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Journal of Information Systems Engineering and Business Intelligence
Published by Universitas Airlangga
ISSN : -     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal ini menerima makalah ilmiah dengan fokus pada Rekayasa Sistem Informasi ( Information System Engineering) dan Sistem Bisnis Cerdas (Business Intelligence) Rekayasa Sistem Informasi ( Information System Engineering) adalah Pendekatan multidisiplin terhadap aktifitas yang berkaitan dengan pengembangan dan pengelolaan sistem informasi dalam pencapaian tujuan organisasi. ruang lingkup makalah ilmiah Information Systems Engineering meliputi (namun tidak terbatas): -Pengembangan, pengelolaan, serta pemanfaatan Sistem Informasi. -Tata Kelola Organisasi, -Enterprise Resource Planning, -Enterprise Architecture Planning, -Knowledge Management. Sistem Bisnis Cerdas (Business Intelligence) Mengkaji teknik untuk melakukan transformasi data mentah menjadi informasi yang berguna dalam pengambilan keputusan. mengidentifikasi peluang baru serta mengimplementasikan strategi bisnis berdasarkan informasi yang diolah dari data sehingga menciptakan keunggulan kompetitif. ruang lingkup makalah ilmiah Business Intelligence meliputi (namun tidak terbatas): -Data mining, -Text mining, -Data warehouse, -Online Analytical Processing, -Artificial Intelligence, -Decision Support System.
Arjuna Subject : -
Articles 246 Documents
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Optimizing Convolutional Neural Networks with Particle Swarm Optimization for Enhanced Hoax News Detection Hermawan, Aditiya; Lunardi, Lidya; Kurnia, Yusuf; Daniawan, Benny; Junaedi
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 1 (2025): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.1.53-64

Abstract

Background: The global spreading of hoax news is causing significant challenges, by misleading the public and undermining public trust in media and institutions. This issue is worsened by the rapid spreading of misinformation which is facilitated by digital platforms, triggering social unrest and threatening national security. To overcome this problem, reliable and robust method is essential to adapt to the evolving tactics of misleading information spreading. Objective: This study aimed to improve the accuracy of hoax news detection tools by evaluating the effectiveness of Deep Learning methods enhanced with Convolutional Neural Networks (CNNs) using Particle Swarm Optimization (PSO). Methods: The dataset was processed by tokenization, stopword removal, and stemming. CNNs were trained with default parameters, due to their potential as one of the effective methods for text classification. Furthermore, PSO was used to optimize the main parameters such as filters, kernel sizes, and learning rate, which was refined iteratively based on validation accuracy. Results: The optimized CNNs+PSO was further tested by data training to show its effectiveness in detecting hoax news and misleading articles. The result showed that the optimized CNNs+PSO model had high effectiveness, by achieving accuracy rate of 92.06%, precision 91.6%, and recall 96.19%. These values validated the model’s ability to classify hoax news in Indonesian accurately. Conclusion: This study showed that the optimized CNNs+PSO method was highly effective in detecting hoax news and misleading articles by achieving impressive accuracy, precision, and recall rate. The integration showed the potential of CNNs+PSO to mitigate the impacts of hoax news, enhance public awareness, and promote people to critically believe the news Keywords: Convolutional Neural Networks, Deep Learning, Hoax, Particle Swarm Optimization, Text Mining
Dynamic Sign Language Recognition in Bahasa using MediaPipe, Long Short-Term Memory, and Convolutional Neural Network Lemmuela , Ivana Valentina; Ayub, Mewati; Karnalim, Oscar
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 1 (2025): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.1.17-29

Abstract

Background: Communication is important for everyone, including individuals with hearing and speech impairments. For this demographic, sign language is widely used as the primary medium of communication with others who share similar conditions or with hearing individuals who understand sign language. However, communication difficulties arise when individuals with these impairments attempt to interact with those who do not understand sign language. Objective: This research aims to develop models capable of recognizing sign language movements in Bahasa and converting the detected gesture into corresponding words, with a focus on vocabularies related to religious activities. Specifically, the research examined dynamic sign language in Bahasa, which comprised gestures requiring motion for proper demonstration. Methods: In accordance with the research objective, sign language recognition model was developed using MediaPipe-assisted extraction process. Recognition of dynamic sign language in Bahasa was achieved through the application of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) methods. Results: Sign language recognition model developed using bidirectional LSTM showed the best result with a testing accuracy of 100%. However, the best result for the CNN alone was 86.67 %. The integration of CNN and LSTM was observed to improve performance than CNN alone, with the best CNN-LSTM model achieving an accuracy of 95.24%. Conclusion: The bidirectional LSTM model outperformed the unidirectional LSTM by capturing richer temporal information, with a specific consideration of both past and future time steps. Based on the observations made, CNN alone could not match the effectiveness of the Bidirectional LSTM, but a combination of CNN with LSTM produced better results. It is also important to state that normalized landmark data was found to significantly improve accuracy. Accuracy within this context was also influenced by shot type variability and specific landmark coordinates. Furthermore, the dataset containing straight-shot videos with x and y coordinates provided more accurate results, dissimilar to those comprised of videos with shot variation, which typically require x, y, and z coordinates for optimal accuracy. Keywords: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), MediaPipe, Sign Language
ChatGPT and Its Impact on Students Assessment Practices in the Higher Educational Sector: A Systematic Review Ofusori, Lizzy Oluwatoyin; Hendradi, Rimuljo
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 1 (2025): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.1.65-78

Abstract

Background: The proliferation of Artificial Intelligence (AI) tools such as ChatGPT is growing at a rapid pace, sparing no sector. One of the AI tools that has grown in it use across the sectors is the use of ChatGPT, a tool that mimics human like capabilities of producing ideas. However, there have been many concerns about how ChatGPT will change the higher education institutions. More worrisome is how it poses risks that compromise the integrity of academic outputs if left unregulated Objective: This study examines the influence of ChatGPT on students’ assessment practices in the higher educational sector Methods: The study carried out a systematic literature review by gathering data from peer reviewed academic papers.  Initially, 140 research papers were identified. Thereafter, these papers went through further filtering, and 35 usable papers were selected and included in the study Results: This study highlighted the importance of using AI tools such as ChatGPT in the higher education sector, underscoring its advantages and the threats that it poses to the sector if the use remains unregulated. The study has recommended institutional policies about the use of AI tools that must be put in place to guide academic staff, researchers and learners in the responsible use of ChatGPT for academic work. Conclusion: “While the widespread adoption of ChatGPT is undeniable, there is an urgent need for a well-balanced regulation regarding its use within Higher Education Institutions (HEIs). Thus, future research should focus on examining the existing policies and practices related to ChatGPT ethics, privacy, and security in education and identify gaps and areas for improvement.  Keywords: ChatGPT, Artificial Intelligence, Chatbot, OpenAI, Higher Education
BloodCell-YOLO: Efficient Detection of Blood Cell Types Using Modified YOLOv8 with GhostBottleneck and C3Ghost Modules Naufal, Mohammad Farid; Ferdiana Kusuma, Selvia
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 1 (2025): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.1.41-52

Abstract

Background: Diagnosing many medical ailments, including infections, immunological problems, and hematological diseases, is a process that depends on precise as well as quick identification of blood cell. Conventional methods for blood cell identification may include skilled pathologists visually inspecting the cell under a microscope, which is a time-consuming choreography. This method is not appropriate for processing vast amounts of data, because the process is time-consuming and is prone to human mistakes. Objective: This study aimed to improve YOLOv8 architecture, offering a more efficient and simplified model for blood cell identification. In addition, the main objective of the analysis was to reduce computational load as well as amount of parameters and still maintaining or improving detection performance. Methods: GhostBottleneck and C3Ghost modules used in the study were included in the head and backbone of YOLOv8 architecture for improvement. All versions of YOLOv8 was subjected to the changes including n, s, m, l, and x. During the analysis, the efficacy of the recommended method was evaluated using a dataset of seven kinds of blood, namely basophil, eosinophil, lymphocyte, monocyte, neutrophil, platelets, and red blood cells (RBCs). The analysis also tested the proposed method on the well-known Blood Cell Count and Detection (BCCD) dataset, which was a common benchmark in this field, for comparing the performance. Performance of the model relating to past studies was assessed through this process. Results: The investigation used GhostBottleneck and C3Ghost modules to reduce GFLOPS by 45.56% and the number of parameters by 76.55%. Mean average precision (mAP50) of 0.984 was achieved using recommended method. Additionally, on BCCD, the method scored 0.94 on New Cell Dataset. Conclusion: Modifications performed to YOLOv8 design significantly increased its blood cell detection efficiency and effectiveness. The improvements showed that the changed model was suitable for real-time use in settings with constrained resources. Keywords: Blood Cell Detection, C3Ghost, Ghostbottleneck, YOLOv8
Improving Café Reputation: Machine Learning Analytics for Predicting Customer Engagement on Google Maps Anisah, Siti; Wasesa, Meditya
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 1 (2025): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.1.91-102

Abstract

Background: Online reviews is a powerful tool in shaping customer decisions, as they significantly influence a business’s reputation and the ability to attract new customer. Given the growing reliance on digital platforms, understanding engagement levels is crucial for business that want to enhance online presence. By analyzing these customer activities, business owners can leverage Machine Learning (ML) analytics to predict engagement on Google Maps reviews. Objective: This study aimed to develop the most suitable ML model in order to predict customer engagement levels in café business on Google Maps, and determine the online review features that have the greatest impact on engagement. Additionally, the analysis aimed to provide actionable recommendations to help business owners improve online reputation and engagement strategies. Method: A total of 5,626 online reviews data were collected using web scraping methods during the analysis. The data was then preprocessed by extracting major review features, calculating engagement levels, and addressing class imbalance with SMOTE method. In the study, K-Means clustering was used to segment engagement levels, while sentiment analysis through VADER Lexicon was applied to measure sentiment content. Various ML models were trained and validated using a 10-fold cross-validation method. Finally, Analysis was conducted using Spearman's correlation to identify relationships among features derived from the best-performing ML models. Results: The result of the analysis showed that Random Forest model achieved the highest accuracy and PR AUC in predicting engagement levels. The four most influential factors were review length (16.23%), photos (15.57%), total rating (12.35%), and author review count (10.19%). Spearman's correlation analysis showed a positive relationship among review length, photos, and author review count, signifying the combined impact on engagement levels. Conclusion: This study described the effectiveness of Random Forest model in predicting engagement levels in Google Maps reviews. Specifically, the model identified review length, photos, total rating, and author review count as the key factors influencing engagement. These results would provide valuable guidance for business owners that desire to improve customer engagement and online reputation. Building on this, future studies should explore larger datasets, integrate additional features, and examine how the engagement contribute to long-term customer retention. Keywords: Online Reputation Management, Customer Engagement, Behavior, Machine Learning, Google Maps Review, Predictive Analytics
Domain-Specific Fine-Tuning of IndoBERT for Aspect-Based Sentiment Analysis in Indonesian Travel User-Generated Content Perwira, Rifki Indra; Permadi, Vynska Amalia; Purnamasari , Dian Indri; Agusdin , Riza Prapascatama
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 1 (2025): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.1.30-40

Abstract

Background: Aspect-based sentiment analysis (ABSA) is essential in extracting meaningful insights from user-generated content (UGC) in various domains. In tourism, UGC such as Google Reviews offers essential feedback, but the challenges associated with processing in Indonesian language, including the unique linguistic characteristics, pose difficulties for automatic sentiment, and aspect detection. Recent advancements in transformer-based models, such as BERT, have shown great potential in addressing these challenges by providing context-aware embeddings. Objective: This research aimed to fine-tune IndoBERT, a pre-trained Indonesian language model, to perform information extraction and key aspect detection from tourism-related UGC. The objective was to identify critical aspects of tourism reviews and classify their sentiments. Methods: A dataset of 20,000 Google Reviews, focusing on 20 tourism destinations in DI Yogyakarta and Jawa Tengah, was collected and preprocessed. Multiple fine-tuning experiments were conducted, using a layer-freezing method by adjusting only the top layers of IndoBERT, while freezing others to determine the optimal configuration. The model's performance was evaluated based on validation loss, precision, recall, and F1-score in aspect detection and overall sentiment classification accuracy. Results: The best-performing configuration involved freezing the last six layers and fine-tuning the top six layers of IndoBERT, yielding a validation loss of 0.324. The model achieved precision scores between 0.85 and 0.89 in aspect detection and an overall sentiment classification accuracy of 0.84. Error analysis revealed challenges in distinguishing neutral and negative sentiments and in handling reviews with multiple aspects or mixed sentiments. Conclusion: The fine-tuned IndoBERT model effectively extracted key tourism aspects and classified sentiments from Indonesian UGC. While the model performed well in detecting strong sentiments, improvements are needed to handle neutral and mixed sentiments better. Future work will explore sentiment intensity analysis and aspect segmentation methods to enhance the model's performance. Keywords: Aspect-Based Sentiment Analysis, Fine-tuning, IndoBERT, Sentiment Classification, Tourism Reviews, User-Generated Content
Exposing Causative Factors on Software Discontinuity using an Elaborative Qualitative Method Gandhi, Arfive; Kusumo, Dana Sulistiyo; Sardi, Indra Lukmana
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 2 (2025): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.2.103-114

Abstract

Background: Software discontinuity due to the inability to accommodate the needs of users is a the significant challenge facing the software development life cycle. This implied that the development team must be capable of producing software with extended lifespan, including the ability to detect outages early, to maintain continuity. Organizations need to determine the contributing and inhibiting factors responsible for discontinuity usage.  Objective: This research aimed to explore the factors that contribute and inhibit the discontinuation of software use in organizations as well as the prevention strategies.  Methods: The summative content analysis technique was used to capture, codify, and classify statements from respondents to discover usage pattern. Data were collected through interview and questionnaire techniques with 10 respondents from various Indonesian companies. The respondents had various sectoral backgrounds in software usage for more than a year. The data collected were compared, contrasted, and synthesized to deliver a holistic pattern among respondents.  Results: The result showed that 10 key factors contributed to software discontinuity, namely Loss of Perceived Usefulness (LUS), Loss of Perceived Ease of Use (LEU), Decreased Effort Expectancy (DEX), Decreased Performance Expectancy (DPX), Social Influence (SOI), Lack of Facilitating Conditions (LFC), Decreased Price Value (DPV), Lack of Habit (LHB), Hedonic Motivation (HDM), and Loss of Perceived Behavioral Control (LBC). The factors were further categorized into three big issues, including Software Usability (LUS, LEU, DEX, and DPX), External Triggers (DPV, SOI, and LBC), and Risk Management after Discontinuity (LFC, LHB, and SOI). Furthermore, the results indicated that nine factors contributed to software discontinuity except HDM with LEU and LUS having weak significance since most respondents stated partial agreement and disagreements.  Conclusion: This research employed a rigorous qualitative method to validate the factors in the proposed software discontinuity model with 10 causative factors. The acquired knowledge is expected to aid organizations or related development units to build software that accommodates user needs, including meeting long-term business targets.  Keywords: Software, Software Discontinuity, Influencing Factors, Qualitative Method
Assessing Information Security Awareness Among Indonesian Government Employees: A Case Study of the Meteorology, Climatology, and Geophysics Agency Prasetyo, Aji; Aji, Rizal Fathoni; Wibowo, Wahyu Setiawan
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 2 (2025): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.2.126-142

Abstract

Background: Cybersecurity is important for government agencies and the usefulness shows the need for a thorough understanding of information security awareness (ISA) among employees in order to enhance protective measures and ensure compliance with regulations. The Meteorology, Climatology, and Geophysical Agency (BMKG) of Indonesia is very important in providing essential national data and this responsibility shows the need to assess and promote ISA among the employees. The efforts to ensure a robust ISA culture can allow BMKG to safeguard sensitive meteorological and geophysical data, strengthen operational resilience, maintain public trust, and mitigate potential cyber threats that are capable of compromising national security.  Objective: This study aimed to evaluate the level of organizational ISA among employees at BMKG and to improve measures considered important.  Methods: The Human Aspects of Information Security Questionnaire (HAIS-Q) was administered as the reference model to assess the knowledge, attitudes, and behaviors of employees regarding information security. A descriptive statistical analysis and Partial Least Squares Structural Equation Modelling (PLS-SEM) were further applied to analyze data from 459 BMKG employees across various security domains, including password management, email use, internet use, social media use, mobile device security, and incident reporting.  Results: The results showed that BMKG employees possessed a high overall level of ISA (88.06%) with the average knowledge, attitudes, and behaviors recorded to be 88.06%, 81.89%, and 80.74%, respectively. Meanwhile, specific areas such as email use (78.70%) and mobile device use (73.19%) had only moderate awareness. The structural model analysis also showed that behavior exerted the most significant influence on ISA (β = 0.423), followed by attitude (β = 0.289) and knowledge (β = 0.214).  Conclusion: The overall awareness level was positive but there was a need for targeted efforts in password management, email use, and mobile device security to improve ISA practices. Moreover, the implementation of comprehensive information security policies, regular training, and organizational support was suggested to be important for fostering a robust security culture within BMKG.  Keywords: Information Security Awareness, Cybersecurity, BMKG, PLS-SEM, Government Employees, Indonesia
Factors Influencing the Diffusion of Blockchain Technology in the Indonesian Goverment Noman, Eltyasar Putrajati; Gwenhure, Anderson Kevin
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 2 (2025): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.2.215-225

Abstract

Background: Blockchain can improve the security and efficiency of government information systems. However, the adoption of this technology in Indonesia is still limited, especially in the government sector. Previous studies have emphasized the importance of regulatory and legal aspects in blockchain implementation. This condition is a challenge and an opportunity to examine the factors that influence the diffusion of blockchain innovation in the Indonesian government.  Objective: This study aims to identify and analyze the factors that influence the diffusion of blockchain technology in the Indonesian government through hypothesis testing and conceptual model development, as well as to determine the current stage of blockchain technology diffusion in the Indonesian government.  Methods: This study uses data from a questionnaire survey of 24 government agencies in Indonesia, representing various levels of central, provincial, district, and city, and focusing on the technology sector. A total of 192 responses were successfully collected. The collected data were analyzed using SmartPLS software to test the validity and reliability of the instrument, research hypothesis, and proposed conceptual model, and the results of the hypothesis test were used to determine the current stage of blockchain technology diffusion in the Indonesian government.  Results: The study's results indicate that the research instruments used are valid and reliable and meet the requirements for use in this study. Of the eight hypotheses proposed, three were accepted, and five were rejected. The tested conceptual model showed good agreement with the empirical data.  Conclusion: This study concludes that relative advantage and stakeholder roles are key factors significantly influencing the Indonesian government's intention to adopt blockchain technology. In contrast, complexity, regulation, top management support, and competence do not significantly influence adoption intentions. The diffusion of blockchain technology in the Indonesian government is still in the knowledge stage, so the decision to adopt it has not been reached. The implication is that the government needs to prioritize blockchain advantages and actively involve stakeholders, such as experts and developers, in efforts to adopt this technology.  Keywords: Diffusion of Innovation, Blockchain, Information Systems, E-Government, Information Technology Management 
User Experience as a Predictor of E-commerce Continuation Intention in Indonesia: Examining the Role of Shopping Orientation as a Moderator Widyaningrum, Premi Wahyu; Astuti, Endang Siti; Yulianto, Edy; Mawardi, Mukhammad Kholid
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 2 (2025): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.2.187-199

Abstract

Background: The integration of Stimulus-Organism-Response (SOR) framework and Technology Acceptance Model (TAM) is still in need of improvement, particularly in studies examining individual behavior in Indonesian e-commerce context. A common challenge in e-commerce adoption is individual willingness and intention to adopt, which is influenced by previous user experience. Consequently, there is a need for the establishment of standard to measure user experience in e-commerce.  Objective: This study aims to measure the post-adoption experience of e-commerce user, which will shape attitude and influence future continuance intention (CI).  Methods: This study integrated SOR and TAM frameworks, followed by the collection and analysis of data from 263 respondents using Structural Equation Modeling-Partial Least Squares (SEM-PLS). Among the four hypotheses proposed, two represented novel contributions to the existing literature.  Results: The results showed a positive and significant influence of Interaction Experience (IE), Sense Experience (SE), and Flow Experience (FE) on Attitude Toward Using (ATU). The data analysis also indicated a positive and significant effect of ATU on Continuance Intention (CI). However, the influence of ATU on CI became insignificant when moderated by Shopping Orientation (SO).  Conclusion: Based on the results, not all hypotheses proposed in this study are supported. However, the results provide both theoretical and practical contributions.  Keywords: SOR, TAM, User Experience, Continuance Intention, e-commerce 

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