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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) CAUCHY: Jurnal Matematika Murni dan Aplikasi Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) Jurnal Varian Prosiding Seminar Nasional Official Statistics Jurnal Riset Mahasiswa Matematika Journal of Research on Community Engagement
Ria Dhea Layla Nur Karisma
Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences Other

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Classification Cyber Harassment on Twitter using Multinomial Naïve Bayes Karisma, Ria Dhea Layla Nur
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 17, No 2 (2025): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v17i2.29866

Abstract

Multinomial Naïve Bayes is a classification method in Naïve Bayes Classifier based on Bayes Theorem and multinomial distribution. This method works optimally in the multiclass classification of text data. Furthermore, it calculates the probability of occurrence of each word by multiplying the class prior probability by the likelihood value of the occurrence of each word in each class. The phenomenon of Cyber Harassment is defined as the behavior of utilizing technology to harm or humiliate people, which has four types of behavior, namely Physical Threats, Purposeful Embarrassment, Racist, and Sexual Harassment. The number of Cyber Harassment cases always increases every year even though the government has made policies to deal with Cyber Harassment cases. The study aims to classify results accurately regarding the types of Cyber Harassment on Twitter using the Multinomial Naïve Bayes method. The classification results obtained are 20 tweets classified as Physical Threats, 10 tweets classified as Purposeful Embarrassment, 25 tweets classified as Racist, and 22 tweets classified as Sexual Harassment. The accuracy of classification of types of Cyber Harassment on Twitter social media using Multinomial Naïve Bayes is 77%, and the results of the model performance test with K-fold cross-validation is 76.21%, showing that the Multinomial Naïve Bayes method can classify the types of Cyber Harassment on Twitter social media is well effective.
Analyzing Lightning Strike Susceptibility Using the Elliptical Fitting Method with a Principal Component Analysis Approach Lovytaji, Helmalia A.; Rozikan, Rozikan; Kuncoro, Djati C.; Karisma, Ria Dhea Layla Nur
Jurnal Varian Vol. 8 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.3183

Abstract

Lightning is a high-current discharge that occurs in Cumulonimbus clouds, with CG (Cloud to Ground)lightning strikes posing significant dangers, especially to human life. Pasuruan, located in the highlandsbetween mountains and the ocean in Indonesia, is particularly vulnerable to such strikes. This studyaims to mitigate the impact of lightning strikes, particularly in industrial areas like Pasuruan, by delineating lightning-prone areas using a sophisticated methodological approach. Our research employs arobust Ellipse Fitting Method, parameterized with Principal Component Analysis (PCA), to accuratelydefine the boundaries of these high-risk zones. The Ellipse Fitting Method, which involves formingan ellipse from the intersection of a plane and a cone, uses five key parameters: a center point, twovertex points, and two focus points. PCA is then applied to these parameters to determine the ellipse’sconfiguration, with the center point derived from the mean of all data points. The major and minoraxes are defined by the first and second eigenvalues of the principal components, respectively. The sizeof the ellipse correlates with the confidence level, with higher confidence resulting in a larger ellipse.The result of integrating these advanced techniques is the generation of two PCA models from datacollected across 28 sub-districts in Pasuruan, with findings indicating a high level of vulnerability inLumbang District and a moderate level of risk in Gempol District. This methodological framework notonly enhances the precision in identifying lightning-prone areas but also provides a scalable approachfor similar studies in other regions. Suggestion for the further research are to overcome extreme pointsor extreme points in the PCA confidence ellipse such as MVEE.
Co-Authors A Rosa, Ramadani Ainindita Aghniacakti Bambang Widjanarko Otok Erna Herawati, Erna Evawati Alisah Fachreza, Putri Aulia Hafildah, Ummi Ismiarti, Dewi Juhari Juhari, Juhari Kuncoro, Djati C. Lovytaji, Helmalia A. Muhammad Khudzaifah Pamungkas, Syahrul Aziz Rozikan, Rozikan Sa'adati, Hawzah Syafika, Vicky Alfina Nur Ummi Hafildah Usman Pagalay