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CSRID
Published by Universitas Potensi Utama
ISSN : 20851367     EISSN : 2460870X     DOI : https://doi.org/10.22303/csrid
Core Subject : Science,
Computer Science & IT
CSRID (Computer Science Research and Its Development Journal) is a scientific journal published by LPPM Universitas Potensi Utama in collaboration with professional computer science associations, Indonesian Computer Electronics and Instrumentation Support Society (IndoCEISS) and CORIS (Cooperation Research Inter University).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 12 Documents
 1   2 
Search results for , issue "Vol. 15 No. 2: June 2023" : 12 Documents clear
Malware Detection pada Static Analysis Windows Portable Executable (PE) Menggunakan Support Vector Machine dan Decision Tree Qusyairi, Mohammad Mirza; Rio Guntur Utomo; Rahmat Yasirandi
CSRID (Computer Science Research and Its Development Journal) Vol. 15 No. 2: June 2023
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.15.2.2023.93-102

Abstract

Malware has become a major issue for computer system security today. Due to its ability to spread rapidly and negatively impact system performance, malware detection becomes crucial. One of the methods for malware detection is performing classification using Machine Learning, which learns the variable values of an application without executing it. In this study, the author evaluates the method of malware detection in the static analysis of Windows Portable Executable (PE) using Support Vector Machine (SVM) and Decision Tree. The author uses a dataset of PE files related to malware and safe applications from malware Using SVM and Decision Tree algorithms to classify the PE files as malware or not, determining the best machine learning algorithm for malware detection in PE files.
Retweet Prediction Using ANN Method and Artificial Bee Colony Jondri, Jondri; Farisi, Kamaludin Hanif; Lhaksmana, Kemas Muslim
CSRID (Computer Science Research and Its Development Journal) Vol. 15 No. 2: June 2023
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.15.2.2023.83-92

Abstract

In the ongoing modern era, the rapid dissemination of information takes place, utilizing various channels for data exchange. One such platform is the social media platform Twitter, renowned for its swift and extensive information propagation. A pivotal factor contributing to information distribution on Twitter is the retweet feature, whereby users can redistribute content to their audience. A study has been conducted to forecast this retweet activity by employing the Artificial Neural Network classification method in conjunction with the Artificial Bee Colony optimization approach. This study leverages diverse features, encompassing content-based feature, user-based feature, and time-based feature. The evaluation results from this study reveal that the proposed method achieves an accuracy value of around 83% with the highest accuracy value reaching 84%. These findings indicate that the fusion of the Artificial Neural Network classification method executed with optimization using the Artificial Bee Colony algorithm yields dependable and consistent performance in predicting retweet activities.

Page 2 of 2 | Total Record : 12

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