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Journal of Computing Theories and Applications
Published by Universitas Dian Nuswantoro
ISSN : -     EISSN : 30249104     DOI : 10.62411/jcta
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Journal of Computing Theories and Applications (JCTA) is a refereed, international journal that covers all aspects of foundations, theories and the practical applications of computer science. FREE OF CHARGE for submission and publication. All accepted articles will be published online and accessed for free. The review process is carried out rapidly, about two until three weeks, to get the first decision. The journal publishes only original research papers in the areas of, but not limited to: Artificial Intelligence Big Data Bioinformatics Biometrics Cloud Computing Computer Graphics Computer Vision Cryptography Data Mining Fuzzy Systems Game Technology Image Processing Information Security Internet of Things Intelligent Systems Machine Learning Mobile Computing Multimedia Technology Natural Language Processing Network Security Pattern Recognition Signal Processing Soft Computing Speech Processing Special emphasis is given to recent trends related to cutting-edge research within the domain. If you want to become an author(s) in this journal, you can start by accessing the About page. You can first read the Policies section to find out the policies determined by the JCTA. Then, if you submit an article, you can see the guidelines in the Author Guidelines or Author Guidelines section. Each journal submission will be made online and requires prospective authors to register and have an account to be able to submit manuscripts.
Arjuna Subject : Ilmu Komputer (semua kategori) - Visi Komputer dan Pengenalan Pola
Articles 143 Documents
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Beyond Binary Fraud Detection: Amount-Aware Operational Ranking for Transaction Risk Prioritization Hartatik Hartatik
Journal of Computing Theories and Applications Vol. 4 No. 1 (2026): JCTA 4(1) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.16260

Abstract

Fraud detection in digital financial transactions is traditionally formulated as a binary classification problem, although real-world fraud investigation requires analysts to prioritize a limited number of suspicious transactions according to operational risk and potential financial impact. This study reformulates fraud detection as an amount-aware operational ranking problem for fraud-risk prioritization. Transactions are organized into time-window query groups, and fraudulent transactions are assigned graded relevance based on training-only transaction-amount quartiles, enabling the ranking objective to distinguish low- and high-severity fraud without relying on proprietary cost matrices. The proposed formulation is implemented using a representative Learning-to-Rank framework based on LambdaMART, while an out-of-fold XGBoost risk score is incorporated as an auxiliary feature to refine the ranking representation rather than serve as the primary contribution. Experiments conducted on a public credit-card fraud dataset using chronological validation and future-holdout testing demonstrate that amount-aware relevance consistently improves severity-aware top-rank ordering compared with conventional binary relevance. The proposed HybridLTR_amount model significantly outperforms XGBClassifier and PureLTR_binary in terms of all-query NDCG@10, whereas its performance is not statistically different from PureLTR_amount, indicating that the primary empirical improvement is attributable to the amount-aware ranking formulation rather than the auxiliary hybrid component. Additional operational analyses show that high-risk transactions and fraudulent financial losses are concentrated within a compact top-ranked segment, while budget-oriented evaluation demonstrates the practical value of the proposed formulation under limited analyst review capacity. These findings establish amount-aware operational ranking as an effective formulation-centric framework for operational fraud-risk prioritization rather than as a new classification algorithm.
Secure Lightweight Face Authentication with MTCNN and LightCNN for Digital Financial Services Dendy K. Pramudito; Jufriadif Na'am; Ferda Ernawan
Journal of Computing Theories and Applications Vol. 4 No. 1 (2026): JCTA 4(1) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.16276

Abstract

Mobile face authentication for digital financial services must simultaneously satisfy recognition accuracy, computational efficiency, and biometric security under resource-constrained deployment conditions. This study proposes and evaluates a lightweight face-authentication framework that integrates detector–recognition pipeline optimization, protected biometric-template transformation, and blockchain-backed integrity support. Five face detection–recognition pipelines were systematically evaluated using a shared LightCNN-29v2 backbone fine-tuned on the Indonesian Muslim Student Face Dataset (IMSFD), with Mahalanobis-based Distance-Based Encryption (DBE) providing protected template matching and blockchain hash anchoring serving as an architectural integrity layer. Experiments on 3,660 images from 68 identities demonstrate that the MTCNN + LightCNN pipeline achieves the most favorable in-domain performance, reaching 94.95% accuracy, a ROC-AUC of 0.9970, an F1-score of 0.95, and successful processing of 3,546 out of 3,660 test images with an overall model size of approximately 5 MB. Applying Mahalanobis-based DBE further improves verification performance on IMSFD, increasing accuracy to 96.88% while reducing the False Acceptance Rate (FAR) from 0.83% to 0.18% and the False Rejection Rate (FRR) from 16.44% to 10.12%. Cross-dataset evaluation on LFW, CFP-FF, CFP-FP, AgeDB-30, CALFW, and CPLFW indicates that the proposed framework generalizes well to frontal-domain benchmarks but exhibits expected performance degradation under cross-pose and cross-age conditions due to distribution shift. Overall, the results demonstrate that detector selection is the dominant factor influencing end-to-end verification performance, while domain-specific fine-tuning and protected template matching are essential for secure and practical deployment in mobile financial authentication systems.
Block-wise Authenticated DNA-based Image Encryption with Tamper Localization using HKDF-Derived Keys Bagus Satrio Waluyo Poetro; Kusworo Adi; Aris Puji Widodo
Journal of Computing Theories and Applications Vol. 4 No. 1 (2026): JCTA 4(1) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.16605

Abstract

Confidentiality alone cannot establish whether a medical, forensic, cloud-stored, or remotely sensed image has been modified during transmission, while a single global authentication verdict cannot identify the affected region. This paper presents a block-wise authenticated DNA-based image encryption framework that integrates a DNA-chaos confidentiality core with pre-decryption integrity verification and spatial tamper localization. An authenticated ephemeral X25519 key exchange establishes a session secret, which is expanded by HKDF-SHA256 into four transcript-bound, domain-separated subkeys for permutation, DNA operations, diffusion, and authentication. Chaotic seeds are derived from a canonical plaintext hash and block coordinates, preserving strong plaintext differential sensitivity while confining post-encryption modifications to the affected blocks. Ciphertext integrity is enforced using a block-wise encrypt-then-MAC construction with 128-bit truncated HMAC-SHA256 tags that authenticate both the canonical header and each ciphertext block. A reduction-based security argument shows that the authentication layer provides ciphertext integrity and conditionally upgrades an IND-CPA encryption core to IND-CCA security under standard HKDF and HMAC assumptions, while the confidentiality claim remains explicitly conditional on the encryption core. Experiments on 30 tuberculosis chest radiographs across five independent sessions achieved a ciphertext entropy of 7.9972, near-zero adjacent-pixel correlation, 99.61% NPCR, and 33.47% UACI. Across five representative tampering attacks, the proposed framework achieved an observed 100% block-level true-positive rate, 0% false-positive rate, and required only 2.847 ± 0.258 ms for block-wise authentication with 6.25% tag overhead using the default 16×16 block configuration. These results demonstrate that the proposed framework effectively combines statistical confidentiality, modern cryptographic key management, and reliable block-level tamper localization within a unified authenticated image encryption architecture.
A Side-Channel-Aware Cryptographic Framework for Secure Interactive, Embedded, IoT, and Edge Communication Systems Walid W. Souror; Mohamed Fouad; Fahmi Khalif; Ali E. Takieldeen
Journal of Computing Theories and Applications Vol. 4 No. 1 (2026): JCTA 4(1) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.16745

Abstract

Secure interactive, embedded, and edge communication systems are often deployed in physically exposed environments where algorithmically secure ciphers may exhibit implementation-specific leakage. This paper presents a simulation-based evaluation of a configurable hybrid AES-Blowfish framework comprising AES-Hybridization, Combined Blowfish Trilogy, and cascaded Blowfish-to-AES modes. The AES-Hybridization path combines AES-256-CBC, Argon2id-derived whitening material, HMAC-based integrity binding, plaintext masking, and modeled randomized hiding activity. The hiding activity is represented solely within the leakage simulation and does not modify the plaintext or ciphertext. The Blowfish path employs session-dependent P-array randomization, dynamic S-box initialization, and a three-stage Feistel-like structure. The framework is evaluated using representative IoT/edge workload proxies, component-level ablation studies, modeled leakage assessment, non-ideal leakage scenarios, parameter-sensitivity analysis, and a software-level performance model. Compared with the simulated baseline configurations, the proposed modes reduce modeled TVLA, CPA, and DPA distinguishability, with AES-Hybridization providing the most balanced security-performance trade-off and the cascaded mode achieving the lowest modeled distinguishability at the highest computational cost. All findings are derived exclusively from simulation; no validation using physical power traces, electromagnetic traces, FPGA implementations, or microcontroller platforms is claimed.
Bridging the AI Deployment Gap in Predictive Policing: An Analytical Review of Hybrid Multimodal Methods in Developing-Country Contexts Mukhtar O. Omiyeniyi; Erastus O. Ogunti; Osekhonmen V. Abhulimen
Journal of Computing Theories and Applications Vol. 4 No. 1 (2026): JCTA 4(1) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15962

Abstract

The rapid growth of crime in Nigeria and other developing countries has intensified the need for proactive, data-driven policing strategies. Predictive policing leverages historical crime data and artificial intelligence (AI) techniques to support crime forecasting, patrol planning, and resource allocation. However, most state-of-the-art predictive policing frameworks have been developed and evaluated in data-rich environments, whereas Nigeria and many developing countries continue to face fragmented crime records, partial digitization, narrative-heavy police data, limited digital infrastructure, and evolving AI governance. This paper presents an analytical review of AI-based predictive policing, focusing on Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), spatial-temporal modeling, graph-based prediction, Explainable Artificial Intelligence (XAI), and ethical AI deployment. The review introduces a taxonomy that organizes the literature according to problem class, model architecture, data modality, interpretability, and deployment requirements. The findings indicate that conventional ML remains effective for structured crime prediction, whereas DL and graph-based approaches provide superior capabilities for temporal forecasting and spatial crime-diffusion analysis. Despite its potential to exploit intelligence embedded in police narratives, witness statements, case files, social media, and other unstructured sources, NLP remains underutilized in operational predictive policing systems. Similarly, XAI is insufficiently integrated into current frameworks, limiting transparency, auditability, bias detection, and public trust. The review further identifies an AI Deployment Gap between developed and developing-country contexts, demonstrating that successful deployment depends not only on predictive accuracy but also on data readiness, local validation, explainability, governance, and institutional capacity. Overall, the review highlights the need for context-aware hybrid ML–NLP–spatial-temporal–XAI frameworks that are computationally efficient, ethically governed, and better suited to the operational realities of predictive policing in Nigeria and other developing countries.
Multi-Descriptor Fusion with Deep Residual Learning for Kinshipship Identification from Facial Images Munzza Bibi; Wakeel Ahmad; Syed M. Adnan; Asifa Bibi
Journal of Computing Theories and Applications Vol. 4 No. 1 (2026): JCTA 4(1) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.16066

Abstract

Kinship identification from facial images aims to determine biological relationships between individuals based on shared facial characteristics. However, subtle kinship-related facial cues are often obscured by variations in illumination, pose, age, and facial expressions, making reliable kinship classification challenging. To address this problem, this study proposes a hybrid framework that integrates handcrafted texture descriptors with deep features for multiclass kinship identification. A preprocessing pipeline consisting of image resizing to 224 × 224 pixels, noise reduction, intensity normalization, and facial region extraction is first applied to improve image consistency and feature quality. Three complementary local texture descriptors, namely Local Binary Pattern (LBP), Local Ternary Pattern (LTP), and Local Directional Pattern (LDP), are then employed to capture fine-grained facial texture and directional information. These handcrafted representations are fused with 2048-dimensional deep features extracted from a ResNet-50 model. The resulting 4562-dimensional pair representation is classified using a Support Vector Machine (SVM) under a four-class setting comprising father–son, father–daughter, mother–son, and mother–daughter relationships. Experiments on the KinFaceW-I dataset demonstrate that the proposed hybrid framework achieves a mean accuracy of 82.97%, with mean precision, recall, and F1-score of 82.99%, 83.00%, and 82.98%, respectively. The results further show consistent performance across all four relationship categories and competitive performance against existing methods, demonstrating the effectiveness of combining complementary local texture descriptors with deep semantic representations.
An Enhanced UNet++ with InceptionNeXt Blocks and Feature-Scale Channel Attention for Ischemic Stroke Lesion Segmentation Muhammad Hilmy Naufal; Wiharto Wiharto; Herdito Ibnu Dewangkoro
Journal of Computing Theories and Applications Vol. 4 No. 1 (2026): JCTA 4(1) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.16410

Abstract

Ischemic stroke lesion segmentation from Magnetic Resonance Imaging (MRI) remains a challenging task due to the small lesion size, irregular morphology, low contrast with surrounding tissue, and severe class imbalance. To address these challenges, this study proposes an enhanced UNet++ architecture that integrates Modified InceptionNeXt Blocks and Feature-Scale Channel Attention (FSCA) to improve multi-scale feature extraction and adaptive feature fusion. Hyperparameter tuning was performed by optimizing the number of initial filters, network depth, and loss function, followed by an ablation study to evaluate the contribution of each architectural component. The proposed model was implemented using the TensorFlow framework and evaluated on the ISLES 2022 dataset. To enhance lesion visibility, Diffusion-Weighted Imaging (DWI) was processed using Contrast Limited Adaptive Histogram Equalization (CLAHE) to generate enhanced DWI (eDWI). Three input configurations, namely DWI, DWI+ADC, and DWI+ADC+eDWI, were investigated through channel concatenation. Performance was assessed using Dice Score, Intersection over Union (IoU), Precision, and Recall. Experimental results demonstrate that the proposed UNet++ model with Modified InceptionNeXt Blocks and FSCA achieves the best performance using the DWI+ADC+eDWI configuration, obtaining a Dice Score of 0.8742, IoU of 0.8464, Precision of 0.9440, and Recall of 0.8906. Furthermore, the segmentation results exhibit high agreement with the ground truth, indicating that the proposed architecture effectively improves ischemic stroke lesion segmentation while maintaining computational efficiency.
Frozen DINOv2–CLIP Fusion and Binary Hashing for Remote Sensing Image Retrieval: A Controlled Evaluation Jumi J; Tedjo Mulyono; Achmad Zaenuddin; Suwardi Suwardi
Journal of Computing Theories and Applications Vol. 4 No. 1 (2026): JCTA 4(1) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.16661

Abstract

This study evaluates a controlled content-based remote sensing image retrieval pipeline that combines frozen vision foundation models with binary hashing. Across five stratified seeds on PatternNet and NWPU-RESISC45, calibrated DINOv2–CLIP score fusion achieved mAP values of 0.8143 ± 0.0007 and 0.5693 ± 0.0020, respectively, improving over the stronger single backbone by 0.0582 and 0.0514. The two similarity spaces were related but not redundant, with Spearman correlations of ρ = 0.510 and 0.515 across 200,000 sampled pairs. The contribution of texture features was dataset- and backbone-dependent: DINOv2–texture received a weight of 0.1 on PatternNet, whereas CLIP–texture and three-stream fusion assigned zero weight to texture; forcing texture into a 32-bit representation reduced mAP by 0.0147 and 0.0510. Among the unsupervised hashing methods, 64-bit iterative quantization (ITQ) exceeded the full-precision fused baseline by 0.0533 and 0.0445 mAP on PatternNet and NWPU-RESISC45, respectively. The corresponding paired parametric tests were significant, although the exact five-pair sign-flip test remained resolution-limited (p = 0.0625). A supervised CSQ-style head achieved mAP values of 0.9886 and 0.9087 at 64 bits; however, this improvement cannot be attributed to compression alone because gallery labels were used during training. In a controlled 200,000-vector benchmark constructed through deterministic repetition, 64-bit flat Hamming search required 1.6 MB and 0.674 ms/query, compared with 1.02 GB and 5.343 ms/query for 1,280-dimensional float vectors. Overall, the results support DINOv2–CLIP fusion and 64–128-bit ITQ as practical label-free choices, while restricting the scalability claim to the tested flat-search setting.
Exploration Is a State, Not a Setting: A Markov-Switching Reinforcement-Learning Model of Strategy Transitions in Sequential Choice Fathimah Al-Ma'shumah; Feneta Fidi Kirani; Nita Ratnawaty
Journal of Computing Theories and Applications Vol. 4 No. 1 (2026): JCTA 4(1) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.16818

Abstract

Computational accounts of human exploration usually assume a stationary policy, in which a single set of parameters for value sensitivity and uncertainty seeking generates every choice in a task, so that all within-task variation is treated as decision noise. Neuroscience instead treats exploration and exploitation as dissociable modes between which the brain switches, which a stationary model cannot represent. We introduce the Markov-Switching Reinforcement-Learning (MS-RL) model, in which a first-order hidden Markov chain governs transitions among a small number of latent decision regimes, each with its own softmax policy over a shared value-learning process. The three regimes are exploitation, directed exploration, and random exploration. The model contains the standard stationary account (one regime) and a temporally unstructured mixture (memoryless transitions) as nested special cases, making the stationarity assumption testable. We estimate the model using Expectation-Maximization with Viterbi decoding and select the number of regimes using the Bayesian information criterion. A parameter- and state-recovery study confirmed that the generating parameters and latent regime paths are recoverable (parameter correlations 0.88–0.94; state accuracy 86 percent; Cohen’s kappa ≈ 0.79). Applied to an openly available two-armed bandit dataset (46 adults, 13,800 choices), a three-regime MS-RL model was preferred over the nested baselines, two- and four-regime variants, and a single-regime model with smoothly time-varying value sensitivity. An ablation analysis attributes the largest gains to the latent regimes and temporal switching. The decoded regime path revealed a systematic within-game shift from directed exploration toward exploitation. The estimated transition matrix provides a per-participant measure of strategy change that has no counterpart in stationary models. We conclude that exploration is better described as a dynamic state than as a fixed trait, and that modeling it as a switching process is both more accurate and useful.
Transformer-Based Support for Content-Validity Pre-Screening in Educational Materials Safuan Safuan; Dhendra Marutho; Ahmad Ilham; Muhammad Munsarif; Wendy Sarasjati; Edy Winarno; Arnold Adimabua Ojugo; De Rosal Ignatius Moses Setiadi
Journal of Computing Theories and Applications Vol. 4 No. 1 (2026): JCTA 4(1) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.16829

Abstract

Content validity assessment is essential for determining whether educational materials adequately represent intended learning outcomes. However, conventional assessment procedures require substantial expert time and may produce inconsistent decisions across large item collections. This study develops a transformer-based framework to support content-validity pre-screening through two complementary tasks: predicting expert-derived Aiken’s V coefficients and classifying instructional-item essentiality. The final dataset comprised 652 Indonesian-language educational text items independently evaluated by four subject-matter experts. To reduce information leakage, identical and normalized-equivalent texts were grouped before applying a group-aware 70:15:15 training–validation–test split. Classical TF-IDF-based baselines were compared with IndoBERT, multilingual BERT, XLM-RoBERTa, and multilingual DeBERTa-v3. For Aiken’s V regression, multilingual BERT achieved the lowest MAE of 0.0501, the lowest RMSE of 0.0625, and the highest R² of 0.5239, whereas multilingual DeBERTa-v3 achieved the highest Spearman correlation of 0.7532. For essentiality classification, XLM-RoBERTa achieved the highest accuracy of 0.8557 and Macro-F1 of 0.8161, whereas multilingual BERT achieved the highest balanced accuracy of 0.8135 and ROC-AUC of 0.9111. Error analysis showed that the models captured textual patterns associated with expert-derived outcomes but remained limited when judgments depended on broader curricular context, competency hierarchies, prerequisite relationships, or relationships among instructional items. The findings support the use of transformer models as human-in-the-loop decision-support tools for prioritizing uncertain or potentially problematic educational items. However, the framework should be interpreted as a pre-screening mechanism rather than a replacement for expert judgment, and external validation across institutions and disciplines remains necessary.

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