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Andi Baso Kaswar
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Journal of Deep Learning, Computer Vision and Digital Image Processing
Published by Sakura Digital Nusantara
ISSN : 29868920     EISSN : 29868939     DOI : https://doi.org/10.61255/decoding
Core Subject : Science,
Computer Science & IT
The Journal of Deep Learning, Computer Vision and Digital Image Processing (DECODING), covers all topics of artificial intelligence and soft computing and their applications, including but not limited to: • Neural networks • Reasoning and evolution • Intelligent search • Intelligent planning • Intelligence applications • Computer vision and speech understanding • Multimedia and cognitive informatics • Data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning • Technology and computing (like particle swarm optimization); intelligent system architectures • Knowledge representation • Bioinformatics • Natural language processing • Automated reasoning • Logic programming • Machine learning • Visual/linguistic perception • Evolutionary and swarm algorithms • Derivative-free optimisation algorithms • Fuzzy sets and logic • Rough sets • Simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc) • Multi-agent systems • Data and web mining • Emotional intelligence • Hybridisation of intelligent models/algorithms • Parallel and distributed realisation ofintelligent algorithms/systems • Application in pattern recognition, image understanding, control, robotics and bioinformatics • Application in system design, system identification, prediction, scheduling and game playing • Application in VLSI algorithms and mobile communication/computing systems
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 3 Documents
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Search results for , issue "article in press" : 3 Documents clear
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
Journal of Deep Learning, Computer Vision, and Digital Image Processing ARTICLE IN PRESS
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i3.1423

Abstract

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 Aria Sastra Wisesa; Jajang Kusnendar; Muhammad Rafi Valliansyah; Lala Septem Riza
Journal of Deep Learning, Computer Vision, and Digital Image Processing ARTICLE IN PRESS
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i3.1633

Abstract

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
Journal of Deep Learning, Computer Vision, and Digital Image Processing ARTICLE IN PRESS
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i3.1771

Abstract

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

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