Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Pseudocode Informatik : Jurnal Ilmu Komputer
Sudiasta Putri, Nyoman Dinda Indira
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Perbandingan Tools Vulnerability Scanning Pada Pengujian Sebuah Website Suputri, Komang Ayu; Maharani, Made Donita; Pratama, Gede Ade; Sudiasta Putri, Nyoman Dinda Indira; Listartha, I Made Edy; Saskara, Gede Arna Jude
Informatik : Jurnal Ilmu Komputer Vol 18 No 3 (2022): Desember 2022
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v18i3.5133

Abstract

Cyber Attack adalah suatu upaya mencuri, mengubah, mengekspos informasi melalui akses tidak sah ke sistem komputer. Terdapat ancaman cyber attack yaitu Phishing, SQL Injection, Man In The Middle, DDOS Attack, Password Attack, XSS, Vulnerability Scanning dan Ransomware Attack. vulnerability Scanning adalah suatu proses mengidentifikasi dan menemukan kelemahan atau kerentanan dalam sebuah sistem. Dalam vulnerability scanning terdapat tiga tools yaitu RedHawk, WebKiller, dan Rapidscan. pengujian ini bertujuan untuk membandingkan setiap tools untuk mencari kerentanan pada suatu website. Pada penelitian ini, setiap tools dilakukan percobaan sebanyak 3 kali percobaan dan hasil celah keamanan yang ditemukan pada setiap tool yaitu pada RedHawk dan WebKiller sejumlah 8 dan pada RapidScan sejumlah 12.
Perbandingan Kinerja Algoritma Naive Bayes dan K-Nearest Neighbor dalam Menganalisis Sentimen Pengguna Game Free Fire Sudiasta Putri, Nyoman Dinda Indira; Maysanjaya, I Made Dendi; Sunarya, I Made Gede
Jurnal Pseudocode Vol 12 No 2 (2025): Volume 12 Nomor 2 September 2025
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.12.2.53-59

Abstract

Free Fire is one of the most popular online games in Indonesia, yet it continues to receive a wide range of user reviews regarding gameplay experiences. These reviews reflect diverse user perceptions, including both praise and criticism, making sentiment analysis essential to understanding user satisfaction. This study aims to classify user sentiments toward Free Fire using a combined dataset collected from the Google Play Store and App Store, and to compare the performance of two text classification algorithms: Naive Bayes and K-Nearest Neighbor (KNN). The data were collected using web scraping techniques and manually labeled by expert validators. Text preprocessing involved cleansing, tokenizing, stopword removal, and stemming, followed by term weighting using the Term Frequency-Inverse Document Frequency (TF-IDF) method. The experimental results show that the Naive Bayes algorithm achieved the highest accuracy of 72.78%, while the KNN algorithm recorded a maximum accuracy of 45.91%. Based on these findings, Naive Bayes is proven to be more effective in classifying user sentiments related to Free Fire. The results of this study are expected to provide constructive insights for developers to improve the quality and user experience of the game.
Co-Authors Gede Arna Jude Saskara I Made Edy Listartha I Made Gede Sunarya I Md. Dendi Maysanjaya Maharani, Made Donita Pratama, Gede Ade Suputri, Komang Ayu