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

2026-08-05T10:17:02+00:00

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

2026-07-28T04:01:08+00:00

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

2026-08-06T19:17:28+00:00

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

2026-08-08T11:02:06+00:00

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

2026-07-28T03:44:20+00:00

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

2026-08-05T08:29:06+00:00

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

2026-08-10T04:05:28+00:00

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

2026-07-28T03:52:30+00:00

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.

Mapping COBIT 2019 Capability to ISO/IEC 27002:2022 Controls for Managed Security Services in Higher Education Cybersecurity Governance

2026-09-28T18:27:38+00:00

Purpose – To assess three prioritized COBIT 2019 objectives at an Indonesian private university and map DSS05 (Managed Security Services) findings to ISO/IEC 27002:2022 controls.
Methods – An embedded case study at Universitas Indo Global Mandiri combined documents, observation, interviews, and a binary checklist completed by five RACI-selected role holders. Capability and evidence completeness were assessed for APO04, APO07, and DSS05; control mapping covered DSS05 only.
Findings – All three objectives achieved 100% at level 1. Level-2 scores were 79.98%, 83.81%, and 82.00%, respectively, below the Fully Achieved boundary of more than 85%. Each therefore remained at level 1 under the study’s scoring rule, against a target of level 4. Evidence completeness was 75.00%, 88.89%, and 92.86%. Missing evidence included innovation-decision records, competency review, resource-utilization records, and physical-access logging.
Research Implications – The single institution and five respondents limit generalizability. Documentary evidence and self-reported capability do not establish technical control effectiveness or improved security outcomes.
Originality – The DSS05 crosswalk links reported capability, institutional evidence, ISO/IEC 27002:2022 controls, and proposed corrective actions within a bounded case.

Evaluating Precision and Semantic Similarity in a Retrieval-Augmented Generation-Based Question Answering System for Shafi'i Prayer Fiqh

2026-09-12T12:21:19+00:00

Purpose – This study evaluates retrieval precision and answer similarity in a closed-domain question-answering system for Shafi'i prayer fiqh using retrievalaugmented generation (RAG).
Methods – Indonesian translations of three classical fiqh texts were segmented into 251 chunks using a 1000-character chunk size and 200-character overlap. LangChain coordinated multilingual-e5-base embeddings, ChromaDB retrieval, and local Qwen2.5:3b generation. The retriever returned up to four eligible chunks. Twenty predefined queries were evaluated using Precision@4, cosine similarity to researcher-prepared reference answers, and qualitative assessment of required answer components.
Findings – Mean Precision@4 was 70.00%, and mean cosine similarity was 0.9197 (91.97%). Retrieval varied across queries, with Query 19 returning only one relevant chunk. Queries 8 and 19 produced incomplete answers despite high semantic similarity. The system returned a predefined fallback response when no retrieved passages passed the distance threshold.
Research Implications – The results support preliminary prototype feasibility but do not establish expert-verified correctness of the rulings. The small query set and absence of a quantitative non-RAG baseline limit generalization and prevent estimation of any reduction in hallucination.
Originality – The study evaluates retrieval and generation separately within a constrained Indonesian fiqh corpus and illustrates the distinction between semantic similarity and answer completeness.

Security Vulnerability Analysis of an Employee Attendance Website Using Black-Box Penetration Testing and OWASP Top 10 2025

2026-09-09T10:04:05+00:00

Purpose – This study evaluates the security posture of the PT. Jaka Kuasa Nusantara employee-attendance website, which supports account authentication, attendance recording, and face-capture functionality.
Methods – An authorized black-box vulnerability assessment and penetration-testing approach was conducted through information gathering, network and service reconnaissance, Nessus vulnerability scanning, and targeted manual verification. The observations were interpreted using the OWASP Top 10 2025 framework, the CIA Triad, and CVSS-oriented risk considerations.
Findings – Nessus identified HTTP configuration issues, cookies without HttpOnly and Secure attributes, an improper or missing Content-Type configuration, and an Apache 2.4.29 version requiring patch-status verification. Manual testing indicated that SQL injection was not successful in the tested login scenario, CAPTCHA and login-attempt limiting were present, and no SSRF weakness was observed in the tested scope.
Research Implications – The assessment was limited to authorized, externally observable behavior and did not include destructive exploitation, source-code review, or full internal-infrastructure testing.
Originality – The study integrates automated vulnerability discovery with scenario-based manual validation and translates the results into a prioritized mitigation roadmap for an operational attendance application. The recommended actions include HTTPS and HSTS enforcement, security-header hardening, secure-cookie configuration, component patching, stronger authentication, centralized logging, and periodic retesting.

Deep Learning Pedagogy and Students’ Critical Thinking in Indonesian Education: A Systematic Review

2026-08-01T11:37:22+00:00

Purpose – To synthesize evidence concerning deep learning pedagogy and students’ critical thinking within educational transformation in Indonesia.
Methods – A systematic literature review following PRISMA 2020 reporting guidance searched Google Scholar, Scopus, ERIC, Dimensions, and Crossref. Of 132 records identified, 21 studies were included after duplicate removal, screening, and eligibility assessment. Findings were synthesized thematically.
Findings – Recurring themes concerned meaningful and reflective learning, critical thinking and higher-order thinking, teacher readiness, and implementation across educational contexts. The corpus included diverse empirical, conceptual, and review studies. It does not establish a pooled correlation or a uniform causal effect; some studies provide contextual rather than direct evidence about deep learning pedagogy.
Research Implications – Implementation should combine teacher development, appropriate resources, and evaluation using clearly defined learning outcomes. The heterogeneity of the evidence and gaps in search and appraisal reporting limit the conclusions.
Originality – The review brings together pedagogical and implementation themes relevant to deep learning and critical thinking in Indonesian education.

Design and Evaluation of an ESP32-Based Automatic Cake Ingredient Dosing System Using Load Cell Control

2026-07-08T09:29:37+00:00

Purpose – This study designs and evaluates an ESP32-based automatic ingredient dosing system for a small bakery experiencing imprecise manual weighing.

Methods - A research and development approach produced two prototypes: an auger-based rice flour doser and a peristaltic-pump coconut milk doser. Both used load cells, HX711 modules, and single-speed on/off control with a 400 ms settling step. Manual and automatic dosing were compared over ten reported repetitions per condition using targets of 263 g flour and 492 mL coconut milk, the latter treated as 492 g under an assumed density of 1.0 g/mL.

Findings - Reported accuracy increased from 91.5% to 97.9% for flour and from 95.2% to 98.9% for coconut milk. Mean absolute errors decreased to 5.4 and 5.3 g, respectively. Automatic dosing averaged 24.9 s for flour and 19.7 s for coconut milk. All nine functional test items were completed successfully.

Research implications – The findings concern one recipe, enterprise, operator, and testing session. Manual and automatic timing covered different task boundaries, and calibration ended at 453 g, below the coconut milk target. Further calibration, equivalent timing comparisons, and longer-term testing are needed before generalizing performance.

Originality - The prototype combines solid and liquid dosing mechanisms within a shared load-cell feedback framework intended for small-scale bakery production.

Development of an IoT-Enabled Rule-Based Adaptive Traffic Signal System Using Discrete Vehicle Queue-State Detection and ESP32

2026-10-07T13:08:48+00:00

Purpose – This study aims to develop and evaluate an IoT-enabled, rule-based adaptive traffic signal prototype that responds to discrete vehicle queue states at a four-way intersection. The system is designed as a low-complexity alternative to fixed-time signal control by associating detected vehicle presence at predefined spatial points with different green-light durations.
Methods – The prototype was developed using ESP32 microcontrollers and eight infrared (IR) sensors, with two detection points assigned to each of four approaches. The physical prototype uses spatially separated detection points to represent different queue states. Sensor inputs are processed locally by the ESP32 and classified into predefined traffic states. The controller assigns green-light durations of 20, 25, or 30 s according to the detected state. The prototype was evaluated under five laboratory-scale traffic configurations using simulated vehicles to verify sensor detection, traffic-state classification, signal response, and IoT communication functionality.
Findings – The experimental evaluation verified that the controller responded according to the predefined queue-state rules. When neither detection point was active, the corresponding traffic state was classified as normal and a 20-s green duration was assigned. Activation of the first detection point resulted in a 25-s green duration, while activation of both detection points resulted in a 30-s green duration. The five predefined scenarios demonstrated that the controller could apply different timing levels according to the traffic-state distribution among the four approaches. Repeated-cycle testing resulted in 49 successful detection-and-control responses out of 50 cycles, corresponding to a 98% successful-cycle rate under the tested laboratory conditions.
Research Implications – The results demonstrate the functional feasibility of integrating discrete IR-based queue-state detection, local ESP32 processing, traffic-signal control, and wireless communication in a laboratory-scale prototype. However, the study does not establish a quantitative reduction in real-world delay, queue length, or congestion because the evaluation does not include field testing or a comprehensive fixed-time performance baseline.
Originality – The contribution of this study lies in the implementation of a low-complexity two-point queue-state detection architecture that translates spatial vehicle-presence information into rule-based green-time selection on a four-approach prototype. The architecture provides a basis for subsequent development involving repeated quantitative experiments, additional detection zones, improved traffic-state estimation, and field-based evaluation.

Design and Development of an Internet of Things-Based Automatic Chicken Egg Sorting System for Real-Time Sorting and Monitoring

2026-07-10T10:45:41+00:00

Purpose – This study develops and evaluates an IoT-based egg sorter integrating weight grading, gas-response screening, automatic diversion, and monitoring.

Methods – The prototype combined an ESP32, a load cell with HX711, an infrared sensor, an MQ-135 sensor, and a servo actuator. Records were transmitted to a PHP REST API and MySQL database on a local server. Evaluation covered ten calibration masses, ten egg-weight measurements, separate gas-screening sample sets, infrared detection, ten end-to-end routing trials, server communication, and manual-versus-automatic sorting time.

Findings – Mean relative error was 0.33% for calibration masses and approximately 0.24% for egg measurements. The MQ-135 separated the selected good and spoiled sample groups using a threshold of 250 ppm-equivalent sensor-library units. All ten end-to-end trials routed eggs according to the assigned category. Server response averaged 1.17 s (SD 0.09 s). Sorting time decreased from 7.35 to 3.18 s per egg, a reduction of 56.73%.

Research implications – The small samples support preliminary functional feasibility. Routing agreement does not establish independently verified classification accuracy. Gas response was not traceable to a calibrated concentration standard, and several environmental and sensor-preparation conditions were unrecorded. Larger controlled studies are needed to validate quality screening and boundary-case grading.

Originality – The design integrates weight sensing, gas-response screening, mechanical diversion, and local-server records within one low-cost egg-sorting workflow.

Contribution of Teacher Professionalism to Elementary School Quality in the Era of Educational Transformation: A Systematic Review

2026-08-01T11:37:44+00:00

Purpose – To examine how the literature relates teacher professionalism to elementary school quality in the context of educational transformation.
Methods – A systematic literature review following PRISMA 2020 reporting guidance searched Google Scholar, Scopus, ERIC, Dimensions, and Crossref for publications from 2015 to 2025. Of 87 records identified, 69 remained after duplicate removal, 28 underwent full-text assessment, and ten were included in the qualitative synthesis.
Findings – The synthesis identified professional development, instructional competence, leadership support, collaborative culture, digital competence, and adaptability as recurring themes. The included evidence was methodologically diverse and did not provide a pooled effect estimate or establish a causal contribution to school quality. Supplementary literature informed the discussion but was separate from the screened corpus.
Research implications – Sustained professional learning, mentoring, supportive leadership, and digital competence development may support school improvement. Conclusions remain limited by the heterogeneous evidence and incomplete reporting of quality appraisal.
Originality – The review integrates professional, organizational, and technological dimensions of teacher professionalism relevant to elementary education.