Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Komputer
Nursalam, Asep Herman
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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

Found 2 Documents
Search

 1 
Analisis Vulnerabilitas Situs Web Universitas Pamulang Menggunakan Nessus Nursalam, Asep Herman; Subekti, R.P. Fiki Wisnu; Safitri, Astried Nirmala; Prasmono, Yossy Veifbrian Fitri; Otafiyani, Adila Indriyani
Jurnal Ilmu Komputer Vol 2 No 1 (2024): Jurnal Ilmu Komputer (Edisi Juli 2024)
Publisher : Universitas Pamulang

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

Abstract

The Pamulang University (UNPAM) website is an official website that is used for various purposes. Therefore, website security needs to be maintained so that it is not exploited by irresponsible parties. Vulnerability analysis is one way to find out the vulnerabilities that exist in a system. This research aims to conduct vulnerability analysis on the UNPAM website using Nessus. The research results show that the UNPAM website has a high level of vulnerability. This is indicated by the existence of high and medium levels of vulnerability. These vulnerabilities can be exploited by irresponsible parties to attack the UNPAM website. To mitigate these vulnerabilities, UNPAM website managers can take preventative steps by upgrading to a cipher suite with a key length of 128 bits or more, verifying the authenticity of the SSL certificate, enabling DNSSEC and implementing a DNSSEC-enabled resolver, using a DNS firewall, and disabling TLS 1.0 and enabling TLS 1.2 or higher version.
Analisis Tipe Kecerdasan Majemuk Siswa Sekolah Dasar Berbasis Catatan Perilaku Menggunakan Algoritma Naive Bayes, K-Nearest Neighbors, dan Support Vector Machine Nursalam, Asep Herman; Agung Budi Susanto; Taswanda Taryo
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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

Abstract

This study aims to identify the types of multiple intelligences of elementary school students based on Howard Gardner's theory by utilizing machine learning algorithms, namely Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The data used comes from student behavior records and intelligence type questionnaires obtained from students or parents. The SEMMA method (Sample, Explore, Modify, Model, Assess) is used, including text preprocessing and TF-IDF feature extraction. The classification process is carried out using Orange Data Mining software and evaluated using accuracy, precision, recall, F1-score, and AUC metrics. The evaluation results show that the SVM algorithm provides the best performance with an accuracy of 93.30% and AUC of 0.997. Naive Bayes follows with 90.50% accuracy and 0.994 AUC, while KNN reaches 89.50% accuracy and 0.941 AUC. The study also results in a web-based application prototype that classifies students' intelligence types and provides personalized learning recommendations. This confirms the effectiveness of machine learning in supporting personalized learning and student potential development.
Co-Authors AGUNG BUDI SUSANTO Otafiyani, Adila Indriyani Prasmono, Yossy Veifbrian Fitri Safitri, Astried Nirmala Subekti, R.P. Fiki Wisnu Taswanda Taryo