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All Journal Sistemasi: Jurnal Sistem Informasi IJoICT (International Journal on Information and Communication Technology)
Manar Talat Ahmad
University of Mosul
Computer Science & IT Electrical & Electronics Engineering Environmental Science

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Evaluating the Performance of Graph-Based Recommendation Systems: A Case Study on Amazon Data Zaid Mundher; Manar Talat Ahmad
IJoICT (International Journal on Information and Communication Technology) Vol. 11 No. 2 (2025): Vol. 11 No. 2 Dec 2025
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v11i2.9878

Abstract

Today, recommendation systems are considered a main component of social media platforms and many other online websites. Recommendation systems can be defined as tools that aim to introduce and suggest products to users. The suggestion process depends on many factors, such as user behavior and product similarity. In recent years, many research papers have discussed recommendation systems and introduced new solutions and methods to build them. On the other hand, in the last few years, data representation has also become an important issue. Because of the massive increase in data, new methods to represent data have been introduced and adopted, such as graph-based data representation. In this work, the efficiency of employing graph-based databases in building recommendation systems was evaluated and compared to traditional approaches.. Specifically, Amazon Product Reviews dataset was used to build a recommendation system using traditional methods. This data was then transformed to a graph format and used to generate recommendations. Metrics such as accuracy, recall, and precision were adopted to determine the efficiency and accuracy of the results, as will be discussed later.
A Large-Scale Open Dataset of Computer Science Research Papers (2020–2025) Zaid Mundher; Manar Talat Ahmad
Sistemasi: Jurnal Sistem Informasi Vol 15, No 4 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i4.6255

Abstract

The rapid growth of publications in different fields, such as computer science, required well-structured datasets to support data-driven research. This paper presents an open large-scale dataset of computer science research papers published between 2020 and 2025, collected from Crossref metadata using the Crossref REST API. A structured keyword-based retrieval framework was developed to collect papers and their associated metadata. Preprocessing techniques, including cleaning, normalization, and validation were also made on the collected data. The introduced dataset has 4,313,328 research paper records which represents one of the largest structured collections of computer science publications for the specified period. The dataset provides comprehensive metadata fields that enable large-scale analysis, research trend identification, collaboration network exploration, and the recommendation systems development.
Design and Implementation of a Digital Forensics Tool to Enhance Windows Artifact Analysis Manar Talat Ahmad
Sistemasi: Jurnal Sistem Informasi Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6346

Abstract

Although the wide use of technology came with many advantages and facilities to the people daily life, it causes the cybercrime to be raised. Digital forensics is one of the most important scientific fields, aiming to investigate cybercrimes and analyze digital evidence. Among different technology’s platforms, operating systems is one of the most important sources of evidence for digital forensic analysts providing a rich information that can used to get important insights. Examples of such evidence include identifying programs that have been executed on a computer, determining files that have been accessed, and identifying storage devices that were connected via USB ports. Practically, accessing and handling this raw information using manual methods is time-consuming, in addition to the lack of accuracy in results due to human errors. In this work, a GUI-based tool is presented to handle most of the evidence provided by Windows operating system that can be used in digital forensics. The research aims to fill the gap caused by the lack of a free tool that deals with these sources, as most available tools are either commercial tools that are complex to use and require expert-level experience. In contrast, available free tools have limited-capability since they are focusing only on one type of evidence. The introduced tool was designed and developed using the C# programming language and was tested on the Windows 10 operating system, where it successfully extracted the required information efficiently and smoothly.
Co-Authors Zaid Mundher Zaid Mundher