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INDONESIA
Journal of Applied Data Sciences
Published by Bright Publisher
ISSN : -     EISSN : 27236471     DOI : doi.org/10.47738/jads
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
One of the current hot topics in science is data: how can datasets be used in scientific and scholarly research in a more reliable, citable and accountable way? Data is of paramount importance to scientific progress, yet most research data remains private. Enhancing the transparency of the processes applied to collect, treat and analyze data will help to render scientific research results reproducible and thus more accountable. The datasets itself should also be accessible to other researchers, so that research publications, dataset descriptions, and the actual datasets can be linked. The journal Data provides a forum to publish methodical papers on processes applied to data collection, treatment and analysis, as well as for data descriptors publishing descriptions of a linked dataset.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 639 Documents
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A Data-Driven Structural Framework Linking Digital Transformation and Islamic Leadership to Academic Performance Anuar Sanusi; MS Hasibuan; Muprihan Thaib; Yulmaini Yulmaini; Ariza Ariza
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1504

Abstract

Digital transformation has become a strategic organizational capability for improving academic performance in higher education; however, existing studies generally examine leadership, organizational values, digital transformation, and quality assurance as separate phenomena, providing limited understanding of how these factors interact to enhance institutional performance. This study develops, tests, and validates an integrated structural framework explaining how Islamic Leadership and Islamic Values influence Academic Performance through Digital Transformation and National Higher Education Standards. A sequential explanatory mixed-methods design was employed using data collected from 196 academic leaders, faculty members, and administrative staff. The quantitative data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), followed by Importance-Performance Map Analysis (IPMA) and SWOT analysis to identify institutional strategic priorities. The measurement model demonstrated satisfactory reliability and validity (Cronbach's α = 0.896-0.921, Composite Reliability = 0.921-0.938, AVE = 0.660-0.717). The structural model explained 70.4% of the variance in Digital Transformation, 58.2% in National Higher Education Standards, and 68.1% in Academic Performance. Digital Transformation emerged as the strongest predictor of both National Higher Education Standards (β = 0.764, p 0.001) and Academic Performance (β = 0.601, p 0.001), while Islamic Leadership (β = 0.495, p 0.001) and Islamic Values (β = 0.398, p 0.001) significantly enhanced Digital Transformation. Furthermore, National Higher Education Standards significantly mediated the relationship between Digital Transformation and Academic Performance, while IPMA and SWOT identified Digital Transformation as the highest managerial priority and positioned the institution within a growth-oriented strategic quadrant. This study contributes by introducing and validating an integrated organizational capability framework that explains how value-based leadership is translated into superior academic performance through digital transformation and institutional quality assurance, providing a novel data-driven perspective for higher education digital transformation research and practice.
Factors Affecting Financial Reporting Quality of Commercial Banks: The Mediating Role of Accounting Information System Quality Tai Nguyen Thanh; Hien Nguyen Anh
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1486

Abstract

This study examines the determinants of financial reporting quality in commercial banks, with accounting information system quality serving as a theoretically specified mediating mechanism. To avoid conceptual ambiguity, financial reporting quality is defined as the broader quality of the reporting process and its information outputs, whereas financial statement quality is treated as one observable manifestation of that broader construct. Drawing on the information systems success perspective, the resource-based view, and institutional theory, the study links human competence, organizational control, regulatory compliance, managerial support, and technological application to accounting information system quality and financial reporting quality. The qualitative phase used semi-structured discussions with 30 officers responsible for accounting, internal control, regulatory reporting, and information systems in commercial bank branches to refine the model and questionnaire. The quantitative phase surveyed accountants, controllers, internal auditors, financial officers, and IT staff involved in accounting systems at commercial banks. Of 700 questionnaires distributed, 635 valid responses were retained for structural equation modeling. Reliability, convergent validity, discriminant validity, multicollinearity, common-method diagnostics, structural paths, and indirect effects were assessed. The results support all proposed hypotheses. Accounting staff competence, internal control system, regulatory compliance, top management support, and information technology application positively affect accounting information system quality. Information technology application shows the largest standardized association with accounting information system quality, although coefficient differences are interpreted cautiously because formal pairwise coefficient-comparison tests were not conducted. Accounting information system quality positively affects financial reporting quality, and information technology application also has a direct positive effect. Product-of-coefficients estimates indicate mediated pathways through accounting information system quality, with the strongest indirect pathway from information technology application to financial reporting quality. The findings suggest that commercial banks should improve financial reporting quality by combining digital transformation, accounting information system enhancement, internal control strengthening, staff competence development, managerial commitment, and regulatory compliance.
Circular Economy Determinants in Electronics Supply Chain: A Pythagorean Fuzzy Clustering Approach Samiksha Budakoti; Vishal Gupta
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1420

Abstract

The realignment from traditional linear models to circular economy has become indispensable in Electronics industry of developing nations such as India. This transition is crucial to combat issues related to resource scarcity and burgeoning e-waste. This research seeks to determine and classify the pivotal determinants impacting circular economy assimilation in India’s electronics supply chain. An extensive literature review coupled with feedback from industry and academic experts, identified twenty-three such determinants. These determinants were integrated with six supply chain stages in order to develop a structured questionnaire. A five-point linguistic scale was used in the questionnaire to gather expert responses on each determinant-stage combination. The experts’ responses were then converted to Pythagorean fuzzy numbers for quantitative analysis of the information. Using multi-criteria decision-making framework involving aggregation, score function and normalization, a de-fuzzified similarity matrix of determinants was obtained. Ward’s method of agglomerative hierarchical clustering in RStudio was applied to the similarity matrix in order to obtain four different clusters of determinants. Strategic and technological determinants formed cluster 1; regulatory and resource-efficiency determinants were highlighted in cluster 2; cluster 3 reflected market and network drivers; macro-institutional and contextual determinants were represented in cluster 4. These clusters together indicate varied yet interconnected determinants affecting implementation of circular economy practices in India’s electronics supply chain. The robustness of the study was also validated by performing sensitivity analysis of the results. This analysis resulted in a mean Adjusted Rand Index of 0.862 reflecting a high degree of consistency in the analytical results. By integrating two robust quantitative methodologies, this research introduces a novel empirical framework for classification of circular economy determinants. This study can aid policymakers, industry practitioners and academicians to formulate coordinated measures for circular economy adoption in India’s electronics supply chains.
An Optimized U-Net and MobileNetV2 Framework for Accurate and Efficient Brain Tumour Classification from MRI Images R Elavarasi; S Murugesan; S Ramalingam; P Kanimozhi; Harprith Kaur R S
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1468

Abstract

Accurate brain tumour identification from magnetic resonance imaging (MRI) requires models that can effectively localize tumour regions while maintaining high classification performance and computational efficiency. This study presents an integrated U-Net–MobileNetV2 framework for automated brain tumour analysis using MRI images. The proposed pipeline incorporates image thresholding, morphological processing, contour-based cropping, bicubic resizing, normalization, and data augmentation prior to model training. U-Net is employed to segment tumour regions and preserve diagnostically relevant spatial information, while MobileNetV2 performs lightweight classification using segmentation-guided representations. The dataset consists of 512 × 512-pixel brain MRI images divided into 80% training, 10% validation, and 10% testing subsets. Model performance is evaluated using accuracy, precision, recall, and F1-score and compared with CNN, Random Forest, Naïve Bayes, and Support Vector Machine classifiers. The proposed framework achieves 99.52% accuracy, 97.57% precision, 98.05% recall, and 97.81% F1-score, outperforming all evaluated baseline models. Compared with the strongest baseline, SVM, the proposed method improves accuracy by 1.43 percentage points. These results demonstrate that combining segmentation-driven tumour localization with lightweight deep feature learning provides an effective framework for accurate and computationally efficient MRI-based brain tumour classification.
A Hybrid Intelligent Cybersecurity Assessment System For Electronic Document Management Systems Assylzhan Svanov; Assel Omarbekova; Gulmira Bekmanova; Alibek Barlybayev; Bibigul Razakhova; Lena Zhetkenbay; Magripa Saukhanova; Aizhan Nazyrova; Zhanar Lamasheva
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1492

Abstract

Electronic document management systems concentrate confidential and commercially sensitive information behind a single perimeter, making them a high-priority cyberattack target, while existing assessment methods remain largely static, checklist-based, or single-technique, poorly capturing configuration dynamics or prioritizing measures by expected risk reduction. The objective is to develop and validate an intelligent system for the quantitative, explainable cybersecurity assessment of such systems. The novelty and contribution are a hybrid architecture jointly integrating Mamdani fuzzy inference, a stacking ensemble of machine-learning classifiers (random forest, gradient boosting, and a multilayer perceptron), and a Bayesian threat network with an attack graph, unified by a shared domain ontology of document-management assets and threats weighted by confidentiality, integrity, and availability. These heterogeneous estimates are aggregated into a composite cybersecurity assessment index via a fuzzy analytic hierarchy process with adaptive re-weighting from confirmed incidents, while a two-level explainability layer traces each score to its features, fired rules, and probable attack paths. The system was evaluated on 10,239 labelled configuration states from an operational deployment using cross-validation, comparison against seven baseline models, and an ablation study. It achieved the best results among all compared approaches, with an F1-score of 0.946, a Matthews correlation coefficient of 0.927, and an area under the ROC curve of 0.972 – a gain of 2.4 percentage points over the strongest baseline and 10.4 over logistic regression – and the ablation study confirmed that the fuzzy and Bayesian components contribute complementary gains. These findings show that combining data-driven learning with expert-interpretable reasoning yields a more accurate and stable assessment than any single paradigm, and indicate that the framework can support continuous, evidence-based cybersecurity monitoring in document-centric organizations, with future work on adversarial robustness and multi-organization validation.
A Hybrid AES–NTRU Cryptographic Framework with Deterministic Dynamic S-Box for Secure Data Transmission Purwanto Simamora; Poltak Sihombing; Mohammad Andri Budiman; Jos Timanta Tarigan
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1307

Abstract

The rapid expansion of interconnected sensing and monitoring systems has significantly increased the volume of digital data exchanged across distributed environments. While these systems provide substantial benefits for real-time monitoring and automated decision making, they also introduce critical challenges related to data confidentiality and secure communication. This study proposes a hybrid cryptographic framework designed to strengthen secure data transmission in distributed sensing environments. The primary objective of this research is to integrate a symmetric encryption mechanism with a lattice-based public-key cryptographic scheme within a hybrid key protection architecture. In addition, a deterministic round-dependent substitution mechanism is introduced into the symmetric encryption process to provide controlled structural variability during data encryption while preserving compatibility with the standard round-based encryption structure. The proposed framework was implemented in a software-based prototype environment and evaluated through a series of encryption and decryption experiments using representative data payloads that emulate typical communication scenarios in distributed sensing systems. The evaluation focused on three aspects: implementation correctness, computational performance, and diffusion characteristics of the encryption process. Experimental results confirm that all encrypted data payloads were successfully decrypted to their original plaintext without bit errors, demonstrating the functional correctness of the integrated cryptographic architecture. The encryption process achieved an avalanche effect of 47.01 percent, with an average encryption time of 118.99 milliseconds and a decryption time of 5.91 milliseconds in the prototype environment. These results indicate consistent diffusion behavior within acceptable experimental bounds while maintaining reasonable computational overhead for gateway-level communication nodes. The findings suggest that integrating adaptive substitution mechanisms within hybrid cryptographic architectures can provide flexible encryption structures suitable for secure data transmission in distributed monitoring systems, while remaining compatible with widely deployed symmetric encryption frameworks.
Ontology-Driven Adaptive Learning Environment Using Large Language Models for Educational Knowledge Extraction Rakhila Turebayeva; Bulat Kubekov; Yenglik Kadyr; Umut Turusbekova; Aizhan Nazyrova; Zhanar Lamasheva
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1456

Abstract

Enriching educational ontologies automatically from low-resource-language text remains an unsolved integration problem: conventional large language model (LLM)-to-knowledge-graph pipelines require a vocabulary-alignment step and lack hallucination control at the commit boundary. This study removes both bottlenecks and tests whether the resulting ontology can drive measurable learning gains. The core idea is schema co-design: the JSON schema constraining LLM output is the OWL T-box of the target ontology, so extracted records are directly populatable and no alignment step is needed; a literal-presence filter rejects entities absent from the source text before commit, the HermiT reasoner verifies consistency, and Kazakh, Russian and English labels are generated in a single follow-up call. The pipeline (Python 3.9, OWLready2) was benchmarked on 90 Kazakh paragraphs across GPT-4o, Claude 3.5 Sonnet and Gemini 1.5 Pro, and the resulting adaptive textbook was evaluated quasi-experimentally with 65 Grade 6 students (control n = 32, experimental n = 33) in two Astana schools over 16 weeks. Of 312 source chunks, 271 (86.9%) survived the full validation chain, yielding 1,847 OWL individuals, 2,931 object-property assertions and 6,512 data-property annotations across 47 classes; the literal-presence filter intercepted 5.2% of validated responses and the reasoner a further 1.1%. Extraction F1 reached 0.89 (GPT-4o), 0.87 (Claude) and 0.86 (Gemini), with hallucination rates of 4.9–8.3%; the one-time corpus build cost USD 3.47–9.64, with zero marginal LLM cost per learner. The experimental group outperformed controls on task accuracy (+19.4 points, d = 2.30), repeated errors (−53.6%, d = 1.85) and sessions to mastery (−34.3%, d = 1.50), all p 0.001 under Bonferroni correction. The novelty lies in making the ontology T-box itself the extraction schema, combined with pre-commit literal grounding, validated in a real low-resource classroom deployment.
A Bi-LSTM Prediction Model Integrated with GIS for Spatiotemporal Malaria Endemicity Mapping and Early Warning in Indonesia Wellie Sulistijanti; Safaat Yulianto; Abdul Syukur; Ngatimin Ngatimin
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1483

Abstract

Malaria continues to be a major public health challenge in Indonesia, particularly in eastern provinces where transmission patterns are influenced by climatic variability, geographical heterogeneity, and historical incidence trends. This study proposes an integrated spatio-temporal malaria forecasting and early warning framework by combining Bidirectional Long Short-Term Memory (Bi-LSTM), Geographic Information Systems (GIS), and SHapley Additive exPlanations (SHAP). Monthly malaria incidence, climate variables, population data, and provincial spatial data from 12 endemic provinces in Indonesia during 2014–2025 were used. The data were preprocessed through incidence-rate conversion, outlier handling, log transformation, Min-Max normalization, and six-month sliding window segmentation. The proposed Bi-LSTM model was assesed using RMSE, sMAPE, and R², and compared againts Naive Forecasting, SARIMA, and Simple LSTM baselines. The model attained optimol global performance, with an RMSE of 0.0522, sMAPE of 18.39%, and R² of 0.9553. The provincial analysis shows good performance throughout most regions, including high-burden areas like Papua and West Papua, however a decline in relative accuracy was observed in West Nusa Tenggara due to near-zero incidence rates. SHAP analysis revealed that historical malaria incidence was the primary predictor, whereas rainfall emerged as the most significant climatic variable. GIS-based forecasting showed spatial patterns aligned with malaria epidemiology in Indonesia, with Papua exhibiting the gratest predicted incidence in December 2025. These findings demonstrate that the Bi-LSTM–GIS–SHAP framework can support malaria endemicity mapping, interpretable forecasting, and province-level early warning for targeted public health interventions.
Fuzzy Expert System for Melamine Moulding Compound Dye Feasibility Identification: A Case study on Plastic Industry Jansen Wiratama; Santo Fernandi Wijaya; Samuel Ady Sanjaya; Florentina Kurniasari; Hendro Budiyanto; Ala Al Kafri; Nuttaphat Sukchitt
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1498

Abstract

This study addresses a practical decision problem within the melamine industry. Production managers are tasked with determining whether a new Melamine Moulding Compound (MMC) dye is suitable for use, based on testing parameters such as boiling point, processing time, and pressure. Given that these values frequently fall between established expert categories, manual decision-making can be challenging to justify and replicate. Accordingly, this research develops a web-based Fuzzy Sugeno expert system to assess the feasibility of MMC dye. The model incorporates three input variables, each characterized by low, medium, and high fuzzy sets. Expert knowledge is formalized into 27 rules employing zero-order Sugeno consequents for three classes: not feasible, conditionally feasible, and feasible. The system has been implemented as a PHP and MySQL application and is accessible via a publicly available login page. An illustrative example involving MMC103—boiling point of 165 °C, processing time of 65 seconds, and pressure of 55 bar—indicated medium and high membership values across all three parameters, activating eight rules. The aggregate firing strength was calculated as 3.00, the weighted consequent sum amounted to 80.00, and the final Sugeno score was 26.67. This score categorizes MMC103 as feasible. The results demonstrate that the model not only provides a classification label but also displays memberships, active rules, rule consequents, and the final computation, thereby enabling verification by the production manager. Furthermore, the study includes an English version of the application interface with privacy masking features for user data.

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