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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Lintang Amarul Fatah
UNIVERSITAS STIKUBANK
Computer Science & IT Engineering

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ANALISIS KOMPARATIF NILAI PASAR DAN PERFORMA PEMAIN: IDENTIFIKASI PEMAIN UNDERVALUED BERBASIS BIG DATA ANALYTIC Lintang Amarul Fatah; Hanacahyani Widya Asih; Mukti Diananingsih; Djoko Pitoyo; Kristiawan Nugroho; Eka Ardhianto
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7460

Abstract

The discrepancy between market value and actual player performance often creates financial inefficiencies in football club recruitment strategies. This study aims to identify undervalued players (high performance but low valuation) using Big Data Analytics on the Transfermarkt dataset. The initial dataset is large-scale, comprising 32,601 player records and 1,706,806 match appearance entries, reflecting high-volume data characteristics The research methodology follows four systematic stages: (1) massive data acquisition and integration covering match statistics and transfer history; (2) implementation of Feature Engineering to convert raw statistics into per-90-minute metrics while accounting for contract duration; (3) fair value modeling using the CatBoost Regressor algorithm optimized with Log-Transformation to handle skewed data distributions; and (4) model validation using 5-Fold Cross Validation and residual analysis to detect price anomalies. The results demonstrate the model's ability to precisely identify potential player segments overlooked by standard market valuations. It is concluded that integrating CatBoost with robust feature engineering serves as a strategic instrument for club management to enhance investment efficiency (Return on Investment). 
SYSTEMATIC LITERATURE REVIEW KERENTANAN LANSIA TERHADAP SERANGAN PHISHING PADA SISTEM DANA PENSIUN DIGITAL Hanacahyani Widya Asih; Lintang Amarul Fatah; Mukti Diananingsih; Djoko Pitoyo; Eka Ardhianto; Aji Supriyanto
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8356

Abstract

The digital transformation of financial systems has improved efficiency while simultaneously expanding the cybersecurity attack surface, particularly for phishing threats in pension fund distribution. Elderly individuals, as primary beneficiaries, are considered highly vulnerable due to limited digital literacy, cognitive decline, and a strong trust bias toward authority. This study aims to identify phishing attack patterns, risk factors, and effective mitigation strategies targeting elderly populations through a Systematic Literature Review (SLR) approach following the PRISMA framework. Literature was sourced from IEEE Xplore, Scopus, SpringerLink, and Google Scholar (2015–2025). A total of 120 articles were initially identified, with 35 studies meeting the inclusion criteria after rigorous screening. The findings indicate that elderly individuals are particularly susceptible to email phishing, smishing, and vishing, with significantly higher success rates compared to younger populations. Key contributing factors include low cybersecurity literacy, cognitive limitations, trust bias, and non-inclusive interface design. Effective mitigation strategies include user-centered security design, biometric multi-factor authentication (MFA), and simulation-based security training. This study contributes to a comprehensive risk mapping of phishing threats among elderly pension beneficiaries and provides actionable insights for designing more secure and inclusive digital pension systems.
Co-Authors Aji Supriyanto Dian Kristiawan Nugroho Djoko Pitoyo Eka Ardhianto Hanacahyani Widya Asih Mukti Diananingsih