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Jurnal Sisfokom (Sistem Informasi dan Komputer)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Atma Luhur
ISSN : 23017988     EISSN : 25810588     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Jurnal Sisfokom merupakan singkatan dari Jurnal Sistem Informasi dan Komputer. Jurnal ini merupakan kolaborasi antara sivitas akademika STMIK Atma Luhur dengan perguruan tinggi maupun universitas di Indonesia. Jurnal ini berisi artikel ilmiah dari peneliti, akademisi, serta para pemerhati TI. Jurnal Sisfokom diterbitkan 2 kali dalam setahun yaitu pada bulan Maret dan September. Jurnal ini menyajikan makalah dalam bidang ilmu sistem informasi dan komputer.
Arjuna Subject : -
Articles 678 Documents
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Decision Prioritization with MCDM in Post-Disaster Management: A PRISMA-Guided Systematic Review and Bibliometric Mapping Pinem, Agusta Praba Ristadi; Gernowo, Rahmat; Koesuma, Sorja
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2528

Abstract

Prioritizing post-disaster actions requires balancing multiple, often conflicting criteria. To consolidate scattered evidence, this study reviews decision prioritization with Multi-Criteria Decision-Making (MCDM) in post-disaster management using a PRISMA-guided systematic review and bibliometric mapping. Initial searches returned 18,454 records from Scopus, 47,206 from Google Scholar, 650 from Emerald Insight, 30,975 from ProQuest, and 4,468 from IEEE Xplore. We included English-language articles published between 2014 and 2025—a window chosen to capture the rise of hybrid and fuzzy variants and early integrations with GIS, AI, and big data—that apply MCDM to prioritizing projects, interventions, or sites. We excluded non-English items, duplicates, and incomplete records; screening and eligibility followed PRISMA. We combined SLR procedures with bibliometric analysis in VOSviewer and R-bibliometrix to map co-occurrence. From the pool, 32 studies met the criteria. Distance-based methods (TOPSIS, VIKOR, EDAS) and AHP dominate; hybrid and fuzzy variants are increasing. Objective and mixed weighting are common, while normalization choices and ranking rules vary by context. Validation is uneven: case applications and expert judgment are common, but sensitivity tests and cross-method comparisons are scarce. We connect objectives, weighting and normalization, ranking, and validation, identify method–context fit, and spotlight reporting gaps. We provide method-selection cues and a reporting checklist for practitioners, and a roadmap for standardized validation, transparent parameterization, and integration with GIS, AI, and big data for researchers.
Face Recognition for Attendance Systems: A Bibliometric Review of Research Trends and Opportunities Agustiyar, Agustiyar; Isnanto, R. Rizal; Widodo, Catur Edi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2529

Abstract

Automated attendance systems have become a critical component of smart education environments. This study presents a bibliometric analysis of research on facial recognition-based attendance systems to identify research trends, collaboration patterns, and potential directions for future studies. Data were collected from the Scopus database for the period 2019–2024 using keywords related to “facial recognition,” “attendance system,” and “deep learning.” The bibliometric analysis was conducted using OpenRefine for data cleaning and Biblioshiny (R-Bibliometrix) for visualization and mapping of scientific networks, including co-authorship, keyword co-occurrence, and citation analysis. The results show a significant increase in research publications, dominated by contributions from India, Indonesia, and Malaysia, with deep learning and convolutional neural networks (CNN) as the most frequently studied techniques. International collaboration remains limited, indicating opportunities for broader cooperation in this field. This research contributes by providing a comprehensive overview of the global research landscape on facial recognition for attendance systems and offering strategic insights for developing more accurate, efficient, and scalable recognition technologies in educational environments.
Large Language Models in Accounting Tasks: Driving Factors and Ethical Dilemmas Among Accounting Students Josephine, Katherine Olivia; Tarigan, Thia Margaretha; Weli, Weli
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2531

Abstract

This research aims to identify the key factors that affect accounting students’ intention to adopt and the actual usage of Large Langauge Models (LLMs), including ChatGPT, in academic contexts. It also addresses ethical concerns that may arise from their use. Using a quantitative design, data were collected through an online survey involving 302 students from various universities in the Greater Jakarta area who had prior experience using LLMs. This research aims to address the gap in literature on AI-based technology acceptance within the accounting field by extending the Technology Acceptance Model (TAM) with trust and academic ethics. The study offers a theoretical contribution by deepening insights into technology acceptance within accounting education and a practical contribution by emphasizing the integration of ethical considerations in the use of LLMs in higher education. The study focuses on key constructs including perceived ease of use, perceived usefulness, trust, academic ethics, behavioral intention, and actual usage behavior. Data was analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique via SmartPLS 4 software. The results show that all examined factors positively influence students’ intention to use LLMs, with perceived usefulness stands out as the most significant driver. Furthermore, behavioral intention significantly predicts actual use, suggesting that students who see practical value in these tools are more likely to adopt them in their learning routines. What sets this research apart is its integration of motivational and ethical dimensions in examining technology acceptance within accounting education.
Machine Learning for Post-Disaster Building Damage Classification and Rehabilitation Recommendation: A Review Rahmawati, Eka; Widodo, Catur Edi; Koesuma, Sorja
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2532

Abstract

Accurate classification of building damage following disasters plays a critical role in facilitating efficient rehabilitation and reconstruction. Traditional field-based assessment methods, however, present significant limitations—including time inefficiencies, susceptibility to subjective interpretation, and potential safety risks for survey personnel. Recent advancements in machine learning (ML) have significantly improved the efficiency and objectivity of post-disaster damage assessment by leveraging diverse data sources such as satellite imagery, unmanned aerial vehicles (UAVs), and even crowdsourced social media content. This study conducts a narrative literature review of 78 peer-reviewed articles published from 2020 to 2024, focusing on ML-driven methodologies for classifying building damage and generating rehabilitation recommendations. The literature review reveals a prevailing reliance on deep learning models—especially convolutional neural networks (CNNs) and transformer-based architectures—due to their robust accuracy and adaptability across varied disaster scenarios. Furthermore, novel approaches like self-supervised learning, ensemble methods, and few-shot learning show promising potential in addressing challenges posed by sparse or unevenly distributed datasets. Despite rapid advancements in ML-based post-disaster building damage classification, real-world implementation remains constrained. This review synthesizes current trends, persistent challenges, and critical research gaps to inform the development of a robust ML framework for post-disaster recovery efforts. This study uniquely highlights the integration of ML-based classification with rehabilitation planning frameworks, providing practical guidance for disaster management agencies to optimize post-disaster recovery strategies.
Harnessing Remote Sensing for Soil Erosion Prediction: A Bibliometric Review of RUSLE Applications Cobantoro, Adi Fajaryanto; Wibowo, Mochamad Agung; Sanjaya, Ridwan
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2533

Abstract

This study examines recent advancements in soil erosion modeling using the Revised Universal Soil Loss Equation (RUSLE), integrated with remote sensing and artificial intelligence techniques. Adopting a Systematic Literature Review (SLR) and bibliometric analysis via Bibliometrix in R, 63 articles were analyzed from an initial 359 based on strict selection criteria. Findings reveal a sharp rise in publications since 2017, especially involving machine learning and Google Earth Engine (GEE) platforms. Co-authorship analysis highlights significant international collaboration, particularly between Asia and Europe. Concept maps and co-word analyses show a shift from traditional RUSLE applications toward AI and big data approaches. Thematic evolution further indicates a growing focus on climate change and the Sustainable Development Goals (SDGs). The review's primary contribution lies in its explicit identification of critical research priorities by pinpointing key gaps: the limited use of field validation, weak SDG integration, and fragmented international research networks. By highlighting these deficiencies, this study provides a clear roadmap for future investigations, steering the field toward more inclusive, data-driven, and validated approaches to address global land degradation and climate resilience. Overall, the study contributes to the development of more effective erosion mitigation models through technological integration and international collaboration.
Literature-Driven Contributions to the Development of LLM-Based Customer Insight Systems Nugroho, Irwan Andriyanto; Adi, Kusworo; Aryasa, Komang Budi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2534

Abstract

This research delineates the conceptual advancement of a sentiment analysis model employing Large Language Models (LLMs), augmented by a dynamic weighting system predicated on the strategic significance of product attributes. This research, based on a systematic review of seven recent studies predominantly utilizing conventional NLP methodologies discovers significant deficiencies, such as disjointed sentiment extraction and the absence of contextual, strategic weighting. Prior studies have established the efficacy of Natural Language Processing (NLP) techniques in evaluating customer satisfaction and online reviews; however, there has been a scarcity of initiatives that integrate sentiment analysis with product prioritization in decision-making processes. The suggested framework presents an innovative amalgamation of LLM-based sentiment analysis with a strategic weighting system that adapts in real-time according to business priorities, setting it apart from earlier customer analytics frameworks that consider sentiment and strategy in isolation. To conceptually validate this model, a thematic synthesis and comparative mapping approach were employed to assess the potential of the proposed components to enhance interpretability and alignment between customer feedback and product decisions. Initial conceptual analysis indicates that the framework may improve decision quality by integrating profound contextual sentiment insights with flexible business prioritization. The goal is to improve strategies for making products better, make sure that customer feedback is in line with strategic goals, and help businesses make decisions based on data in changing business environments.
Detection of Reconnaissance Attacks Using a Hybrid CNN–LSTM on IoT Network Susanto; Dermawan, Budi Arif; Rasenda, Rasenda
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2535

Abstract

The rapid expansion of the Internet of Things (IoT) has increased connectivity across various sectors but also exposed systems to new and evolving cybersecurity threats. One of the most critical threats is the reconnaissance phase, where attackers gather system information to prepare more sophisticated intrusions. Conventional intrusion detection systems often fail to detect reconnaissance due to similarities with benign traffic. To address this problem of ineffective reconnaissance detection, this study proposes a hybrid detection framework that combines autoencoder-based feature extraction with a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) classifier. The autoencoder, an unsupervised neural network that compresses input data and reconstructs it with minimal loss, is used to reduce data dimensionality and learn meaningful hidden features. The CNN captures spatial patterns and LSTM models temporal dependencies in network traffic. Experiments were conducted using the CICIoT2023 dataset, focusing exclusively on reconnaissance attacks. The evaluation metrics include accuracy, precision, recall, specificity, False Positive Rate (FPR), False Negative Rate (FNR), and F1-score. Results show that the proposed model achieves an overall accuracy of 99.79%, specificity of 0.9994, precision of 0.9948, recall of 0.9445, and F1-score of 0.9648. Class-level analysis demonstrates high performance across most attack types, though Ping Sweep exhibits a lower recall of 0.6853 despite achieving perfect precision. These results demonstrate that the hybrid CNN–LSTM model with autoencoder-based feature extraction can effectively detect reconnaissance attacks in IoT networks. The approach enhances detection accuracy, reduces false alarms, and provides a promising foundation for improving real-world IoT security monitoring systems.
A Systematic Literature Review and Bibliometric Analysis on Forest Fire Prediction Models Hanindito, Gregorius Anung; Wibowo, Mochamad Agung; Sanjaya, Ridwan
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2537

Abstract

Forest fires cause a significant ecological, social and economic threat globally, particularly in regions prone to climate change and anthropogenic pressures. This study conducts a comprehensive Systematic Literature Review (SLR) and Bibliometric Analysis of forest fire prediction models from 2021 to mid 2025, focusing on the utilization of machine learning, deep learning, and hybrid approaches. The review systematically analyses 65 relevant publications sourced from Scopus and ScienceDirect, selected through a PRISMA framework. The findings indicate that models like random forest, 1D CNN, ConvLSTM, and Transformer are commonly applied, leveraging diverse datasets including satellite picture, atmospheric data, vegetation health index, and fire history records. Despite advances, most studies still emphasize meteorological variables, while local contexts such as socio-economic and land use factors remain underexplored. Furthermore, current models often face limitations in generalizability across regions. This study identifies key trends, gaps, and opportunities in the development of more robust and interpretable fire prediction models. This research also provides knowledge and insight related to the warning system for forest fire disasters. Recommendations include integrating socio-environmental data and developing geographically adaptive frameworks to enhance forest fire risk assessment and early warning systems.
Mapping Bitcoin Research in Information Systems: A Comprehensive Bibliometric Analysis (2008–2025) Munazilin, Akhlis; Agung Wibowo, Mochamad; Parlika, Rizky
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2538

Abstract

Bitcoin has been a major focus of interdisciplinary research in information systems, finance, and economics since its emergence in 2008. Despite the extensive literature on Bitcoin, patterns of intellectual collaboration, the evolution of research themes, and research gaps have not been comprehensively mapped. This study presents a bibliometric analysis of 3,312 scientific articles indexed by Scopus from 2008 to May 2025, using a quantitative approach based on Bibliometrix. The analysis includes publication trends, author and institutional collaboration networks, co-citation mapping, and thematic clusters based on keywords. The results reveal five dominant themes: (1) blockchain development beyond crypto, (2) regulatory challenges and global adoption, (3) Bitcoin price volatility, (4) impacts on the global financial system, and (5) social implications in developing countries. The study also identifies an epistemological fragmentation between technical and policy approaches. These findings reinforce the need for an integrated multimodal approach that combines market data, sentiment analysis, and regulatory context to develop more robust predictive models. This study is the first comprehensive bibliometric review of Bitcoin in global scope that explicitly links findings to information systems research opportunities.
Automatic Detection of Cyberbullying on Text, Image, and Video: A Systematic Literature Review Fitro, Achmad; Wibowo, Mochamad Agung; Widodo, Catur Edi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2542

Abstract

This study presents a systematic literature review (SLR) on the automatic detection of cyberbullying across multiple media modalities, including text, images, and videos, between 2020 and 2025. Unlike previous SLRs that focused only on textual or unimodal data, this research provides a comprehensive synthesis of multimodal approaches that integrate linguistic, visual, and audiovisual cues. Using the PRISMA framework, 4,272 records were screened, resulting in 120 studies for full analysis. The findings reveal a sharp increase in publications in 2025, driven by advances in large language models (LLMs), multimodal transformers, and heightened global attention to online safety. Quantitatively, 69% of studies focused on text-based detection, 21% on multimodal (text-image), and 10% on video-based approaches. NLP, CNN, SVM, BERT, and LSTM remain the most commonly used models, while emerging hybrid frameworks (e.g., ResNet–BiLSTM) show promising performance. Previous studies were often limited by real-time detection capabilities, fairness concerns, and lack of explainable AI. This SLR addresses those gaps by synthesizing methodological trends, highlighting ethical challenges, and identifying opportunities for future integration of explainable and human-centered AI. The practical implication of this study lies in providing a structured reference for researchers, policymakers, and social media platforms to design fair, transparent, and adaptive cyberbullying detection systems.

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