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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 246 Documents
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The Influence of Gamification Affordance on Customer Loyalty among E-Commerce in Indonesia Zega, Luther Risman Luosaro; Perdanakusuma, Andi Reza; Hariyanti, Uun
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.11.2.115-125

Abstract

Background: The e-commerce industry in Indonesia is experiencing competition due to the rising number of users and price-sensitive consumers, making user loyalty a major challenge for companies. Although gamification, such as task/quest type, was recognized as a strategy to boost loyalty, previous studies showed inconsistent results regarding its impact on hedonic and utilitarian values.  Objective: This study aimed to explore the relationships among task/quest-type gamification affordance (GA), hedonic value (HV), utilitarian value (UV), satisfaction (SA), and loyalty (LOY) among Indonesian e-commerce users.   Methods: A total of 284 e-commerce app users who had engaged in task/quest-type gamification were selected as participants using a convenience sampling method. A quantitative method was adopted and survey data were examined by covariance-based structural equation modeling (CB-SEM) conducted in SmartPLS4.  Results: The analysis showed that gamification affordance significantly impacted users’ perceived hedonic and utilitarian values. An increase in these values significantly enhanced user satisfaction, and strongly correlated with loyalty. Gamification affordance also indirectly influenced loyalty through hedonic value, utilitarian value, and satisfaction.  Conclusion: Task/quest-type gamification affordance effectively enhanced user loyalty in Indonesian e-commerce by improving perceived hedonic and utilitarian values and satisfaction. These results suggested that gamification strategies focusing on task/quest-type elements could foster loyalty in a competitive e-commerce environment.  Keywords: Gamification Affordance, Hedonic Value, Utilitarian Value, Satisfaction, Loyalty
Classification and Counting of Mycobacterium Tuberculosis using YOLOv5 Saurina, Nia; Chamidah, Nur; Rulaningtyas, Riries; Aryati, Aryati
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.11.2.267-278

Abstract

Background: Indonesia is a nation with the third-highest number of tuberculosis (TB) cases worldwide, after China and India. TB detection has been facilitated using YOLOv5 deep learning framework despite previous studies not having incorporated assessment metrics recommended by International Union Against Tuberculosis and Lung Disease (IUATLD).   Objective: This study aims to present a method for classifying and enumerating Mycobacterium tuberculosis by using YOLOv5 architecture with IUATLD evaluation standards. Sputum samples served as the primary medium for identifying the presence of Mycobacterium tuberculosis. In addition, the method showed precise delineation of bacterial boundaries to minimize classification inaccuracies and improve edge clarity through YOLOv5.  Methods: Following the acquisition of microscopic images of TB, the data were resized from 1632x1442 to 640x480 pixels. Annotation was performed using YOLOv5 bounding boxes, and the model was subsequently trained as well as tested according to IUATLD guidelines.  Results: During the analysis, YOLOv5-based classification system produced optimal performance. The model achieved 84.74% accuracy, 87.31% precision, and Mean Average Precision (mAP) score of 84.98%. These metrics showed high reliability in identifying Mycobacterium tuberculosis in the image dataset.  Conclusion: The classification and quantification of Mycobacterium tuberculosis using YOLOv5 framework shows high precision, with mAP score of 84.98%, signifying strong model performance. Additionally, the counting process achieves a MAPE (Mean Absolute Percentage Error) of 0.15%, reflecting excellent prediction accuracy.  Keywords: IUATLD, Tuberculosis, YOLOv5.
Boosting Multiverse Optimizer by Simulated Annealing for Dimensionality Reduction Mutlag, Wamidh K.; Mazher, Wamidh Jalil; Ibrahim, Hadeel Tariq; Ucan, Osman Nuri
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.11.2.254-266

Abstract

Because of its dynamic graph structure and exceptional global/local search abilities, the Multiverse Optimizer (MVO) is widely used in feature selection. The exponential growth of the search space makes finding the optimum feature subset for numerous dimensional datasets quite challenging. Despite that MVO is a promising algorithm, the sluggish convergence issue affects the multi-verse optimizer performance. This work focuses on hybridizing and boosting MVO with the powerful local search algorithm, Simulated Annealing algorithm (SAA), in order to get around MVO limitations and enhance feature selection efficiency in high dimensional datasets. Stated differently, a paradigm known as high-level relay hybrid (HRH) is put forth that sequentially implements self-contained optimization (i.e. MVO and SAA). As a result, the optimal regions are found by MVO and then supplied to SAA in the suggested MVOSA-FS model. Ten high-dimensional datasets obtained from the Arizona State University (ASU) repository were used to verify the effectiveness of the proposed method; the results are compared with other six state-of-the-art feature selection algorithms: Atom Search Optimization (ASO), Equilibrium Optimizer (EO), Emperor Penguin Optimizer (EPO), Monarch Butterfly Optimization (MBO), Satin Bowerbird Optimizer (SBO), and Sine Cosine Algorithm (SCA). The results validate that the proposed MVOSA-FS technique performed better than the other algorithms and showed an exceptional ability to select the most significant and optimal features. The lowest average error rates, classification standard deviation (STD) values, and feature selection (FS) rates are obtained by MVOSA-FS across all datasets.
Enhancing the Comprehensiveness of Criteria-Level Explanation in Multi-Criteria Recommender System Rismala, Rita; Maulidevi, Nur Ulfa; Surendro, Kridanto
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.11.2.160-172

Abstract

Background: The explainability of recommender systems (RSs) is currently attracting significant attention. Recent research mainly focus on item-level explanations, neglecting the need to provide comprehensive explanations for each criterion. In contrast, this research introduces a criteria-level explanation generated in a content-based pardigm by matching aspects between the user and item. However, generation may fall short when user aspects do not match perfectly with the item, despite possessing similar semantics.  Objective: This research aims to extend the aspect-matching method by leveraging semantic similarity. The extension provides more detail and comprehensive explanations for recommendations at the criteria level.    Methods: An extended version of the aspect matching (AM) method was used. This method identified identical aspects between users and items and obtained semantically similar aspects with closely related meanings.   Results: Experiment results from two real-world datasets showed that AM+ was superior to the AM method in coverage and relevance. However, the improvement varied depending on the dataset and criteria sparsity.  Conclusion: The proposed method improves the comprehensiveness and quality of the criteria-level explanation. Therefore, the adopted method has the potential to improve the explainability of multi-criteria RSs. The implication extends beyond the enhancement of explanation to facilitate better user engagement and satisfaction.  Keywords: Comprehensiveness, Content-Based Paradigm, Criteria-Level Explanation, Explainability, Multi-Criteria Recommender System
Optimizing IndoBERT for Revised Bloom's Taxonomy Question Classification Using Neural Network Classifier Darfiansa, Lazuardy Syahrul; Fitriyani; Larasati, Sza Sza Amulya
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.11.2.226-237

Abstract

Background: A major challenge in Indonesian education system is the continued dominance of exam questions that primarily assess basic thinking skills, such as remembering and understanding. In order to effectively nurture students with critical, analytical, and creative thinking skills, the integration of higher-order thinking questions has become increasingly urgent. An effective conceptual framework that can be utilized in this regard is Revised Bloom's Taxonomy (BT). This framework classifies cognitive skills into 6 levels, namely remember, understand, apply, analyze, evaluate, and create. Furthermore, the framework is particularly important as it promotes the development of exam questions that transcend lower-level thinking skills, fostering a deeper and higher level of understanding among students. In this context, automated systems powered by deep learning (DL) have shown promising accuracy in classifying questions based on BT levels, thereby offering practical support for educators aiming to design more meaningful and intellectually stimulating assessments.  Objective: This research aims to develop a classification system that can effectively classify Indonesian exam questions based on BT using IndoBERT pretrained models. These models were combined with Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) classifiers (referred to as IndoBERT-CNN and IndoBERT-LSTM) to determine the model with the highest performance.   Methods: The dataset utilized was self-collected and underwent several stages of preparation, including expert labeling and splitting. Furthermore, preprocessing was conducted to ensure the dataset was consistent and free from irrelevant features related to case folding, tokenization, stopword removal, and stemming. Hyperparameter fine-tuning was subsequently carried out on IndoBERT, IndoBERT-CNN, and IndoBERT-LSTM. Model performance was evaluated using Accuracy, F-Measure, Precision, and Recall.  Results: The fine-tuned IndoBERT model results showed that IndoBERT-LSTM outperformed IndoBERT-CNN. The optimal hyperparameter configuration, batch size of 64 and learning rate of 5e-5, showed the highest performance, achieving Accuracy of 88.75%, Precision of 85%, Recall of 88%, and F-Measure of 86%.  Conclusion: IndoBERT, IndoBERT-CNN, and IndoBERT-LSTM reflected promising results, although the performance of the models was significantly affected by respective architectures and hyperparameter settings. Among the three observed models, IndoBERT was found to perform best with smaller batch sizes and moderate learning rates. IndoBERT-CNN achieved stronger results with a higher learning rate and similar batch sizes. IndoBERT-LSTM recorded the highest accuracy with larger batch sizes for gradient stability. However, IndoBERT was constrained by its focus on Indonesian language, and the interpretability of the predictions made, specifically in relation to expert-labeled data, remained unclear.  Keywords: Bloom’s Taxonomy, CNN, Hyperparameter Fine-Tuning, IndoBERT, LSTM, Question Classification
Aligning Software Product Management with Software Engineering Concepts: A Systematic Literature Review Oruthotaarachchi, Chalani; Wijayanayake, Janaka
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.11.2.143-159

Abstract

Background: Software Product Management (SPM) plays a vital role in the success of many software projects by aligning customer needs with their business objectives and ensuring a seamless and effective software product lifecycle. SPM is established as a collection of tools, techniques, and practices that help an organization accomplish its objectives and enhance the predictability and profitability of software product development. However, despite its significance, SPM research has been fragmented into specific topics having limited SPM literature reviews. This research study addresses this gap and discusses the status of the SPM domain in a more holistic spectrum.  Objective: The study aims to review recent literature on SPM, focusing on the alignment of SPM with software engineering concepts, a product manager’s role, the existing framework, ontologies, and best practices that support ensuring the success of a product manager’s role.  Methods: A systematic literature review was conducted using SCOPUS, IEEE Xplore, ACM Digital Library, ScienceDirect, and ProQuest Central as databases. 71 articles were selected following a rigorous screening process as per the PRISMA 2000 statement.  Results: Integrating SPM and SE is crucial in delivering value-driven software solutions. Available theoretical models and frameworks can help with this integration; however, implementing these frameworks often has challenges. Even though product managers play a vital role in the software lifecycle, they lack sufficient organizational support to enrich their skills and knowledge. Other major challenges are the lack of knowledge to use emerging technologies such as AI for data-driven decision-making processes and the tendency to replace humans with such technologies.  Conclusion: Aligning strategic vision with agile flexibility is important to integrate SPM with SE practices. To improve decision-making and ensure better alignment of SPM with business objectives, organizations have to enhance product managers’ capabilities by leveraging emerging technologies. Research can focus on developing adaptable and user-friendly SPM frameworks that match both medium-scale and large-scale organizational expectations.  Keywords: Organizational Value, Product Manager Role, Software Engineering Integration, Software Product Management, SPM Challenges, SPM Frameworks 
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

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