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Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Published by Universitas Pendidikan Ganesha
ISSN : 20898673     EISSN : 25484265     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Education Engineering
Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) is a collection of scientific articles in the field of Informatics / ICT Education widely and the field of Information Technology, published and managed by Jurusan Pendidikan Teknik Informatika, Fakultas Teknik dan Kejuruan, Universitas Pendidikan Ganesha. JANAPATI first published in 2012 and will be published three times a year in March, July, and December. This journal is expected to bridge the gap between understanding the latest research Informatika. In addition, this journal can be a place to communicate and enhance cooperation among researchers and practitioners.
Arjuna Subject : -
Articles 666 Documents
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AI-Driven Adaptive Learning to Enhance Digital Literacy Integrated with Mental Health Support of Teen Learners Nurul Kholisatul
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.105560

Abstract

The rapid advancement of digital technology has significantly transformed education worldwide, including in Indonesia. Artificial intelligence (AI) is increasingly used to create personalized, adaptive, and interactive learning experiences. Despite high exposure to technology, many high school and vocational students still struggle with digital literacy and mental well-being. This study proposes DigiMind, an AI-driven adaptive learning system that integrates digital literacy with mental health support to promote balanced and inclusive learning. Using a mixed-method approach, the study involved 146 students and 10 teachers from two partner schools in Surakarta. Quantitative data were collected through surveys based on the UNESCO Digital Literacy Framework and ICT Watch Indonesia, while qualitative data were obtained from focus group discussions (FGD) with teachers and students. The findings indicate that although 98.6% of students have used AI-based learning tools, many lack awareness of data privacy, digital ethics, and focus management. About half experienced digital fatigue, while most emphasized the need for well-being features such as break reminders, relaxation content, and adaptive materials. Teachers, in contrast, prioritized monitoring, analytics, and early stress detection—revealing a dual orientation between student engagement and teacher support.The resulting DigiMind blueprint combines adaptive learning modules, AI-based mental health analytics, and dual dashboards for students and teachers. Expert validation confirmed its pedagogical, technical, and psychological feasibility. Overall, integrating digital literacy and digital well-being within AI-driven adaptive learning enhances both cognitive performance and emotional resilience, offering a holistic model for sustainable digital education in Indonesia
A Hybrid Salp Swarm Optimization and Behavioral Nudge Framework for Optimizing Software Developer Task Allocation Ashabul Kahfi; Muhammad Faisal; Titin Wahyuni; Desi Anggreani; Darniati Darniati; Muhammad Syafaat S Kuba; Andi Makbul Syamsuri; Ida Mulyadi
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.106920

Abstract

Effective task allocation is critical in Agile software development, yet most optimization-based approaches treat it as a purely technical scheduling problem and disregard behavioral factors such as motivation, fairness, and engagement. This study proposes a Hybrid Salp Swarm Optimization–Behavioral Nudge Framework (HSSO–BNF) for developer–task allocation that integrates technical constraints with human-centered cues. The model formulates allocation as a multi-objective function combining workload balance, skill mismatch, deadline penalties, and a motivation score derived from three nudge components: Motivational Cue (MC), Social Comparison (SC), and Effort–Reward Feedback (ERF). These behavioral signals are embedded directly into the SSO position update and fitness evaluation, enabling the swarm to adapt simultaneously to performance and motivational states. Experiments on real developer–task records collected from GitHub compare HSSO–BNF against GA, PSO, and standard SSO using convergence behavior, allocation cost, fairness, satisfaction, and motivation dynamics. The results show that HSSO–BNF achieves faster and more stable convergence, reduces allocation cost by approximately 32% compared with GA and SSO and about 25% compared with PSO, and improves workload fairness and developer satisfaction while preserving psychologically sustainable specialization patterns. Heatmap visualizations and motivation trends further confirm that the behavioral layer produces more coherent and interpretable task assignments, indicating that behavior-aware metaheuristics are a promising direction for intelligent, human-centered task allocation in Agile teams.
Development of Digital Learning Media in Informatics Vocational Education in Facing the Industrial Revolution 4.0 (2003–2023) Faizatul Amalia; Fitra Bahtiar
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.111111

Abstract

The large number of digital learning media developed made researchers conduct this research. The objects of the study is to determine the extent of research trends on digital learning media in vocational schools, and the types of digital learning media that are most widely used. This study used the traditional literature review method. The tools used to obtain articles on the theme of digital learning media are Publish or Perish (PoP) and Scopus for the analysis using descriptive analysis. By entering the title and keywords in the PoP and Scopus applications, 44 articles were obtained and filtered. Based on the filtering process, 16 articles were mapped by specific topics: informatics vocational schools, and publications in relevant journals. The publication trend on digital learning media started in 2006, 2013, 2016, 2018, 2019, 2021, 2022, and 2023 and peaked in 2021. The types of digital learning media used in vocational schools include e-learning, e-books/flipbooks/e-modules, Augmented Reality, Android applications, and podcasts.
Multi-Class Classification and Segmentation on Kvasir Endoscopic Images Using Deep Learning Methods Rahman Ardi Saputra; Suhendro Yusuf Irianto; Egi Safitri
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.111973

Abstract

This study evaluates the use of deep learning methods for multi-class classification and polyp segmentation on Kvasir endoscopic images. A dual-model approach was employed, where EfficientNet handles multi-class classification and U-Net handles polyp segmentation, each trained and evaluated independently on their respective datasets. The EfficientNet-B0 model achieved high performance, with accuracy, precision, recall, and F1-score values exceeding 91%, demonstrating its effectiveness in detecting various gastrointestinal abnormalities across eight classes. The U-Net model, while showing strong performance in background detection, faced challenges in lesion delineation, achieving a Dice Similarity Coefficient (DSC) of 34.50% and IoU of 20.85%. These results suggest that running both models in parallel on the same input image could provide simultaneous classification and segmentation outputs, offering more comprehensive diagnostic information compared to single-task approaches. This study contributes to the understanding of independent deep learning components that could support AI-based medical decision-making in gastrointestinal endoscopy.
Real-Time Deep Learning and OCR for Medication Recognition in Visually Impaired Users: A Systematic Literature Review Qory Hidayati; Hendra Kusuma; Muhammad Attamimi
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.112489

Abstract

Medication misidentification remains a safety risk for visually impaired and low-vision users who manage medicines independently. This systematic literature review synthesizes eligible studies on real-time medication recognition systems on real-time medication recognition systems that combine deep learning (DL), optical character recognition (OCR), and accessible feedback mechanisms. Following PRISMA 2020, searches were conducted in IEEE Xplore, Scopus, PubMed, Web of Science, ACM Digital Library, and ScienceDirect for studies published from 2019 to July 2025. Fifty studies met the eligibility criteria. Of these, 30 reported quantitative accuracy and/or latency metrics suitable for comparative extraction. The evidence was synthesized across platform type, recognition technique, evaluation dimension, multimodal integration, and deployment feasibility. Reported accuracies ranged from approximately 70% to 99%, but direct comparison remains limited by differences in datasets, number of medicine classes, image conditions, and metric definitions. Latency evidence indicates that several mobile and embedded systems support sub-second to near-real-time feedback, while cloud-assisted systems may require longer response times. Mobile, wearable, embedded, and multimodal approaches show complementary strengths, yet persistent gaps remain in public datasets, real-world validation with visually impaired users, robust multilingual OCR, transparent quality reporting, and healthcare-system integration. This review contributes a structured synthesis of DL-OCR techniques, accessibility features, and deployment constraints for assistive medication recognition.
A Scholarship Award Recommendation System by Integrating Interpolation and Profile Matching Methods I Made Pande Darma Yuda; I Made Agus Wirawan; Putu Hendra Suputra
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.112616

Abstract

Scholarships are financial aid provided to students to support the continuity of their studies. Universitas Pendidikan Nasional (UNDIKNAS) offers various scholarship programs, both from internal sources and external institutions, with limited quotas. However, the selection process, which has not been fully based on objective assessment, has resulted in outcomes that do not optimally reflect the actual eligibility conditions of applicants. To determine appropriate scholarship recipients, a selection mechanism based on objective and representative criteria is required. This study identifies 15 criteria derived from four types of scholarships, namely Kartu Indonesia Pintar (KIP) Kuliah, PPA, Unggulan, and Bank Indonesia, including parental income, parental education, number of dependents, residential status, land area, building area, electricity capacity, average report card score, academic and non-academic achievements, type of vehicle, foreign language proficiency, and certificate of financial hardship. Furthermore, the weighting of these criteria is conducted using the Entropy method to objectively determine the level of importance of each criterion. The Decision Support System (DSS) is designed by applying a two-point linear interpolation method to determine the alternative values for each criterion, while the Profile Matching method is used to match the participants’ profiles with the ideal profile, calculated based on core factor and secondary factor weights. This study utilizes 82 Undiknas scholarship data from the 2021 to 2024 cohorts and 44 Bank Indonesia scholarship data from the 2022 and 2023 cohorts. The results of this study indicate that the Interpolation method is capable of generating alternative values for each criterion in an objective and structured manner, as the calculations are based on a mathematical approach that considers predefined value ranges. Based on the evaluation and testing conducted, one weight combination demonstrated a high level of ranking consistency between the actual ranking results and the predicted rankings, namely the 0.50:0.50 weight combination with a value of 0.84168653. This value is categorized as very strong, as it falls within the range of 0.80–1.00 according to the predetermined Spearman Rank correlation interpretation scale.
An Integration Framework of Google Earth Engine as a Cloud Processing Engine in a Web-GIS Architecture for Flood Risk Analysis Ni Luh Suryani Agustini; Ketut Agus Seputra; Pariwate Varnakovida; Kadek Yota Ernanda Aryanto
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.112765

Abstract

Flooding is a significant hydrometeorological hazard in Southeast Asia, causing severe social, economic, and environmental impacts. Effective flood risk management is hindered by the lack of timely and accurate information, especially in areas such as Nakhon Pathom, Thailand, where seasonal floods frequently disrupt settlements and infrastructure. This study addresses this issue by proposing an integration framework that combines Google Earth Engine (GEE), a cloud-based geospatial analytics platform, with Web-GIS architecture for flood risk analysis. The integration aims to process large-scale, multi-temporal satellite data, such as Sentinel and Landsat imagery, to map flood-prone areas, analyze rainfall, and assess historical flood events. The system leverages GEE’s cloud computing capabilities to perform spatial analyses and deliver results via an interactive Web GIS interface. The architecture includes a proxy server to enable secure, efficient communication between the Web-GIS frontend and the GEE backend. By adopting this cloud-based approach, the system improves scalability, enhances data processing efficiency, and ensures accessibility via web browsers on various devices. The study shows that this integrated framework enables real-time flood risk assessments and dynamic visualizations, supporting decision-making in disaster management. This approach can be generalized to other disaster risk analyses, offering a scalable solution for flood mitigation and adaptation to climate change.
Optimizing V2X Intersection Safety: A Hybrid KF-LSTM Framework for Intelligent Collision Detection Ni Putu Amanda Saraswati; Ngurah Indra ER; Ni Made Ary Esta Dewi Wirastuti
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.112808

Abstract

This position paper argues that current Vehicle-to-Everything (V2X) safety strategies have reached a plateau due to their reliance on rigid, single-prediction models that fail to account for the heterogeneous movement characteristics of road users at intersections. A position paper, as recognized in the scientific literature, advocates for a specific research direction through systematic evidence synthesis and preliminary validation, rather than presenting exhaustive experimental results. Our systematic review of 24 studies spanning 2020–2025 reveals a critical tension: while Long Short-Term Memory (LSTM) models dominate the research landscape with a 42% share, they are computationally expensive for edge deployment, whereas lighter kinematic models exhibit prediction errors exceeding 3.60 meters in complex settings. To resolve this tension, we position a hybrid Kalman Filter (KF) and LSTM architecture implemented within the Sensing, Connectivity, Intelligence, and Actuating (SCIA). The KF, augmented with a formally specified stop-aware kinematic logic, handles structured vehicle trajectories, while LSTM exclusively handles the stochastic behavior of pedestrians. Preliminary co-simulation results (SUMO, OMNeT++, Artery; 500-second scenario; 14,000+ samples per horizon) demonstrate that the hybrid model reduces pedestrian trajectory RMSE from 5.07 m to 1.23 m (75.74% reduction) and vehicle RMSE from 23.69 m to 21.57 m (8.94% reduction) at the 5-second horizon compared to a linear regression baseline. These results confirm that asymmetric model delegation is architecturally superior to uniform single-model approaches and validate the proposed direction for future V2X safety application development. Full collision detection performance evaluation constitutes the primary contribution of a companion experimental study currently in preparation.
Static Sign Language Classification Using RGB, Skeleton Image, and Fusion Representations: A Cross-Dataset Evaluation Moch. Iskandar Riansyah; Mohamad Yani; Ubaidillah Umar; Helmy Widyantara
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.114587

Abstract

This study investigates static sign language classification by examining three image-based input representations, namely RGB images, skeleton images, and RGB+skeleton fusion, using two datasets with different visual characteristics. The first dataset reflects a relatively controlled acquisition setting, whereas the second dataset contains more complex background and visual variations. As an initial baseline, eight pretrained convolutional neural network (CNN) architectures were evaluated, and two representative models were subsequently selected for more detailed analysis. The baseline evaluation indicates that ResNet50 and EfficientNetB0 achieve the most competitive performance when RGB images are used as input. Further analysis of input representations shows that skeleton images are highly effective, particularly on the more challenging dataset, while RGB+skeleton fusion does not consistently improve classification performance. The cross-dataset evaluation further reveals a considerable performance drop across all configurations, suggesting the presence of a strong domain shift between the two datasets. In the A-B scenario, EfficientNetB0 with RGB input yields the best results, while in the B-A scenario, EfficientNetB0 with skeleton input shows the most stable performance. These findings indicate that the most effective input representation depends on the direction of domain transfer and that high intra-dataset performance does not necessarily reflect good generalization capability.
AI-Based IPTV Monitoring System for Optimizing Hospitality Entertainment Services: A Case Study of Radisson Blu Uluwatu Bali Sidin Rahman; Luh Gede Astuti; Cokorda Pramartha; I Ketut Gede Suhartana; Anak Agung Ngurah Istri Eka Karyawati; I Made Widiartha
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 15 No. 2 (2026)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v15i2.115194

Abstract

This study proposes an intelligent IPTV monitoring system aimed at enhancing service reliability and operational performance in hospitality environments. The system integrates multicast IPTV monitoring and Chromecast tracking with the Random Forest algorithm to identify, classify, and handle service disruptions automatically. Developed using the Design Science Research methodology, the system was evaluated through Quality of Service (QoS) and Mean Opinion Score (MOS) measurements. The findings show that the proposed model achieves an accuracy rate of 94.37% while improving several network performance indicators, including lower delay, reduced packet loss, and higher throughput. In addition, the implementation of real-time notifications helps accelerate response handling and supports more efficient operational processes. By combining AI-based monitoring with multicast and Chromecast technologies, the proposed approach provides a practical and scalable solution for improving digital entertainment services in real hospitality operations.

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