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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 646 Documents
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Systematic Literature Review: Use of Augmented Reality as A Learning Media: Trends, Applications, Challenges, and Future Potential Heydemans, Charnila; Elmunsyah, Hakkun
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.78825

Abstract

This article conducts a systematic literature review (SLR) focusing on the application of Augmented Reality (AR) as an educational tool. The review process, guided by SLR and PRISMA methodologies, included steps such as identification, screening, eligibility assessment, inclusion, and data analysis, utilizing tools like Publish or Perish 8 and NVIVO 12 Plus. An initial search on Scopus produced 800 articles, which were subsequently narrowed down to 59 relevant studies. These were analyzed with NVIVO 12 Plus according to specific topics. The results indicate that AR effectively enhances students' academic achievement, interest, motivation, and participation across various subjects such as science, mathematics, languages, and engineering education. However, challenges include hardware and software limitations and insufficient technical training for teachers. AR holds great potential for improving learning experiences, particularly for students with special needs. Future developments should focus on affordable software and adequate teacher training to expand AR's educational use. Further research should explore AR in vocational education to better understand its specific requirements.
Analysis of Field Work Practice Information System Service Quality Using The Webqual 4.0 Method and Importance Performance Analysis Nurdiana, Dian; Maulana, Muhamad Riyan; Aprijani, Dwi Astuti; Amastini, Fitria
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.79182

Abstract

In the current digital era, the quality of website services is a crucial factor in supporting the effectiveness and efficiency of information systems, including the Information Systems Study Program Field Work Information System (SIPKL) at Universitas Terbuka. However, currently there is no in-depth evaluation of the quality of SIPKL services from a user perspective. This research aims to review the service quality of the SIPKL website as a whole and measure the level of user satisfaction with the services provided. To achieve this goal, the WebQual 4.0 method is used which measures three main dimensions of service quality, namely usability, information quality, and interaction quality. In addition, the Importance Performance Analysis (IPA) method is applied to evaluate the importance and performance of each service attribute being measured, so as to identify areas that require improvement. Data was collected through a survey with 100 respondents from Information Systems study program students who had used the SIPKL website. The research results show a value of 101.6% for the level of conformity, which indicates that the SIPKL website service performance has met or even exceeded user expectations and interests. Meanwhile, the gap value is categorized as “Good” with a positive value of 0.08 or >0. Indicators that require improvement are in quadrants II and III. Overall, this research provides strategic recommendations for SIPKL website managers to improve service quality so that it is more optimal in supporting students' needs in undergoing PKL.
Enhancement of Internal Business Process Using Artificial Intelligence Santoso, Joseph Teguh; Wibowo, Agus; Raharjo, Budi
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.79242

Abstract

This research aims to explore the feasibility of Artificial Intelligence (AI) enabled process improvement systems to assist businesses in optimizing Internal Business Process (IBP) by making and adopting suggestions and improvements. Over the last two decades’ technological advances in the new generation have allowed us to use more sophisticated systems to speed up different tasks, as well as AI which has cognate from theory to something more efficient and applicable. This study confirms that a feasible AI-based system can provide benefits to companies in terms of increasing revenue. A mixed method was used; quantitative research was carried out through surveys to gather knowledge about the use of AI in the IBP, while qualitative research was carried out through interviews to obtain an overview of the use of AI in certain IBP. The results show that constructing AI in process optimization is a complicated task than one might expect.
Detection of UDP Flooding DDoS Attacks on IoT Networks Using Recurrent Neural Network Warcita; Kurniabudi; Eko Arip Winanto
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.79601

Abstract

Internet of Thing (IoT) is a concept where an object can transfer data through a network without requiring human interaction. Complex IoT networks make it vulnerable to cyber attacks such as DDoS UDP Flood attacks, UDP Flood attacks can disrupt IoT devices. Therefore, this study proposes an attack detection method using a deep learning approach with the Recurrent Neural Network (RNN) method. This study uses Principle Component Analysis (PCA) to reduce the feature dimension, before learning using RNN. The purpose of this study is to test the combined performance of the PCA and RNN methods to detect DDoS UDP Flood attacks on IoT networks. The testing in this study used 10 datasets sourced from CICIOT2023 containing UDP Flood and Benign DDoS traffic data, and the testing was carried out using three epoch parameters (iterations), namely 10, 50, and 100. The test results using RNN epoch 100 were superior, showing satisfactory performance with an accuracy value of 98%, precision of 99%, recall of 99%, and f1-score of 99%. Based on the experimental results, it can be concluded that combining PCA and RNN is able to detect UDP Flooding attacks by showing high accuracy.
Banana and Orange Classification Detection Using Convolutional Neural Network Lumban Batu, Benedict Evan Lumban Batu; Saputra, Wahyu Andi; Sa’adah, Aminatus
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.80032

Abstract

Fruits play a crucial role in human health, with an average consumption of 81.14 grams per capita per day in Indonesia, where bananas and oranges are the most consumed fruits. Inconsistent fruit quality, typically evaluated manually by farmers, can influence consumer decisions. Artificial intelligence (AI) and computer vision can enhance efficiency and consistency in analyzing fruit quality. Convolutional Neural Networks (CNN) are particularly effective in image recognition. This research uses CNN to classify the quality of bananas and oranges from a dataset of 4000 images, divided into 10% test data, 80% training data, and 10% validation data. Among three models tested, Model 2 performed best with an accuracy of 96.75% and balanced high F1-scores across all categories. The results demonstrate that the CNN model is capable of classifying the quality of bananas and oranges with high accuracy and good evaluation results.
The Implementation of Enterprise Resource Planning During the Product Design Process Through the Process of Design Thinking Budi Dharma, I Gusti Bagus; I Gusti Bagus Baskara Nugraha
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.80691

Abstract

The implementation of Enterprise Resource Planning (ERP) systems in the product design phase plays a crucial role in modern industries. The product design phase with the design thinking approach produces an innovative product that meets user requirements. The product design process, which begins with capturing user requirements and culminating in a thorough specification of a finished product, requires extensive data and expertise. Iterative product design processes contribute to the complexity of the data that must be managed within an organization. This study involved the implementation of an Enterprise Resource Planning (ERP) software named Odoo within the product design process using a design thinking methodology. By examining the student practicum activities in automotive design, starting from the user survey phase and going all the way to the component design details, a comprehensive ERP system was developed. This system is capable of seamlessly integrating all the data throughout the entire process. Based on the outcomes of testing and assessment, it can be concluded that the modules in Odoo software can be effectively integrated into the product design process. Effective processes in integrating ERP into product design phases can improve production quality and efficiency as well as facilitate greater flexibility and innovation. Implementing ERP throughout the product design phase results in a seamless flow of information, enhanced inventory control, and overall productivity enhancement. This ultimately leads to operational efficiency, competitive advantage, and high user satisfaction in the industry.
Classification of Lung Diseases in X-Ray Images Using Transformer-Based Deep Learning Models Mahajaya, Nyoman Sarasuartha; Putu Desiana Wulaning Ayu; Roy Rudolf Huizen
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.81425

Abstract

This research evaluates the performance of two Transformer models, the Vision Transformer (ViT) and Swin Transformer, in the analysis of thoracic X-ray images. The study's objective is to determine whether Transformer models can enhance diagnostic accuracy for lung diseases, considering challenges such as early symptom variability and similar radiological signs. The dataset includes 21,165 X-ray images, featuring 3,616 COVID-19 cases, 10,192 normal images, 6,012 images of Lung Opacity, and 1,345 pneumonia images. Model development involved tuning hyperparameters such as epoch numbers and optimizer choice. The results indicate that using the AdamW and Adamax optimizers achieves an optimal balance between computational efficiency and accuracy. The Swin Transformer model, using the Adamax optimizer, reached the highest testing accuracy of 96.10% in 33,802.70 seconds, while the Vision Transformer achieved a testing accuracy of 95.10% in 33,503.10 seconds.
The Influence of Educational Robotics in STEM Integrated Learning and the Development of Computational Thinking Abilities Sadik, Muhammad Aqil; Budiyanto, Cucuk Wawan; Yuana, Rosihan Ari
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.81608

Abstract

Currently, educational robotics are becoming an important trend in education, introducing transformative elements into the classroom to improve the learning environment. Educational robotics in STEM-integrated learning can develop computational thinking skills. Educational robotics has begun to be widely adopted and is expected to enhance computational thinking skills in early childhood education, secondary school, and higher education. In this study, we examine the role of educational robotics in integrated STEM learning environments and its impact on the development of computational thinking. The method used was a systematic literature review. Initial search returned 541 articles from various journals indexed in Scopus. Subsequently, 351 articles published between 2020 2024 were sorted out, and only 37 articles were included in the final analysis. Studies show that educational robotics effectively promotes STEM education and facilitates the development of computational thinking skills. The importance of project-based learning and the integration of STEM disciplines in educational robotics inform educators and policymakers about the potential benefits of educational robotics in promoting STEM education and developing computational thinking skills.
Optimization of XGBoost Algorithm Using Parameter Tunning in Retail Sales Prediction Wijaya, Hendra; Hostiadi, Dandy Pramana; Triandini, Evi
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.82214

Abstract

In retail companies, the owner needs sales analysis to make decisions in the company's business processes. Several previous studies have introduced forecasting techniques using regression analysis, and classification approaches that need optimization. This article proposes a new approach to sales prediction using XGBoost, which is optimized by comparing the best performance from three optimization methods: Random search, grid search, and Bayesian optimization. The aim is to obtain the best comparative analysis and increase prediction accuracy. The novelty of the proposed model is determining the best value for each optimization method using XGBoost. The results of the evaluation show that the best results were achieved by the grid search optimization technique in the XGBoost model with an increase in the evaluation value R^2 from 97.31 to 98.41. The results of the proposed model analysis can help retail business owners in accurate sales predictions to determine the development of business processes.
Network Intrusion Detection Using Transformer Models and Natural Language Processing for Enhanced Web Application Attack Detection Priatna, Wowon; Sembiring, Irwan; Setiawan, Adi; Setyawan, Iwan
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.82462

Abstract

The increasing frequency and complexity of web application attacks necessitate more advanced detection methods. This research explores integrating Transformer models and Natural Language Processing (NLP) techniques to enhance network intrusion detection systems (NIDS). Traditional NIDS often rely on predefined signatures and rules, limiting their effectiveness against new attacks. By leveraging the Transformer's ability to capture long-term dependencies and the contextual richness of NLP, this study aims to develop a more adaptive and intelligent intrusion detection framework. Utilizing the CSIC 2010 dataset, comprehensive preprocessing steps such as tokenization, stemming, lemmatization, and normalization were applied. Techniques like Word2Vec, BERT, and TF-IDF were used for text representation, followed by the application of the Transformer architecture. Performance evaluation using accuracy, precision, recall, F1 score, and AUC demonstrated the superiority of the Transformer-NLP model over traditional machine learning methods. Statistical validation through Friedman and T-tests confirmed the model's robustness and practical significance. Despite promising results, limitations include the dataset's scope, computational complexity, and the need for further research to generalize the model to other types of network attacks. This study indicates significant improvements in detecting complex web application attacks, reducing false positives, and enhancing overall security, making it a viable solution for addressing increasingly sophisticated cybersecurity threats

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