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INDONESIA
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 15 Documents
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Search results for , issue "Vol. 11 No. 2 (2025): June" : 15 Documents clear
Incorporation of IndoBERT and Machine Learning Features to Improve the Performance of Indonesian Textual Entailment Recognition Tandi, Teuku Yusransyah; Abidin, Taufik Fuadi; Riza, Hammam
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.11.2.173-186

Abstract

Background: Recognizing Textual Entailment (RTE) is a task in Natural Language Processing (NLP), used for question-answering, information retrieval, and fact-checking. The problem faced by Indonesian NLP is based on how to build an effective and computationally efficient RTE model. In line with the discussion, deep learning models such as IndoBERT-large-p1 can obtain high F1-score values but require large GPU memory and very long training times, making it difficult to apply in environments with limited computing resources. On the other hand, machine learning method requires less computing power and provide lower performance. The lack of good datasets in Indonesian is also a problem in RTE study.  Objective: This study aimed to develop Indonesian RTE model called Hybrid-IndoBERT-RTE, which can improve the F1-Score while significantly increasing computational efficiency.  Methods: This study used the Wiki Revisions Edits Textual Entailment (WRETE) dataset consisting of 450 data, 300 for training, 50 for validation, and 100 for testing, respectively. During the process, the output vector generated by IndoBERT-large-p1 was combined with feature-rich classifier that allowed the model to capture more important features to enrich the information obtained. The classification head consisted of 1 input, 3 hidden, and 1 output layer.  Results: Hybrid-IndoBERT-RTE had an F1-score of 85% and consumed 4.2 times less GPU VRAM. Its training time was up to 44.44 times more efficient than IndoBERT-large-p1, showing an increase in efficiency.  Conclusion: Hybrid-IndoBERT-RTE improved the F1-score and computational efficiency for Indonesian RTE task. These results showed that the proposed model had achieved the aims of the study. Future studies would be expected to focus on adding and increasing the variety of datasets.  Keywords: Textual Entailment, IndoBERT-large-p1, Feature-rich classifiers, Hybrid-IndoBERT-RTE, Deep learning, Model efficiency
IT Maturity Model Design and Evaluation for Sustainable Smart Cities Assessment Adwan, Ehab Juma
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.11.2.279-292

Abstract

Background: The Economic Vision for sustainable smart cities (SSC) necessitates a continuous monitoring tool that assesses the long-term planning progress of ‎the Economic ‎maturity level (ML) which is dependent on the Maturity Models (MM) of the Enabling Technology/ICT capabilities as its analyzes, measures the maturity levels (ML) of Smart Cities (SCs), and assesses the Economic ML of the SSCs. Recent MM have several shortcomings such that they are: 1) undedicated and overlapping the SC domains, 2) missing details of SC cases, 3) applying indicators ‎from ambiguous databases, 4) unable to identify SC baseline, 5) lacking easiness, usefulness, decision support, comprehensiveness, ‎timeliness, and usage intention, and/or 6) not targeting the Economic dimension of SSC.  Objective: Aiming at monitoring the long-term planning progress of ‎the SSC’s Economic ‎maturity level (ML), this study ‎‎developed and evaluated an Enterprise Architectural (EA) MM tool (BSSC-ML) that is capable to continuously assess the SC’s transition from ‎‎AS-IS (SC) to TO-BE (SSC’s Economic MLs) by ‎analyzing the Enabling Technology/ICT capabilities, 2) measuring the MLs of Enabling Technology/ICT capabilities based on 20 formulated ‎indicators, and 3) ‎assessing the MLs of Economic SSC based on 30 formulated KPIs.  Methods: The Design Science ‎Research ‎methodology (DSRM) ‎orchestrated the development of BSSC-ML at which design, implementation, data collection & ‎analysis, ‎validation, ‎and evaluation were ‎‎performed by utilizing semi-structured ‎interviews were conducted ‎with 7 officials of the ‎Information & eGovernment Authority (iGA), while the ‎web content analysis and Delphi methods respectively were employed to ‎analyze the ‎official portals while preserving the validation quality and ‎‎to evaluate the model.  Results: The findings revealed 50.3% ML score w.r.t 116 Business services and ‎‎3 sets of 260 Technology/ICT capabilities, 3rd ML score w.r.t Economic ‎SSC, and ‎‎‎88.123%‎ w.r.t evaluation’s acceptance rate.  Conclusion: The study described the development process of BSSC-ML for SSC’ Economic MLs assessment at which the evaluation scores proved its effectiveness as a monitoring too for local and global SCs.  Keywords: Technology/ICT Maturity Model, Smart City, Enterprise Architecture‎, Design and Evaluation, Economic Sustainability
Exploring Enabling Factors of E-Recruitment Adoption in the Public Sector and Its Contribution to Public Value Creation Altino, Iqbal Caraka; Sensuse, Dana Indra; Lusa, Sofian; Putro, Prasetyo Adi Wibowo; Wibowo, Wahyu Setyawan; Cahyaningsih, Elin
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.11.2.200-214

Abstract

Background: E-recruitment systems are increasingly prevalent in the public sector to improve candidate outreach and enhance transparency. Despite their potential, users remain skeptical due to challenges such as recruitment fraud and limited system availability, especially in developing countries like Indonesia. Consequently, it remains unclear how much e-recruitment systems contribute to public value creation. This uncertainty is mainly because there is a lack of research that directly explores the relationship between these systems and public value creation in the public sector, especially in developing countries.  Objective: This research aims to examine the factors that influence the use of e-recruitment systems in the public sector and the impact into creation of public values.   Methods: This quantitative study collected data from 408 respondents via an online survey, all of whom had used Indonesian National Civil Service Agency's e-recruitment system. Data were analyzed using the Partial Least Square—Structural Equation Model (PLS-SEM) method.  Results: The study revealed that system, information, and service quality have a positive impact on perceived usefulness and perceived ease of use and have a positive impact on the use of the e-recruitment system. It also shows that the adoption of an e-recruitment system gives a positive impact on public value creation.  Conclusion: This research highlights the critical role of system information quality in fostering e-recruitment adoption and its positive impact on public value creation in the public sector. These findings enrich previous studies that have not yet explored the direct relationship between the use of e-recruitment systems and public value creation. Future research may investigate technological aspects, like artificial intelligence and virtual reality, that could enhance user experience and the adoption of e-recruitment systems in the public sector.  Keywords: E-recruitment, PLS-SEM, Information System Success Model, Technology Acceptance Model, Public Value Theory
Exploring the Barriers to Public Transport App Adoption Using Innovation Resistance Theory Labiba, Mazaya Nur; Mutiara, Dhina Rotua; Shadrina, Refiany; Handayani, Putu Wuri; Harahap, Nabila Clydea
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.11.2.293-310

Abstract

Background: The adoption of digital solutions in public transportation has transformed mobility services worldwide. However, resistance to innovation remains a significant challenge, preventing the successful implementation of transport applications. Despite advancements in mobile technology and smart transit solutions, many users remain hesitant to adopt new applications due to various barriers, including information quality concerns.  Objective: This study aims to investigate the relationship between information quality and innovation resistance in the adoption of public transport applications. Utilizing the Innovation Resistance Theory (IRT), this research examines how different resistance factors impact the intention to use transport apps.  Methods: A mixed-methods approach was applied, consisting of a quantitative survey with 443 respondents from an urbanized region and analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM). Additionally, qualitative insights were gathered through interviews with 30 individuals, analyzed using content analysis.  Results: Findings indicate that information quality significantly reduces innovation resistance, facilitating the adoption of transport applications. Moreover, usage barriers, value barriers, and tradition barriers negatively affect users’ intention to use transportation apps, while risk, image, and complexity barriers show no significant influence.  Conclusion: This study underscores the critical role of information quality in overcoming resistance to innovation in public transportation applications. The findings provide insights for app developers to enhance data accuracy and usability, as well as for policymakers to improve digital transportation services by addressing key resistance factors.  Keywords: Public Transport App, Innovation Resistance, M-Commerce, Intention to Use, Innovation Resistance Theory, Information Quality, PLS-SEM
A Systematic Literature Review of Topic Modeling Techniques in User Reviews Mustaqim, Ilham Zharif; Suryono, Ryan Randy
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.11.2.238-253

Abstract

Background: The escalating volume of user review data is necessitating automated methods for extracting valuable insights. Topic modeling was a vital method for understanding key discussions and user opinions. However, there was no comprehensive analysis of the scientific work specifically on topic modeling applied to user review datasets, including its main applications and a comparative analysis of the strengths and limitations of identified methods. This study addressed the gap by characterizing the scientific discussion, identifying potential directions, and exploring currently underutilized application areas within the context of user review analysis.  Objective: This study aimed to recognize the implementation trend of topic modeling in various areas and to comprehend the methodology that could be applied to the user review dataset.  Methods: A systematic literature review (SLR) was adopted by implementing Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines within six-year spans, narrowing 1746 to 28 selected primary studies.  Results: The underlying insight was that user reviews had been critical as the primary data for topic modeling in analyzing various applications. Digital banking and transportation applications were the sectors that received the greatest attention. In this context, Latent Dirichlet Allocation (LDA) was the most extensively used method, with a focus on overcoming its limitations by incorporating additional strategies into LDA-based models.  Conclusion: The bibliometric analysis and mapping study practically contributed as a reference when assessing the dominant topic in similar app categories and topic modeling algorithms. Furthermore, this study comprehensively analyzed various topic modeling algorithms, presenting both the strengths and weaknesses of informed selection in relevant applications. Considering the keywords cluster analysis, service quality could be adopted based on the output of the topic modeling.  Keywords: Topic modeling, User review, Systematic literature review, Bibliometric analysis

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