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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JITU : Journal Informatic Technology And Communication
Muhammad Jauhar Fardani
Universitas Semarang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Library & Information Science

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METODE MOORA DENGAN PERFORMA KLUB LIGA INGGRIS TERHADAP KLASEMEN AKHIR Muhammad Jauhar Fardani; Arya Anggara; Agusta Praba Ristadi Pinem
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 1 (2025): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i1.5523

Abstract

The English Premier League (EPL) is a highly popular football league with intense competition among clubs every season. To assess the performance of EPL clubs objectively, data-driven evaluation is required to measure various aspects of team performance. This study aims to evaluate the performance of EPL clubs in the 2018/2019 season using the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method. MOORA was chosen because it can consider quantitative criteria such as goals scored, goals conceded, clean sheets, shots on target, total corner kicks, average ball possession, number of cards, and fouls. The data used includes statistics from 20 EPL clubs. The results show that top-ranked clubs like Manchester City, Liverpool, and Chelsea had consistent results with their final positions in the standings. However, differences were found in some clubs, such as Wolverhampton, which finished 7th in the final standings but ranked 10th according to MOORA. The analysis reveals that the MOORA ranking results, based on non-point statistical criteria, have a very high correlation with the final standings, with a Spearman correlation value of 0.9624. These findings emphasize the relevance of non-point statistical data in evaluating club performance quantitatively. This study contributes to the development of data-driven evaluation methodologies for football, providing valuable insights for club managers and sports analysts in identifying team strengths and weaknesses objectively, thereby supporting more accurate decision-making in strategic planning.
Analisis Efektivitas Teknik File Carving pada Pemulihan Data Digital Menggunakan PhotoRec Susanto; Irfan Hanafi; Narisa Aulia; Muhammad Jauhar Fardani; Muhammad Nur Irfan
JITU Vol 10 No 1 (2026)
Publisher : Universitas Boyolali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jitu.v10i1.2245

Abstract

Data loss is a critical issue in the digital era, both in everyday computing and in forensic investigations. This study aims to evaluate the effectiveness of PhotoRec, an open-source file carving tool, in recovering lost files from a FAT32 storage medium. A strict digital forensic approach was applied by performing disk imaging, preserving evidence integrity, and documenting each stage in detail. Data loss was simulated through logical deletion and by scanning unallocated sectors to closely replicate real-case conditions. PhotoRec was then utilized to select the target media, identify partitions, determine the file system type, and scan free space to extract files based on signature recognition. The findings show that PhotoRec successfully recovered a total of 50 files, including all primary files deleted during the experiment. In addition, several system-related files such as ELF were also recovered, illustrating the aggressive nature of carving techniques that extract any recognizable data structure. These results confirm that PhotoRec can operate effectively in data loss scenarios on FAT32 media, even when file metadata is no longer available. This study contributes to a deeper understanding of PhotoRec’s performance within the context of digital forensics and highlights its relevance as an investigative tool that prioritizes accuracy, integrity, and procedural traceability.
Co-Authors Agusta Praba Ristadi Pinem Arya Anggara Irfan Hanafi Muhammad Nur Irfan Narisa Aulia Susanto