Journal of Deep Learning, Computer Vision and Digital Image Processing

The AI–Gamification Integrated HR Control (AGIHC) Model: A Conceptual Framework for Employee Selection and Placement in Indonesia

Sony Putra, Wilda Fasim, Basri, Etty Sri Wahyuni — Sun, 23 Aug 2026 00:00:00 +0000

Purpose – Selection and placement in many organizations remain subjective and inefficient, while gamification’s engagement potential is rarely applied to hiring. This study explains why an integrated approach is needed and proposes the AI–Gamification Integrated HR Control (AGIHC) Model, which couples AI as an analytic engine with gamification as an engagement interface to optimize the HR control system in an emerging-market (Indonesian) context.
Methods – A Design Science Research approach was combined with a structured narrative synthesis of Scopus- and Sinta-indexed literature (2020–2026). The model was developed as a designed artifact, with a mixed-methods validation roadmap specified for the next phase.
Findings – Based on the synthesized literature rather than on primary measurement in this study, individual primary studies report reductions in time-to-hire and cost-per-hire on the order of one-third; this figure is taken from those cited studies and is not an average computed in the present synthesis. Only 7.3% of gamification studies address recruitment versus 85.4% targeting existing employees. The resulting AGIHC Model specifies three layers: an Input Layer (AI screening plus gamified assessment), a Process Layer (AI matching plus gamified onboarding), and an Output Layer (AI analytics plus a gamified dashboard).
Research implications – As a conceptual contribution, the propositions await empirical testing, and the single-country Indonesian framing bounds generalizability. Deployment also depends on data governance, candidate consent, and the explainability of AI scoring.
Originality – The study bridges two previously parallel literatures in an integrated AI–gamification HR control framework and provides testable propositions for engagement-driven, fairness-aware selection and placement.

Design of an Apperception Strategy to Activate Students’ Prior Knowledge Using Visual Block Programming

Sun, 23 Aug 2026 00:00:00 +0000

Purpose – This study aims to implement an Activating Prior Knowledge (APK) strategy supported by the OOPify visual block programming tool in Object-Oriented Programming (OOP) learning and to examine students' learning outcomes and learning responses following its implementation.
Methods – The study employed the Research and Development (R&D) method, consisting of preliminary study, product development, implementation, and evaluation. Data were collected through observations, interviews, pretest–posttest assessments, and questionnaires. The data were analyzed using the Shapiro–Wilk normality test, the Wilcoxon Signed-Rank Test, Normalized Gain (N-Gain) analysis, and descriptive statistics.
Findings – The Wilcoxon Signed-Rank Test showed a statistically significant difference between the pretest and posttest scores (Z = −3.346, p = 0.001). The N-Gain analysis indicated that students with low initial proficiency achieved the highest average N-Gain score of 0.493 (moderate category). In addition, the usability evaluation showed that OOPify obtained an overall usability score of 76.23%, indicating good usability and positive student perceptions.
Research implications – These findings provide preliminary evidence that integrating an Activating Prior Knowledge strategy with the OOPify visual block programming platform may support students' conceptual understanding and learning experiences. Because this study employed a one-group pretest–posttest design, the observed differences should not be interpreted as definitive causal effects.
Originality – This study integrates the Activating Prior Knowledge strategy through the Know–Want to Know–Learned (KWL) approach and brainstorming activities with the OOPify visual block programming tool to support more meaningful learning of Object-Oriented Programming concepts

Integrating Immersive Learning and Interactive Media in Teaching Islamic Cultural History: A Qualitative Case Study

Novia Ramadhani, Wahyudin Nur Nasution — Sun, 23 Aug 2026 00:00:00 +0000

Purpose – This exploratory qualitative case study describes the initial implementation of technology-based learning media (Wordwall, video, and PowerPoint) within Islamic Cultural History (ICH) learning at MTsN 2 Deli Serdang.
Methods – The study involved five informants: one (ICH) teacher, a principal as a contextual informant, and three eighth-grade students. Data were collected naturally through eight classroom observation sessions, documentation studies, and five in-depth interviews. To comply with ethical standards for protecting minors, all student identities were anonymized, and visual documentation was digitally blurred.
Findings – Based on observations and interviews, integrating interactive media reduced student passivity common in conventional lecture methods. Visual quizzes and videos helped direct student focus and facilitated historical recollection. However, given the small qualitative sample size, improvements in students' motivation, critical thinking, and social skills remained situational and did not reach full theoretical saturation. Practical implementation was also constrained by limited lesson time, infocus shortages, and the challenges of large crowd management due to noise during gamified quizzes.
Research implications – This study offers context-specific qualitative insights into how interactive tools stimulate classroom engagement in madrasah settings.
Originality – The findings reject macro-level generalizations or broad TPACK and self-determination claims, illustrating instead the practical challenges

Digital Forensic Investigation of Linux SSH Authentication Logs Using NIST SP 800-86 with GeoIP and ASN Enrichment

Danur Wijayanto, Ahmad Widad Saksana — Thu, 10 Sep 2026 00:00:00 +0000

Purpose – This study applies the National Institute of Standards and Technology Special Publication (NIST SP 800-86) framework to investigate SSH authentication logs from the Lensa UNISA Server while preserving evidence integrity and enriching forensic findings using GeoIP and Autonomous System Number (ASN) information.
Methods – The investigation followed the Collection, Examination, Analysis, and Reporting phases of NIST SP 800-86. The auth.log.1 file was acquired using Secure Copy Protocol (SCP), verified through SHA-256 hashing, and analyzed using standard Linux utilities. GeoIP and ASN information was obtained using MaxMind GeoLite2 and RDAP.
Findings – SHA-256 verification confirmed identical hashes between the original evidence and working copy. The analysis identified 96,182 OpenSSH-related events, including 15,522 failed authentication attempts, 4,604 invalid-user messages, and eight successful sessions. Systematic username enumeration targeting institutional account names was observed. GeoIP and ASN enrichment showed that substantial authentication activity originated from source IP addresses in the Netherlands and Turkey, particularly AS43350, AS48090, and AS154383, consistent with activity associated with hosting infrastructure.
Research Implications – The findings demonstrate the practical applicability of NIST SP 800-86 for SSH log investigation using standard Linux tools. GeoIP and ASN enrichment provide additional context for interpreting authentication artifacts.
Originality – This study presents an operational forensic case study integrating NIST SP 800-86, Linux-based log analysis, and GeoIP–ASN enrichment

Comparative Analysis of XGBoost and CatBoost for Detecting AI-Generated Synthetic Faces Using Texture and Color Features

indra firmansyah, Defry Hamdhana , Wahyu Fuadi — Thu, 10 Sep 2026 00:00:00 +0000

Purpose – The rapid advancement of Generative AI produces near-perfect synthetic face images, posing fraud risks. This study compares XGBoost and CatBoost for detecting real versus synthetic faces using texture and color features.
Methods – Texture features were extracted using GLCM with four orientations (0°, 45°, 90°, 135°), generating 16 features, while YCbCr mean and standard deviation provided 6 color features (22 total). A balanced 750-image dataset (real and AI from GPT Image 2, Nano Banana, Leonardo AI, Canva, Dreamina AI) was split 80:20 and normalized with StandardScaler.
Findings – On an asymmetric, source-shifted test split, where real test images were captured independently while synthetic test images came from the same generators as training, CatBoost outperformed XGBoost, achieving 84.67% accuracy (vs. 80.67%), 83.33% precision (vs. 78.05%), 86.67% recall (vs. 85.33%), and 84.97% F1-score (vs. 81.53%), reducing False Positives by 27.78% and False Negatives by 9.09%. However, repeated 5-fold stratified cross-validation (5×10 repeats) revealed only marginal, non-significant differences (0.05-0.87 percentage points, p > 0.09).
Research implications – Although the moderate dataset excludes recent architectures such as StyleGAN, findings affirm the feasibility of efficient, interpretable detection through feature engineering. Since within-domain cross-validation shows no significant algorithmic difference, the observed cross-domain gap should be regarded as preliminary, pending validation through repeated cross-domain evaluations across diverse AI platforms.
Originality – The novelty lies in comparing XGBoost and CatBoost on multi-orientation GLCM and YCbCr feature fusion for contemporary AI-generated images, identifying cr_std and contrast_0 as stable, interpretable discriminative markers.

Enhancing Enhancing High School Learning Management Systems Through Explainable AI-Based Behavioral Analytics for Personalized Learning Support

Yaya Sudarya Triana, Nurhasanah, Hadi Santoso — Thu, 10 Sep 2026 00:00:00 +0000

Purpose – This study develops an Explainable AI (XAI)-enhanced Learning Analytics framework for analyzing student learning behaviors in a senior high school Learning Management System (LMS) and supporting retrospective performance-risk identification using score-independent behavioral features.
Methods – The study analyzed 57,095 LMS quiz activity records from 111 students collected between July 2024 and October 2025. Seven behavioral variables were initially engineered, while Answer Accuracy, Learning Consistency, and Learning Efficiency were excluded from predictive modeling because they are derived from score-related information. The final models used Quiz Frequency, Learning Duration, Activity Level, and Dominant Study-Time Pattern. Random Forest, Decision Tree, and Logistic Regression were evaluated, with SHAP TreeExplainer used for interpretation.
Findings – Decision Tree achieved the highest accuracy (73.9%), macro F1-score (0.638), and macro ROC-AUC (0.771) on the 23-student test set. Random Forest achieved 69.6% accuracy, 0.631 macro F1-score, and 0.669 macro ROC-AUC, while Logistic Regression achieved 56.5%, 0.455, and 0.656, respectively. Learning Duration had the highest mean absolute SHAP value.
Research Implications – The results indicate that score-independent behavioral features provide a limited-to-moderate signal for retrospective performance classification. Prospective early-warning utility requires temporal validation.
Originality – The study integrates XAI with behavioral Learning Analytics while explicitly separating score-independent predictors from the score-derived target to support interpretable educational decision-making

A Single-Node Performance Evaluation of Hash-Chain and Dependency-Graph Scheduling for Simulated Smart-Contract Workloads in Organic Certification

Yuni Dwi Anggraeni, Arum Prasetyajati, Akhmad Unggul Priantoro — Thu, 10 Sep 2026 00:00:00 +0000

Purpose – This study evaluates when dependency-aware parallel scheduling improves a single-node simulated smart-contract workload for organic certification while separating scheduling effects from hash-chain and dependency-graph record representation.
Methods – A deterministic Python rule engine evaluates 18 certification tasks per submission. The same transactions were executed under four conditions: hash-chain sequential (HS), hash-chain dependency-aware parallel (HP), dependency-graph sequential (DS), and dependency-graph dependency-aware parallel (DP). Main experiments varied 18–900 transactions and 1–3,000 iterative SHA-256 rounds, with five warm-ups and 30 paired technical repetitions. Worker, graph-shape, conflict, functional, integrity, and focused reconciliation tests were also performed.
Findings – Representation-only ratios remained close to unity, whereas scheduling produced the dominant performance effect. At 3,000 rounds, HP/HS and DP/DS speedups were 1.680× and 1.620× at 450 transactions and 1.564× and 1.554× at 900 transactions. The focused reconciliation run showed that eight workers minimized makespan for both 180 and 900 transactions, although per-worker efficiency declined as worker count increased. All 12 functional and integrity tests passed.
Research Implications – Dependency information is useful primarily as a scheduling mechanism; the observed speedup should not be interpreted as evidence that a distributed DAG ledger is intrinsically faster than a hash chain.
Originality – The study provides a carefully documented 2 × 2 single-node benchmark that isolates record representation from execution policy for a simulated certification smart-contract workload.

Development of OOPify as a Visual Block Programming Environment for OOP Learning and Group-Level Practical Performance

Naufal Oktavian, Yogi Prasetyo, Muhammad Rafi Valliansyah, Lala Septem Riza — Thu, 10 Sep 2026 00:00:00 +0000

Purpose – Object-Oriented Programming (OOP) is challenging for students because its abstract concepts are difficult to translate into practical programming solutions. This study developed OOPify, a visual block-based learning environment for OOP practical activities, and examined changes in students’ conceptual understanding and group-level practical performance.
Methods – The study employed a Research and Development approach using the ADDIE model and a one-group pretest–posttest design. Participants were 34 vocational high school students in West Java, Indonesia. Data were collected through OOP conceptual understanding tests, group-level practical performance observations, expert validation, and student response questionnaires.
Findings – OOPify was rated feasible (82.5%) by one expert. The Wilcoxon Signed-Rank Test indicated a significant improvement in OOP conceptual understanding (p < .001), with 70.59% of students achieving moderate-to-high N-Gain and an average N-Gain of 0.50. Group-level practical performance, covering psychomotor, cognitive, and collaborative indicators, averaged 85.22% across Meetings 2 and 3. However, these results represent preliminary observational evidence because formal inter-rater reliability was not assessed.
Research implications – Visual block-based learning environments may support OOP learning by reducing syntactic complexity and allowing students to focus on concepts and problem-solving. Generalizability is limited by the single-school context and absence of a control group.
Originality – This study integrates visual block programming, OOP practical activities, and group-level practical performance within a single learning environment.