Syntax : Jurnal Informatika

Comparison Email Spam detection vectorizing using bag of word, TFIDF and Word2Vec in Multinomial Naïve Bayes

2024-05-15T07:21:10+07:00

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.

Sari, Eka DESAIN SISTEM PEMINJAMAN PERALATAN KANTOR BERBASIS WEBSITE

2024-06-13T11:52:19+07:00

Asset management in the company is an important part of ensuring smooth operations and supporting employee performance. However, the lack of a structured lending system often causes problems such as damage, loss of assets, difficulties in monitoring and recording. Those who still use Microsoft Excel are also very vulnerable to input errors and data loss. Therefore this study aims to design and build a website-based information system for borrowing office tools assets that can simplify the process of borrowing assets, improve monitoring of office tools assets, and provide accurate reports regarding borrowing office tools. The system development method used in this study uses the waterfall method, and for system design using UML (Unified Modeling Language). The final results of this study are a website-based office tools asset lending system using the Laravel framework which can facilitate monitoring of the condition and availability of assets, reduce the risk of data loss, and increase the accuracy of reporting.

Penerapan Algoritma Apriori untuk Memprediksi Pembayaran UKT

2024-05-05T14:34:46+07:00

Penelitian ini menerapkan algoritma Apriori untuk memprediksi hasil anilsis pola asosiasi pembayaran cicilan uang kuliah di Universitas Buana Perjuangan Karawang. Aturan asosiasi menunjukkan bahwa pembayaran Cicilan 3 memiliki dampak besar terhadap Cicilan 4, dengan tingkat support sebesar 84.60% dan confidence sebesar 93.47%. Ketergantungan positif antara Cicilan 2 dan Cicilan 3 dengan Cicilan 4 juga teridentifikasi dengan nilai support sebesar 84.57% dan nilai confidence sebesar 94.03%. Rekomendasi kebijakan mencakup penggabungan paket pembayaran pada Cicilan 3 dan Cicilan 4 serta insentif pembayaran lebih awal. Pemodelan menggunakan algoritma Apriori dengan implementasi Python dan Google Colaboratory.