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International Journal of Advances in Data and Information Systems
Published by Indonesian Scientific Journal
ISSN : -     EISSN : 27213056     DOI : https://doi.org/10.25008/ijadis
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Advances in Data and Information Systems (IJADIS) (e-ISSN: 2721-3056) is a peer-reviewed journal in the field of data science and information system that is published twice a year; scheduled in April and October. The journal is published for those who wish to share information about their research and innovations and for those who want to know the latest results in the field of Data Science and Information System. The Journal is published by the Indonesian Scientific Journal. Accepted paper will be available online (free access), and there will be no publication fee. The author will get their own personal copy of the paperwork. IJADIS welcomes all topics that are relevant to data science, and information system. The listed topics of interest are as follows: Data clustering and classifications Statistical model in data science Artificial intelligence and machine learning in data science Data visualization Data mining Data intelligence Business intelligence and data warehousing Cloud computing for Big Data Data processing and analytics in IoT Tools and applications in data science Vision and future directions of data science Computational Linguistics Text Classification Language resources Information retrieval Information extraction Information security Machine translation Sentiment analysis Semantics Summarization Speech processing Mathematical linguistics NLP applications Information Science Cryptography and steganography Digital Forensic Social media and social network Crowdsourcing Computational intelligence Collective intelligence Graph theory and computation Network science Modeling and simulation Parallel and distributed computing High-performance computing Information architecture
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 168 Documents
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Blockchain Technology Adoption for Life Insurance: Risk, Readiness, and Relevance Susianto, Isyrofi; Yazid, Setiadi; Ermawan, Geri Yesa
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1479

Abstract

Blockchain technology has been widely discussed as a transformative solution for operational inefficiencies in the insurance sector, particularly in automating claims processing, enhancing transparency, and ensuring data immutability. However, adoption within the life insurance industry remains limited. This paper investigates the barriers and potential of blockchain implementation in life insurance through a mapping analysis using the People–Process–Technology (PPT) framework into risk, readiness, and relevance. The research identifies strategic misalignment with existing revenue models, regulatory compliance frictions, and organizational readiness gaps as key obstacles. A five-year cost comparison indicates that while blockchain incurs higher initial investment, it delivers lower operational costs in the long run—particularly in high-volume, deterministic insurance products. Architectural comparisons further highlight the operational advantages and integration challenges of blockchain-based systems over traditional IT infrastructures. The study concludes that although blockchain holds significant promise, its adoption depends on targeted use case selection, organizational transformation, and regulatory alignment.
The Role of Simplified Enterprise Architecture (Mini TOGAF) in Improving Project Management Governance and Decision-Making Budianto, Farhan Alif; Lubis, Muharman; Mukti, Iqbal Yulizar; Budianto, Setyo
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1493

Abstract

The development of information technology in the digital age requires companies to have a system architecture that is aligned with their business strategy. One commonly used framework is The Open Group Architecture Framework (TOGAF). However, the complexity of TOGAF is often an obstacle to its implementation, especially for organizations with limited resources. This study introduces a Mini TOGAF framework—an adaptive simplification of TOGAF 10 artifacts—designed specifically for digital creative enterprises. Unlike previous simplification models that mainly addressed SMEs in traditional industries, this framework integrates agile principles and stakeholder-centered validation cycles, reflecting the current evolution of enterprise architecture practice in 2023–2025. The method used is Design Science Research (DSR) with three main cycles: the Relevance Cycle to identify organizational needs, the Rigor Cycle to review relevant theories and methods, and the Design Cycle to iteratively design and evaluate artifacts. Data was collected through interviews, observations, and literature studies, then validated by the company. The results of the study show that the application of Mini TOGAF can improve architectural understanding, operational efficiency, business agility, and corporate strategy alignment. The simplification of TOGAF artifacts has been proven to reduce the complexity of implementation without reducing the main benefits of the framework. This study contributes to enterprise architecture literature by proposing an adaptive TOGAF 10 simplification model that strengthens the theoretical link between architectural governance and digital business agility. These findings provide practical contributions for organizations in adopting Enterprise Architecture efficiently and adaptively to modern business needs.
Improving Precision in Small Area Proportion Estimation Using Logit Transformation: A Case of Internet Utilization in Papua’s Regencies, Indonesia (2021) Sumarni , Cucu; Alifh , Muhammad; Sholihin , M. Rijalus; Putri , Christiana Anggraeni; Sohibien, Gama Putra Danu
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1438

Abstract

The development of technology and communication reflects economic growth, with internet usage serving as one key indicator. While this indicator is generally available at the national or provincial level, reliable estimates at the regency level remain limited. Small Area Estimation (SAE) methods can address this gap by integrating survey data with census or administrative records. However, the basic SAE model may be less suitable for proportions, particularly in rare cases, due to violations of the normality assumption. This study shows that applying a logit transformation within the SAE framework improves the precision of proportion estimates. Using internet usage in Papua, Indonesia, as a case study, the results demonstrate that the logit-transformed SAE model outperforms both direct survey estimates and the basic SAE model. 
Uneven Transitions in Container Ship Capacity Across Indo-Pacific Economies (2010–2022): Integrating PCA, ANOVA, and Clustering Evidence Setiawan, Ariyono; Otok, Bambang Widjanarko; Handoko, Wisnu; Hadi, Abdul Razak Abdul; Onn, Choo Wou; Arli, Denni
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1442

Abstract

We examine uneven transitions in container ship capacity (TEU per ship) across five Indo-Pacific economies  China, Singapore, Australia, Vietnam, and Indonesia  during 20102022 using an integrated statistical framework that combines ANOVA, Welch ANOVA, GamesHowell post-hoc tests, Principal Component Analysis (PCA), and clustering. Results reveal persistent divergence: China and Singapore maintain high-capacity fleets (>10,000 TEU/ship), Australia stabilizes in the mid-tier range (~7,000 TEU/ship), while Indonesia and Vietnam experience rapid but low-level growth (<6,000 TEU/ship). ANOVA confirms significant cross-country differences (F=28.33; p<0.001; 0.65), with Welch ANOVA yielding consistent results under unequal variances (p<0.01). PCA indicates one dominant component (PC199.5%) explaining most variance, forming three readiness clusters: high, medium, and low capacity economies. These patterns suggest that policy inertia, infrastructure bottlenecks, and green transition constraints drive the uneven capacity development. The study contributes by introducing TEU per ship as a cross-national indicator for maritime readiness, linking statistical divergence to SDG targets 8, 9, 10, 13, and 14, and offering empirical guidance for low-carbon fleet transition and port modernization in emerging economies..
Comparasion Of Weather Classification Methods On Weather Images Using GLCM Features With Random Forest And Catboost Algoritms Noorhafizi, Muhammad; Saragih, Triando Hamonangan; Mazdadi, Muhammad Itqan; Muliadi, Muliadi; Herteno, Rudy; Rozaq, Hasri Awal Akbar
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1456

Abstract

Weather image classification is an essential process for improving automated weather information systems. However, most existing studies rely on numerical meteorological data and rarely utilize the textural characteristics embedded in atmospheric imagery. This study addresses that limitation by applying the Gray Level Co-Occurrence Matrix (GLCM) for texture feature extraction combined with Random Forest (RF) and CatBoost algorithms for classification. The dataset, obtained from Kaggle, consists of 1,125 weather images categorized into four classes: cloudy, rain, shine, and sunrise. All images were uniformly normalized and augmented using four rotation angles (0°, 45°, 90°, 135°). GLCM features were extracted with a pixel distance of 1 and gray-level quantization of 8, generating four statistical attributes: contrast, correlation, energy, and homogeneity. Both algorithms were optimized through parameter tuning and evaluated using a 5-fold cross-validation scheme with an 80:20 split ratio. Results show that the Random Forest model (n_estimators = 100, max_depth = 10, random_state = 42) achieved the highest accuracy of 92.43% (±1.12), precision of 92.50%, recall of 92.43%, and F1-score of 92.42%. In comparison, CatBoost (iterations = 100, learning_rate = 0.1, depth = 6) achieved an accuracy of 68.88% (±2.31). The findings demonstrate that GLCM feature extraction combined with Random Forest offers superior stability and accuracy for weather image classification, providing a foundation for efficient and interpretable weather information systems.
Systematic Literature Review - Turning Heads: Quantifying Hedonic, Eudaimonic, and Behavioural Engagement in 360° Tours Rizal, Chairul; Saari, Erni Marlina Binti
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1477

Abstract

This systematic literature review (SLR) examines the relationship between 360° virtual experiences, user engagement, and conversion outcomes. It explores how immersion influences emotional, cognitive, and behavioral responses across fields such as education, tourism, cultural heritage, retail, and gaming. Drawing from empirical studies published between 2013 and 2025, the review investigates how factors like presence, perceived control, enjoyment, and cognitive load mediate engagement in immersive environments. Experimental and quasi-experimental designs dominate the analyzed studies, often using frameworks such as PLS-SEM to assess mediation and moderation effects. Findings show that interactivity (hotspots, mini-maps, gamification) and guidance mechanisms (narration, AI support) tend to enhance behavioral engagement, while high visual fidelity, contextual relevance, and user-centered control strengthen both hedonic and utilitarian conversions, including purchase intentions and revisit likelihood. However, gaps remain in methodological rigor, including small samples and inconsistent engagement metrics. The review concludes that effective 360° virtual experiences integrate emotional engagement with usability to transform immersion into measurable outcomes. It recommends future research to emphasize cross-domain comparisons, standardized measures, and longitudinal studies to better understand how immersive systems sustain engagement and influence conversion behavior over time.
Collaborative Governance in Smart City Makassar: Actors-Networks Across Capacity, Infrastructure, and Policy Domains Sutanto, Muh. Awalil Resky; Nurmandi, Achmad; Lawelai, Herman; Prakoso, Velandani; Jovita, Hazel; Sohsan, Imron; Agustiyara, Agustiyara
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1478

Abstract

This study explores the collaborative governance approach in implementing Smart City Makassar, which faces challenges including low digital literacy, fragmented data, and weak institutional coordination addressing persistent issues of low digital literacy, fragmented data systems, and limited inter-agency coordination that remain underexamined in smart city governance research. Using a qualitative case study method supported by NVivo-assisted network content analysis, data was collected from policy documents, online news, and interviews. The research identifies actor roles, network patterns, and cross-domain integration of policies, infrastructure, and capacities to support smart city sustainability. Findings reveal that digital technologies operate as institutional infrastructures enabling cross-sector interoperability and data-driven coordinationin data-driven decision-making. A tripartite network structure emerges, with government as central orchestrators, private sector as co-innovators driving technological deployment, and citizens as active contributors shaping service responsiveness Matrix-query analysis indicates strong policy–infrastructure integration, highlighting regulation as the structural anchor of collaboration. The success of Smart City Makassar is shaped by the alignment of technical capacity development, regulatory coherence, and adaptive collaborative governance mechanisms. The study positions Makassar as a model for inclusive smart cities in Southeast Asia, contingent upon strengthened public trust and resilient digital infrastructure.
Integration of Machine Learning and GAP Analysis for a Data Driven Lecturer Performance Evaluation System Purba, Ramen Antonov; Bukit, Tori Andika
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1481

Abstract

The objective of this research is to design and implement a performance evaluation system that combines Machine Learning for data processing, predictive modeling, and pattern recognition with the GAP method to measure discrepancies between expected competencies and actual performance. Eight primary criteria were cooperation, communication, initiative, alertness, discipline, leadership, problem solving, time usage each consisting of several sub-criteria. The study involved 18 lecturers, and the evaluation was conducted using a web-based decision support system equipped with machine learning models trained to classify performance levels and identify underlying patterns within the assessment data. System usability was examined through four categories: ease of use, completeness, accuracy, and interface composition. The results show that the integrated system successfully identified the highest-performing lecturer (Lecturer 7) with a score of 6.1801, followed by Lecturer 12 with 4.9314 and Lecturer 4 with 4.1157. Usability testing also yielded positive outcomes, with scores of 89% for ease of use, 87% for completeness, 90% for accuracy enhanced through machine learning validation and 88% for interface composition. These results produced an overall average of 88%, classifying the system as Very Worthy. In conclusion, integrating Machine Learning and GAP Analysis in a web-based DSS significantly improves the effectiveness and efficiency of lecturer performance evaluation. The system accelerates data processing, enhances assessment quality, and strengthens decision-making through predictive analytics and automated classification. This framework offers a valuable reference for future performance evaluations in higher education institutions seeking accountability, transparency, and data-driven decision-making.
Performance Evaluation of AdamW, RMSProp, and Nadam Optimizers on EfficientNetB2 Model for Image Data Classification Damayanti, Fanita; Surono, Sugiyarto; Thobirin, Aris
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1482

Abstract

This study examines the effect of different optimization algorithms on the performance of the EfficientNetB2 model in classifying lung and colon histopathology images. Three commonly used optimizers AdamW, RMSprop, and Nadam were analyzed to compare their influence on convergence trends, classification accuracy, and overall learning consistency. Using a five-class dataset covering benign and malignant tissue samples, the experimental results show that all three optimizers are able to deliver reliable predictions, although with varying performance characteristics. RMSprop emerges as the most effective optimizer, achieving the highest accuracy across all evaluation stages, with 99.05% during training, 99.16% on validation, and 98.72% on testing, along with the lowest loss values. This indicates that RMSprop facilitates faster and more stable convergence compared to the other two methods. AdamW also demonstrates strong predictive performance but shows limitations when distinguishing cancer types with closely similar morphological structures. Nadam attains high accuracy in early stages yet exhibits lower initial stability than RMSprop. Overall, pairing EfficientNetB2 with RMSprop provides the most optimal configuration for this classification task. These results offer valuable insights for designing better training strategies and strengthening the effectiveness of medical imaging based computer aided diagnostic systems.
Effectiveness Fine-Tuned Multilingual BERT Model for Sentiments Classification Toward Bali’s Cultural Attractions Utami, Nengah Widya; Saad, Amna Binti; Putra, Made Adi Paramartha; Putra, I Gede Juliana Eka
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1483

Abstract

This study examines the performance of a fine-tuned Multilingual BERT (mBERT) model for sentiment analysis of tourist reviews on Balinese cultural attractions. A multilingual dataset comprising 7,878 user-generated reviews from Google Maps and TripAdvisor was utilized to capture diverse linguistic expressions and visitor perspectives. The research methodology includes: (1) problem formulation and literature review; (2) dataset collection, preprocessing, and tokenization; (3) model training using mBERT as the baseline; (4) fine-tuning for domain adaptation; and (5) comparative evaluation with other Transformer models (XLM-Roberta and Distil-mBERT) and classical algorithms including Logistic Regression, Support Vector Machine, and Naïve Bayes. The results demonstrate a substantial improvement after fine-tuning. The baseline mBERT achieved 85.45% accuracy, while the fine-tuned model reached 92.13% accuracy with an AUC of 0.909, confirming the effectiveness of domain-specific adaptation. Although XLM-Roberta obtained slightly higher performance (93.15% accuracy, AUC 0.946), the fine-tuned mBERT showed stable and competitive results, making it the primary model of this study. Comparisons with classical methods further indicate that Transformer-based approaches provide more balanced and reliable sentiment classification. Sentiment distribution analysis reveals that tourist perceptions are predominantly positive, particularly regarding cultural authenticity and the quality of performances such as the Kecak and Fire Dance. Negative sentiments mainly relate to operational aspects, including crowd management, seating arrangements, and ticketing processes. Overall, this study provides empirical evidence that fine-tuned mBERT can effectively support data-driven evaluation of tourist experiences and deliver actionable insights for improving service quality and sustainability of Bali’s cultural tourism

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