Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Ilmu Komputer
Quraisy, Muhamad
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Jurnal Ilmu Komputer

 1 
Rancang Bangun Private Server Menggunakan Platform Proxmox dan Penerapan Zero Trust Model dengan Cloudflare Yulianto, Bimo Tri; Quraisy, Muhamad; Daulay, Anggriyana; Daulay, Anggriyani; Sari, Ayu Puspita
Jurnal Ilmu Komputer Vol 1 No 2 (2023): Jurnal Ilmu Komputer (Edisi Desember 2023)
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This implementation emphasizes using Proxmox as the primary virtualization platform combined with the Zero Trust security concept, where each access request is rigorously assessed before being permitted. Integration with Cloudflare provides an additional layer of security through features such as web application firewall (WAF), DDoS protection, and strict access control. By adopting the Zero Trust model and leveraging Cloudflare services, the server infrastructure becomes more resilient against current cyber threats. The meticulous integration between Proxmox and Cloudflare offers a high level of security at every server access point, creating a reliable and safeguarded environment for IT services.
Analisis Sentimen Ulasan Aplikasi MyUnpam di Google Play Store Menggunakan Metode Naive Bayes Quraisy, Muhamad; Tanjung, Thoyyibah
Jurnal Ilmu Komputer Vol 1 No 2 (2023): Jurnal Ilmu Komputer (Edisi Desember 2023)
Publisher : Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Sentiment analysis is the process of automatically extracting, understanding and processing unstructured text data to obtain sentiment information contained in opinions or opinion statements that are positive, negative, or neutral. The data is classified using Naive Bayes. The analysis is divided into 10 stages: crawling, labeling, data cleaning, pre-processing, case folding, stopwords removal, tokenizing, stemming, word weighting, and sentiment classification. Word weighting employs the TF-IDF method (Term Frequency - Inverse Document Frequency). The data is classified into 3 classes: positive, negative, and neutral. Subsequently, the data is evaluated using confusion matrix testing with parameters such as precision, recall, f1-score, and support. The test results indicate that for the 3-class test (positive, negative, and neutral), the best result was achieved with an accuracy of 71.33%.
Co-Authors Ayu Puspita Sari Chairiansyah, Syafril Daulay, Anggriyana Daulay, Anggriyani Tanjung, Thoyyibah Yulianto, Bimo Tri