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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 669 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 using Multi-Criteria Decision-Making (MCDM) in post-disaster management through a PRISMA-guided systematic review and bibliometric mapping. Initial searches yielded 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 that apply MCDM to prioritizing projects, interventions, or sites. This timeframe was chosen to capture the rise of hybrid and fuzzy variants, as well as early integrations with GIS, AI, and big data. We excluded non-English items, duplicates, and incomplete records, following PRISMA guidelines for screening and eligibility. We combined SLR procedures with bibliometric analysis using VOSviewer and R-bibliometrix to map keyword co-occurrence. From the initial pool, 32 studies met the final criteria. The results show that distance-based methods (TOPSIS, VIKOR, EDAS) and AHP dominate the field, while hybrid and fuzzy variants are increasingly utilized. Objective and mixed weighting methods are common, whereas normalization choices and ranking rules vary by context. Validation practices remain inconsistent; while case applications and expert judgment are frequently used, sensitivity tests and cross-method comparisons are scarce. This study synthesizes objectives, weighting, normalization, ranking, and validation to identify method–context fit and highlight reporting gaps. We provide method-selection guidelines and a reporting checklist for practitioners, alongside a roadmap for researchers focusing on standardized validation, transparent parameterization, and integration with GIS, AI, and big data.
Face Recognition for Attendance Systems: A Bibliometric Review of Research Trends and Opportunities Agustiyar, Agustiyar; R. Rizal Isnanto; Catur Edi Widodo
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

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; Edi Widodo, Catur; 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 Adi Fajaryanto Cobantoro; Mochamad Agung Wibowo; Ridwan Sanjaya
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). Key research gaps include the limited use of field validation, weak SDG integration, and a lack of strong international research networks. This review offers strategic insights to guide future investigations, emphasizing the need for more inclusive, data-driven studies capable of addressing 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; Kusworo Adi; Komang Budi Aryasa
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

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Abstract

This study presents the conceptual development of a sentiment analysis model using Large Language Models (LLMs), integrated with a dynamic weighting system based on the strategic value of product attributes. While previous research has demonstrated the effectiveness of Natural Language Processing (NLP) techniques in analyzing customer satisfaction and online reviews, few efforts have aligned sentiment insights with product prioritization in decision-making. Drawing from a systematic review of seven recent studies—most of which rely on traditional NLP approaches—this research identifies critical gaps, including fragmented sentiment extraction and the lack of contextual, strategic weighting. The proposed LLM-based framework advances prior insights by combining sentiment analysis, customer voice modeling, and adaptive prioritization mechanisms. The outcome is intended to enhance product improvement strategies, align customer feedback with strategic objectives, and support data-driven decision-making in dynamic business environments.
Detection of Reconnaissance Attacks Using a Hybrid CNN–LSTM on IoT Network Susanto; Arif Dermawan, Budi; 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 introduced significant cybersecurity challenges, particularly during the reconnaissance phase, where attackers collect system information to launch more severe attacks. Conventional intrusion detection systems often fail to detect reconnaissance due to similarities with benign traffic. To address this, 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 reduces data dimensionality and extracts meaningful latent features, while 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 findings indicate that the hybrid CNN–LSTM model with autoencoder feature extraction is effective for reconnaissance attack detection, with strong generalization and minimal misclassification.
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

Abstract

Forest fires pose a significant ecological and socio-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 such as 1D CNN, ConvLSTM, Transformer, and Random Forest are commonly applied, leveraging diverse datasets including satellite imagery, meteorological data, vegetation indices, 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; Agung Wibowo, Mochamad; Edi Widodo, Catur
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. The PRISMA framework was employed to guide the review process, focusing on articles published from 2020 to 2025 in the Scopus database. A total of 4272 records were initially identified, and after a structured screening process, 120 studies were included in the final synthesis. The results indicate an increasing scholarly interest in the topic, with a peak in 2025. Text-based detection remains the most prevalent approach, but there is a growing trend toward the integration of image and video analysis, particularly using multimodal and hybrid models. Commonly used techniques include Natural Language Processing (NLP), Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and advanced models such as BERT and LSTM. The review also identifies gaps in real-time detection capabilities and the limited use of explainable AI. This study contributes to a deeper understanding of current methods, trends, and future research directions for cyberbullying detection systems

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