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All Journal Syntax Jurnal Informatika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Arifiandy, Rony
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Computer Science & IT

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Comparison Email Spam detection vectorizing using bag of word, TFIDF and Word2Vec in Multinomial Naïve Bayes Arifiandy, Rony; Fahmi, Hasanul
SYNTAX Jurnal Informatika Vol 13 No 01 (2024): Mei 2024
Publisher : Universitas Singaperbangsa Karawang

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Abstract

Email has become very popular among people nowadays. In fact, it the cheapest, popular and fastest means of communication in recent times. Email also has become official communication media in business area. The popularity of email is also used by irresponsible people as a medium for sending fake news, as a medium for fraud and so on. We call this kind email as spam email. There are dangerous and not dangerous spam email. We will focus on detection dangerous spam email, there are 2 type dangerous spam email. The first is email Phishing: Phishing is a term used to define fraudulent practices in which spammers try to trick victims. This can be detrimental to the person who receives these emails. And this kind email may deliver massively and very disturbing the email user. This research will try to find better preprocessing text technique to support the Multinomial Naïve Bayes algorithm with 3 class (ham, phishing and fraud) to classify kind of email, it is hoped that it can help users more accurately classify spam emails. To be able to do that, in preprocessing data we need to vectorizing body email so machine learning can make calculation. Vectorization enables the machines to understand the textual contents by converting them into meaningful numerical representations. The effectiveness of various text vectorization methods, namely the bag of word, TF-IDF and word2vec are investigated for email spam detection using the Multinomial Naïve Bayes. The paper presents the comparative analysis of different vectorization methods on spam email dataset. This paper will give the best vectorization with Multinomial Naive Bayes.
Analisis Faktor yang Berkontribusi Terhadap Pengurangan Karyawan Berdasarkan Clustering Self-Organizing Map Arifiandy, Rony; Herry Utomo, Wiranto
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 2 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i2.11224

Abstract

Employee turnover can disrupt the organization's operations and more or less cause losses to the business. Therefore, it is important to understand the causal factors so that organizations can take anticipatory action. Identify reasons employees leave their jobs is crucial for both employers and policy makers, especially when the goal is to prevent this from happening. Data on the causes of employee turnover is complex data that can have many dimensions, so a certain method is needed to analyze it. In this research, an analysis of data on the causes of employee turnover with 10 dimensions will be carried out using the Self Organizing Map (SOM) method. The Self-Organizing Map (SOM) is a technique for clustering and visualizing high-dimensional data by mapping it to a two-dimensional space while preserving the data's topological structure. This neural network-based method ensures that similar data points remain close to each other in the resulting 2D representation. SOM will cluster the data into several uniform groups. The results of this SOM grouping will be assessed with the Silhouette score, Dunn index and Connectivity value to determine how uniform the grouping is. Hopefully that by using the results of this SOM grouping, it shows that the clusters formed are very good and the data is clearly grouped. Therefore, we can analyze these groups with more accurate results.
Co-Authors Fahmi, Hasanul Herry Utomo, Wiranto