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INDONESIA
Journal of Information Systems and Informatics
Published by Asosiasi Doktor Sistem Informasi Indonesia
ISSN : 26565935     EISSN : 26564882     DOI : 10.63158/journalisi
Core Subject : Science,
Computer Science & IT
Journal-ISI is a scientific article journal that is the result of ideas, great and original thoughts about the latest research and technological developments covering the fields of information systems, information technology, informatics engineering, and computer science, and industrial engineering which is summarized in one publisher. Journal-ISI became one of the means for researchers to publish their great works published two times in one year, namely in March and September with e-ISSN: 2656-4882 and p-ISSN: 2656-5935.
Arjuna Subject : -
Articles 733 Documents
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Navigating Digital Careers: A Multi-Case Study of Women’s Career Decisions in Indonesia’s IT Sector Rinda Faiz Shabira; Mahendrawathi ER
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1390

Abstract

Indonesia’s rapid digital transformation has intensified demand for IT talent, yet female attrition remains high. Aligning with the Sustainable Development Goals’ emphasis on inclusivity, this study examines women’s IT career decisions in Indonesia through the Individual Differences Theory of Gender and IT (IDTGIT). Using a qualitative multi-case design, 12 semi-structured interviews with female IT professionals reveal three career trajectories: stayers (women who remain in IT roles), movers (transitioning to non-IT sectors), and leavers (exiting the workforce completely). Findings show that career decisions are shaped by the interaction between internal drivers (self-actualization, personal characteristics, and career–person fit) and external contexts (organizational culture, relational support, and societal infrastructure). We found that work–family conflict and value reorientation emerge as pivotal mediators triggering transitions across career paths. This study advances IDTGIT by demonstrating its applicability in a developing, collectivist country and introducing a comparative framework across three career decisions. Practically, the findings suggest operationalizing flexible work arrangements through a Results-Only Work Environment (ROWE) and asynchronous tools, while strengthening inclusive policies via gender-responsive health support and accessible childcare to accommodate women’s dual professional and caregiving roles.
Factors Influencing Generative AI Adoption for Knowledge Management in South Africa’s Automotive Sector Diana Maphefo Ratsiku; Mmatshuene Anna Segooa; Cecil Hlopego Kgoetiane
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1393

Abstract

South Africa’s automotive sector is under increasing pressure to sustain competitiveness amid Fourth Industrial Revolution (4IR) transitions, persistent operational inefficiencies, and workforce ageing. Generative AI (GenAI) presents a potential pathway to strengthen knowledge management (KM) by supporting faster knowledge capture, synthesis, retrieval, and decision support. This study identifies the determinants of GenAI adoption for improving KM practices in South Africa’s automotive context. A quantitative, hypothesis-driven design was employed, integrating constructs from the PPOA, TEOG, and IEO frameworks to provide a consolidated adoption perspective. Survey data were collected from 142 industry participants and analysed using SPSS (correlation and multiple regression). The model demonstrated strong explanatory power (Adjusted R² = 0.624, p < 0.001). Results indicate that GenAI adoption is significantly and positively influenced by FATAA ethical principles, KM practices, GenAI tool capability, perceived enjoyment, perceived usefulness, compatibility, competition intensity, organisational size, mimetic pressure, and normative pressure (p < 0.05). In contrast, perceived ease of use and coercive pressure were not statistically significant in this context (p > 0.05). The study contributes a context-specific, integrated adoption model for GenAI-enabled KM in an under-researched setting and offers actionable implications for managers and policymakers focused on responsible, effective GenAI deployment.
A Regulation-Based Readiness Assessment Model for Smart City Development in Indonesia Widyantari Febiyanti; Rizkillah Ridha
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1395

Abstract

This study addresses the lack of a smart city readiness assessment instrument that is explicitly aligned with Indonesia’s urban governance framework, particularly Government Regulation No. 59 of 2022. Existing readiness models often provide generic or technology-centred measures and do not sufficiently operationalise national regulatory requirements, limiting their utility for Indonesian local governments. To fill this gap, the study develops a regulation-based smart city readiness model comprising measurable, context-specific indicators that support readiness evaluation prior to implementation. The research adopts a Design Science Research (DSR) methodology, supported by a PRISMA-guided Systematic Literature Review to identify and synthesise candidate indicators, followed by iterative refinement. Instrument validation was conducted through expert judgement, face validity, and inter-rater reliability testing using Cohen’s Kappa. The final output is a validated readiness assessment instrument consisting of 70 indicators organised into five regulation-derived dimensions: infrastructure, facilities, public utilities, human resources, and suprastructure. Reliability results show strong inter-rater agreement (κ = 0.895), indicating robust and consistent indicator classification. The study contributes a policy-aligned readiness instrument grounded in Indonesia’s regulatory context and provides local governments with a standardised tool to assess readiness, identify development gaps, and support evidence-based planning for sustainable smart city implementation.
Enhancing Recruitment Transparency Using Simple Additive Weighting in Smart City Governance Rahimi Fitri; Nitami Lestari Putri; Abdul Rozaq; Agus Setiyo Budi Nugroho; Upik Upik; Masyita Ratu Diba
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1396

Abstract

The advancement of digital governance requires municipal recruitment processes that are transparent, accountable, and based on measurable criteria. In many local government environments, recruitment remains manual or semi-structured, increasing subjectivity, reducing efficiency, and limiting the traceability of decision outcomes. Although Decision Support Systems (DSS) using the Simple Additive Weighting (SAW) method are widely applied for candidate ranking, prior work often emphasizes technical scoring accuracy with limited attention to Smart City governance needs such as transparency, auditability, and accountable decision justification. This study develops and evaluates a SAW-based DSS to support objective, transparent, and traceable recruitment decisions within a Smart Governance context. Using a quantitative system development approach, candidate attributes were transformed into numerical scores and assessed through weighted criteria: education, work experience duration, English proficiency, age (cost criterion), and relevance of work experience. The SAW computation produced consistent and interpretable rankings, with the highest preference score reaching 98.462, indicating reduced reliance on unstructured subjective judgment. Usability testing using the System Usability Scale (SUS) yielded an average score of 87.6 (“Excellent”), demonstrating strong acceptance and practical feasibility across stakeholder roles. Overall, the proposed system functions as a governance-support tool that strengthens transparency and accountability in public-sector recruitment.
Stacking Ensemble Learning for University Student Dropout Prediction Aden Nia Firdaus; Yoannes Romando Sipayung
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1403

Abstract

Student dropout in STEM programs remains a persistent challenge for higher education institutions, reducing educational quality, weakening retention outcomes, and increasing inefficiencies in resource utilization. This study develops an interpretable Stacking Ensemble Learning approach to predict STEM student dropout risk and identify key academic and socioeconomic determinants that can support data-driven early intervention. Following the CRISP-DM framework, we analyze 3,630 student records from the UCI Machine Learning Repository containing demographic, academic, and socioeconomic attributes. The proposed stacking architecture combines Random Forest, Gradient Boosting, and XGBoost as base learners with Logistic Regression as a meta-learner, while SMOTE–Tomek Links is employed to address class imbalance and reduce boundary noise. Experimental results show that the model achieves strong predictive performance with 90.91% accuracy and ROC–AUC of 95.72%, demonstrating stable discrimination and outperforming individual base models. Feature importance analysis indicates that early academic trajectory variables—especially first- and second-semester success rates, total approved units, and average grades—are the most influential predictors of dropout risk. The proposed framework contributes a practical, interpretable early warning model by integrating stacking ensemble learning with imbalance handling and trajectory-based feature engineering, supporting actionable intervention planning in higher education.
Evaluating Pinterest User Experience and Usability Using AttrakDiff and PLS-SEM Septhia Charenda Putri; Ali Ibrahim; Yadi Utama; Endang Lestari Ruskan; Fathoni Fathoni
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1408

Abstract

The rapid development of visual platforms such as Pinterest necessitates a comprehensive understanding of how functional and emotional aspects jointly influence users’ perception and engagement. This research addresses the gap in user experience (UX) evaluation of visually rich applications by examining the effects of Pragmatic Quality, Hedonic Quality-Stimulation, and Hedonic Quality Identity on the perceived Attractiveness of the Pinterest application. A quantitative approach was employed using the 28-item AttrakDiff instrument, based on data collected from a final sample of 524 valid respondents, predominantly aged 18–25 years, and using Pinterest several times a week. The data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the aid of SmartPLS to examine the relationships among latent variables. The findings demonstrate that the structural model exhibits a high level of explanatory capability, with an R² value of 0.684. With all three UX dimensions exerting positive and statistically significant effects on Attractiveness. PQ shows the strongest influence (path coefficient = 0.457), followed by HQS (0.391) and HQI (0.112). These findings confirm that functional usability remains the primary driver of attractiveness on Pinterest, while hedonic qualities play a complementary role in enhancing user experience. Practically, this research suggests that designers and developers of visual platforms should prioritize efficient functionality while maintaining stimulating and identity-supporting elements to improve overall user appeal.
Event-Based Detection of Provocative Political Discourse on Indonesian Twitter: A Comparative Study of SVM and IndoBERT Evril Fadrekha Cahyani; Ali Nur Ikhsan; Deuis Nur Astrida
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1409

Abstract

Political polarization on Indonesian social media intensified during the August 2025 House of Representatives (DPR) demonstrations, where provocative and sarcastic tweets helped amplify institutional criticism and widen public conflict. This study examines event-based automatic detection of provocative political discourse by comparing a feature-based Support Vector Machine (SVM) classifier with a transformer-based IndoBERT model on a large-scale Indonesian Twitter (X) corpus collected from 15 August to 15 September 2025. Tweets were preprocessed and labeled using a rule-based proxy lexicon to distinguish provocative from neutral content, then both models were trained and evaluated under the same experimental setting. Results show that SVM is highly effective for recognizing explicit provocation expressed through repetitive and lexically salient slogans, whereas IndoBERT provides more stable detection of implicit and context-dependent provocation, including irony and sarcasm that are common in Indonesian political talk online. In addition, temporal exploration indicates sharp spikes in tweet volume that align with key offline protest moments, suggesting a close coupling between street-level mobilization and digital discourse dynamics. Overall, the findings support the use of contextual NLP models within event-centered social media analysis to strengthen scalable monitoring of polarization and to inform early-warning approaches for escalating conflict in Indonesia’s digital public sphere.
A Web-Based Disaster Report Recapitulation System Using the Simple Additive Weighting Method Fanuel Juventino Palandeng; Sondy Campvid Kumajas; Kristofel Santa
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1410

Abstract

Many regional disaster management agencies still manage incident reporting through conventional, semi-manual workflows (e.g., spreadsheets and paper archives). These practices often create repetitive recapitulation tasks, increase the likelihood of data inconsistency, and produce reports that remain largely descriptive—limiting analytical support for timely, evidence-based decision-making. To address this limitation, this study develops a web-based disaster reporting and recapitulation system integrated with the Simple Additive Weighting (SAW) method to generate an event-level Impact Index for prioritization. The system is built using a Prototyping approach, enabling iterative refinement through user feedback to ensure operational fit. SAW is applied using weighted criteria—number of casualties, affected families, damage level, and disaster type—so that each recorded event can be scored and ranked automatically. In contrast to many prior disaster-related SAW applications that emphasize beneficiary selection or aid distribution, this research applies SAW for internal managerial evaluation, prioritizing disaster events themselves to support organizational review and mitigation planning. A case study at BPBD Minahasa Regency demonstrates the system’s feasibility and performance: Black Box Testing achieved a 100% functional success rate, and manual SAW verification confirmed that automated Impact Index outputs are mathematically consistent with theoretical calculations. Overall, the proposed application offers a structured and transparent analytical tool to standardize reporting, accelerate recapitulation, and strengthen decision support through objective impact-based ranking.
From Catch to Consumer: A Process-Centric Model for Resilient Fisheries Value Chains M Arif Kamal; Aunur Rofiq Mulyarto; Usman Effendi
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1413

Abstract

Capture fisheries value chains are highly fragmented and time-sensitive, with multiple stakeholders and perishable products creating persistent challenges for quality management, traceability, and resilience. Many improvement initiatives prioritize digital technologies, yet benefits often remain limited when tools are not embedded in day-to-day business processes. This study proposes a process-centric framework that integrates Porter’s Value Chain with Business Process Model and Notation (BPMN) to diagnose and redesign capture fisheries workflows. Using a case-based approach, As-Is BPMN models are developed across key actors—fishing vessels, landing/auction sites, processors, and distributors—to identify process gaps, information discontinuities, and subjective decision points that weaken end-to-end traceability and quality assurance. Building on these insights, a To-Be process architecture is designed that embeds standardized identifiers, explicit decision logic, quality checkpoints, and traceability controls directly into operational workflows. The findings indicate that process-oriented redesign strengthens information continuity, accountability, and compliance readiness, shifting traceability from a retrospective reporting obligation to an operational management mechanism. Methodologically, the study demonstrates how Porter’s Value Chain can function as a process landscape and how BPMN connects strategic value creation to execution. Practically, the framework offers actionable guidance to improve governance and resilience in capture fisheries value chains.
Design and Implementation of a Hierarchically Interoperable Tag-Based File System using FUSE (PreTFS) Lie Steven Staria Nugraha; Fahri Firdausillah
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1416

Abstract

Traditional hierarchical file systems make semantic organization awkward: a file that naturally belongs to multiple contexts must be forced into a single directory, leaving users to choose an arbitrary location or rely on duplication, linking, or search. This paper presents the design, prototype, and evaluation of a file system that preserves conventional hierarchical standards while adding an opt-in, tag-based semantic layer for multi-context categorization. We describe (i) a design in which tags are represented as directories with reserved, prefixed names and tag intersections are expressed through ordinary path nesting, and (ii) a proof-of-concept implementation that validates feasibility in practice. The implementation, PreTFS, is built as a FUSE (Filesystem in User Space) file system and uses SQLite to store file metadata and content. Results show that the design is realizable and remains compatible with conventional applications and workflows without external tools or specialized APIs. Benchmarking against a native kernel file system (btrfs) reveals expected overheads from user-space indirection and metadata management, measuring approximately ~2–73 ms for metadata-oriented operations and ~1–160 ms for file-content operations. These costs indicate the approach is practical for small-scale environments such as personal information management, where semantic flexibility and interoperability can outweigh peak performance. The novelty lies in a simple, hierarchically interoperable tagging design that enables semantic categorization through standard directory navigation.

Page 70 of 74 | Total Record : 733

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