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All Journal JURNAL SISTEM INFORMASI BISNIS Alkhawarizmi KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIV : International Journal on Informatics Visualization SemanTIK : Teknik Informasi Jurnal RESISTOR (Rekayasa Sistem Komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) IJISTECH J-SAKTI (Jurnal Sains Komputer dan Informatika)
Handrizal Handrizal
Program Studi S1 Ilmu Komputer Fakultas Ilmu Komputer Dan Teknologi Informasi Universitas Sumatera Utara
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Social Sciences

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ANALISIS PERBANDINGAN TOOLKIT RECUVA DATA RECOVERY DAN STELLAR PHOENIX WINDOWS DATA RECOVERY UNTUK DIGITAL FORENSIK Handrizal Handrizal
semanTIK Vol 4, No 2 (2018): semanTIK
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1834.304 KB) | DOI: 10.55679/semantik.v4i2.4692

Abstract

This paper presents an analysis with two digital forensic toolkits for deleted data scenarios. The used toolkit is Recuva Data Recovery and Stellar Phoenix Windows Data Recovery. They can recover data that is being and analyzed in a USB flash drive. The results of the comparison that the two toolkits can work well regarding finding data that has been discarded or in recovering the deleted data.Keywords—Data, Recovery, Forensic, Recuva, Stellar DOI: 10.5281/zenodo.1438737
Analisis Perbandingan Toolkit Puran File Recovery, Glary Undelete Dan Recuva Data Recovery Untuk Digital Forensik Handrizal, Handrizal
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 1, No 1 (2017): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1317.265 KB) | DOI: 10.30645/j-sakti.v1i1.31

Abstract

This paper presents a comparative analysis of three digital forensics toolkit for data recovery scenario that has been deleted. Toolkit used is Puran File Recovery, Glary Undelete and Recuva Data Recovery. Their ability to restore deleted data has been tested and analyzed in a USB flash drive. The results of the comparison show that this third toolkit can work well in terms of finding the data that has been deleted or in recovering the deleted data.
Analisis Perbandingan Toolkit Puran File Recovery, Glary Undelete Dan Recuva Data Recovery Untuk Digital Forensik Handrizal, Handrizal
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 1, No 1 (2017): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v1i1.31

Abstract

This paper presents a comparative analysis of three digital forensics toolkit for data recovery scenario that has been deleted. Toolkit used is Puran File Recovery, Glary Undelete and Recuva Data Recovery. Their ability to restore deleted data has been tested and analyzed in a USB flash drive. The results of the comparison show that this third toolkit can work well in terms of finding the data that has been deleted or in recovering the deleted data.
Advancements in Detection Top Influencer Marketing in the Airline Industry: A Combination of the Leiden Algorithm and Graph Coloring Handrizal, Handrizal; Sihombing, Poltak; Budhiarti Nababan, Erna; Andri Budiman, Mohammad
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3440

Abstract

In recent years, the airline industry has increasingly utilized social media and online platforms to engage customers and enhance brand loyalty. Identifying key influencers within these networks is crucial for optimizing marketing strategies and improving customer engagement. Influencers play a pivotal role in shaping opinions, driving behaviors, and amplifying brand messages within social networks. Consequently, efficient methods for detecting influencers are essential for understanding network dynamics and maintaining a competitive edge. This study introduces a novel contribution to the field of social network analysis by proposing the Leiden Coloring Algorithm, an enhancement of the traditional Leiden algorithm that integrates graph coloring techniques. The scientific contribution of this research lies in improving the precision of community detection and computational performance in large-scale networks. Experimental results on five airline-related datasets demonstrate that the proposed method achieves higher modularity (average 0.9375), faster processing time (average 204.88 seconds), and identifies fewer, more cohesive communities compared to the Louvain Coloring Algorithm. These findings highlight the algorithm's effectiveness in influencer detection and its potential application in community detection, marketing optimization, and strategic decision-making within the airline industry.
Co-Authors Agus Perdana Windarto Andri Budiman Andri Budiman, Mohammad Bintang Bestari Budhiarti Nababan, Erna Desy Rahayu Ardani Enjelica Rumapea Herriyance Herriyance Joose Andar Laidin Manurung M Fauzan Poltak Sihombing Poltak Sihombing Rivaldo, Rian Sadewo, Mhd Gading Siti Dara Fadilla Solikhun Solikhun Sundari Retno Andani Ummi Fauziyah