent. This activity, commonly known as spamming, is typically carried out by individuals referred to as spammers. SMS spam, which often originates from unknown sources, frequently contains advertisements, phishing attempts, scams, and even malware. Such spam messages can be pervasive, affecting almost all mobile phone numbers, thereby causing significant disruptions to communication by delivering irrelevant content. The persistent nature of spam messages underscores the need for effective filtering mechanisms. This study investigates the application of the K-Nearest Neighbors (KNN) algorithm for classifying SMS messages as either spam or non-spam (ham). The findings demonstrate that KNN, when optimized through various methods for determining the appropriate value of K, can achieve an impressive average accuracy of 99.16% in classifying SMS spam. This high level of accuracy indicates that KNN is a reliable method for spam detection.
Literature Review: Threat of Artificial Intelligence (Ai) Cyber Attacks on Data Security Apriansyah, Ferryma Arba; Ujianto, Erik Iman Heri; Rianto, Rianto
International Journal of Education, Information Technology, and Others Vol 7 No 2 (2024): International Journal of Education, information technology   and others (IJEIT)
Publisher : Peneliti.net

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.10968289

Abstract

The purpose of this study is to present an overview of the approaches and areas of concentration in data security identification. The report analyzes AI cyberattacks on Indonesian data security through a review of the literature. In order to find relevant papers, search terms like "Cyber Attacks," "Artificial Intelligence," and "Data Security" were used in this study. 80 of the 1,057 studies that were found between 2019 and 2023 were employed in this analysis. The outcomes of this evaluation of the literature can help future researchers conduct more in-depth studies on data security. A number of factors, such as extending the study's purview, incorporating a wider range of topics, and including people who may provide additional insights, might be taken into account for future data security research.
Co-Authors Donny Avianto Erik Iman Heri Ujianto Rianto Rianto