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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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A Security Enhancement to The Secure Mutual Authentication Protocol for Fog/Edge Farida, Yeni; Azzahra, Arsya Dyani; Lestari, Andriani Adi; Siswantyo, Sepha; Handayani, Annisa Dini; Priambodo, Dimas Febriyan
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.84725

Abstract

A secured mutual authentication protocol (SMAP Fog/Edge) has been developed for fog computing. The protocol provides secure mutual authentication which lightweight and efficient for fog computing environments. However, based on AVISPA’s verification from Azzahra research, this protocol has been found to be vulnerable to man-in-the-middle (MITM) attacks due to the absence of an authentication scheme between authentication server and the edge user. The attacks are carried out assuming that the public key of the fog server is not distributed over a secure channel. Rhim research and Lestari research successfully enhance the security level with digital signature. In line with that, we propose modified mechanism that utilizes encryption and digital signatures to substitute the secure channel for distributing the public key of the fog server and authenticating edge users by the authentication server. All modification is using authentication server for digital signature to enhance the security of SMAP Fog/Edge and make it resistant to man-in-the-middle attacks. The proposed protocol is revalidated using the AVISPA tool to determine whether the vulnerability still exists. The result indicates prototype successfully resistant to MITM
MultiResUNet for COVID-19 Lung Infection Segmentation Based on CT Image Ferdinandus, F.X.; Setiawan, Esther Irawati; Santoso, Joan
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.85386

Abstract

Image segmentation plays a crucial role in medical image analysis, facilitating the identification and characterization of various pathologies. During the COVID-19 pandemic, this technique has proven valuable for detecting and assessing the severity of infection. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have significantly enhanced the efficacy of image segmentation. Numerous CNN-based architectures have been proposed in the literature, with MultiResUNet emerging as a promising approach. This study investigates the application of the MultiResUNet architecture for segmenting regions of COVID-19 infection within patient lung CT images. Experimental results demonstrate the effectiveness of MultiResUNet, achieving an average Dice score of 73.10%.
CS-Bot: Smart and Interactive Digital Innovation as a Modern Solution for Early Detection of Stunting Andesti, Cyntia Lasmi; Dian, Rahmad; Ningsih, Sri Restu
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.85660

Abstract

The high prevalence of stunting in West Sumatra Province, especially in Pariaman City, is a big challenge in efforts to improve children's health. With more than 600 children at high risk of stunting, manual approaches in examining and recording health data are less effective. This system not only slows down the detection process but also has the potential to result in data loss and redundancy. In this context, late detection hinders early prevention, which is especially important in the First 1000 Days of Life (HPK) period. Therefore, this research aims to develop a chatbot-based application that is able to detect stunting quickly and accurately. This application is named CS-Bot, namely Prevent Stunting using Chatbot. This application is designed to be easy for the public to use, while providing education about the importance of stunting prevention. Research methods include analysis of data on factors and symptoms of stunting, formation of rules using the Forward Chaining Method, and probability calculations using Certainty Factor. The data used in this study comes from health centers and some data comes from several experts who are accustomed to dealing with stunting problems in children. It is hoped that the research results will not only provide tools for parents and the community, but will also become the basis for making more effective policies in stunting prevention programs in Indonesia, especially in West Sumatra.
Multimodel Prediction Score Based on Academic Procrastination Behavior in E-Learning Sartana, Bruri Trya; Nugroho, Supeno Mardi Susiki; Yuhana, Umi Laili; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.85880

Abstract

This research investigates the impact of academic procrastination on student performance in online learning environments and explores a multimodel approach for grade prediction. Academic procrastination is a well-documented issue that negatively affects learning outcomes, often leading to lower academic performance and increased dropout rates in self-paced learning platforms. This study analyzes behavioral data from 377 students, extracted from Moodle activity logs, which record real-time student interactions with learning materials. To address the gap in understanding procrastination patterns through activity logs, key procrastination-related features were derived from timestamps of task access, submission, and engagement duration. Using K-Means clustering with the Elbow method, students were categorized into three procrastination clusters: low procrastination with high academic performance, high procrastination with low performance, and moderate procrastination with average performance. Seven machine learning models were evaluated for predicting student grades, with Random Forest (RF) achieving the highest accuracy (R² = 0.812, MAE = 6.248, RMSE = 8.456). These findings highlight the potential of using activity logs to analyze procrastination patterns and predict student performance, allowing educators to develop early intervention strategies that support at-risk students and improve learning outcomes.
Cost-Effective Parkinson’s Disease Diagnosis Through IoT-Based Finger Tapping and Real-Time Machine Learning Classification Arraziqi, Dwi; Sardjono, Tri Arief; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.86371

Abstract

Parkinson's disease (PD) is a progressive neurological condition that significantly impacts motor functions, including finger tapping (FT). This study aims to develop a cost-effective, real-time, easily implementable, IoT-enabled electronic health record (EHR)-integrated FT analysis system capable of remotely detecting PD with high accuracy. The study uses peak amplitude, the Internet of Things (IoT), and various machine learning classifiers to detect PD through FT pattern analysis on a smartphone application. K-Nearest Neighbors, Convolutional Neural Networks, Support Vector Machines, and Logistic Regression exhibited 100% accuracy, while Naïve Bayes and Decision Trees (DT) had accuracies ranging from 71% to 92%. All classifiers had an Area Under the Curve (AUC) value of 1, except DT with an AUC value of 0.75. This study introduces a novel IoT system for PD detection that demonstrates high diagnostic accuracy, cost-effectiveness, real-time monitoring capability, easy implementation, scalability for telemedicine, and accessibility to EHR during the COVID-19 pandemic. Future studies will focus on expanding the dataset.
Optimizing Brain Tumor MRI Classification with Transfer Learning: A Performance Comparison of Pre-Trained CNN Models Mardianto, M. Fariz Fadillah; Pusporani, Elly; Salsabila, Fatiha Nadia; Nitasari, Alfi Nur; Lu’lu’a, Na’imatul
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.87377

Abstract

This study aims to classify brain MRI images into several types of brain tumors using the Convolutional Neural Network (CNN) approach with transfer learning. This method has the advantage of processing complex images in a shorter time than conventional CNN approaches. In this study, the data used was a public database from Kaggle, which consisted of four categories: glioma, meningioma, no tumor, and pituitary. Before entering the transfer learning process, data augmentation is carried out on the training data. Four pre-trained CNN models were used: VGG19, ResNet50, InceptionV3, and DenseNet121. The four models compared their ability to classify MRI images with several evaluation metrics: accuracy, precision, recall, and F1 score. The results of the performance comparison of the four pre-trained models show that the ResNet50 is the best model, with an accuracy of 98%. Meanwhile, VGG19, DenseNet121, and InceptionV3 produce 97%, 96%, and 95% accuracy, respectively. The ResNet50 architecture demonstrated superior performance in brain tumor classification, achieving 98% accuracy. It can be attributed to its residual learning structure, which efficiently manages complex MRI features.  Further research should concentrate on larger, more diverse datasets and advanced preprocessing techniques to enhance model generalizability.
Balinese Script Handwriting Recognition Using CNN and ELM Hybrid Algorithms Mas Diyasa, I Gede Susrama; Wijaya, Pandu Ali; via, Yisti Vita
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.87524

Abstract

One of the foundational scripts used in Balinese culture is the Balinese script, known as “Aksara Bali”. In its writing, Aksara Bali follows specific rules regarding distinctive stroke shapes that must be carefully maintained to preserve authenticity and readability. This study proposes the use of a hybrid algorithm combining Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM) to recognize handwritten Balinese script characters. The preprocessing stage includes dataset splitting, rescaling, data augmentation, batch size adjustment, and visualization of class distribution. The training stage utilizes the Adam Optimizer to enhance model accuracy. Using 1,691 images of various Balinese script characters, the dataset is divided into an 80:10:10 ratio for training, validation, and testing. Experimental results show that the best accuracy achieved is 91%, indicating that the CNN-ELM hybrid model effectively recognizes Balinese script characters.
Optimizing Sentiment Analysis of Electric Vehicles Through Oversampling Techniques on YouTube Comments Lapendy, Jessica Crisfin; Resky, Andi Aulia Cahyana; Tenriola, Andi; Surianto, Dewi Fatmarani; Sidin, Udin Sidik
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.88205

Abstract

Air pollution from motorized fuel vehicles causes adverse impacts on the environment and human health, driving the need for more sustainable alternatives such as electric vehicles. However, the transition to electric vehicles is often met with mixed responses from the public, reflected by sentiments that are split between positive and negative. This research investigates such sentiments through analyzing comments on the YouTube platform, which are classified using two algorithms, SVM and Naïve Bayes, and three oversampling techniques: Random Oversampling, SMOTE, and ADASYN. A comparative evaluation is conducted to determine the most effective algorithm and oversampling strategy for handling imbalanced sentiment data, where negative comments dominate. Initial experiments showed that Naïve Bayes with SMOTE achieved the best result among baseline models, with 64% accuracy. However, traditional oversampling methods alone were not sufficient to significantly improve classification quality. To address this, the study proposes a hybrid method that combines Easy Data Augmentation (EDA), specifically Synonym Replacement (SR), with oversampling techniques. The proposed method substantially improved performance. Naïve Bayes combined with SR and SMOTE or Random Oversampling achieved 88% accuracy, with F1-scores of 0.84–0.85 for the positive class. The best result was obtained using SVM with SR and Random Oversampling, reaching 97% accuracy and F1-scores of 0.97 (negative) and 0.96 (positive). These findings demonstrate the effectiveness of combining augmentation and oversampling in improving sentiment classification and provide insights for stakeholders in promoting EV adoption.
Virtual Smart School: A Blended Learning Approach for Schools in Papua’s 3T Regions Sampebua, Mingsep Rante; Supiyanto, Supiyanto; Kmurawak, Remuz Maurenz
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.90329

Abstract

Traditional classroom-based teaching methods are still used in schools, particularly in Papua’s 3T (frontier, outermost, undeveloped) areas. This highly relies on the teacher-centered method and limits the completion of the curriculum due to time constraints imposed by the schedule. This research aims to design a web-based virtual smart school as a blended learning solution for schools in Papua’s 3T areas. The virtual smart school learning media application is developed using the ADDIE model, a five-step process encompassing analysis, design, development, implementation, and evaluation. This research results in a virtual smart school application that can increase student motivation, enable independent learning, evaluate student learning progress, provide quick access to learning materials, and facilitate interactions between teachers and students at any time or location. The research concludes that virtual smart schools can become a blended learning solution to improve the quality and equity of education in underdeveloped areas of Papua.
Service Management Audit of SIKUAT Using The ITIL 4 Framework Darmawan, I Gede Hadi; Gunantara, Nyoman; Sudarma, Made
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.91093

Abstract

The Balinese Traditional Village of Financial Information Management System (SIKUAT) is a web-based information technology service used to manage the financial administration of 1,493 Traditional Villages in Bali. To ensure service quality, user convenience, and improved service performance aligned with user needs, it is necessary to conduct a service management audit on SIKUAT. This research focuses on auditing the service management of SIKUAT by adopting the ITIL 4 framework, particularly within the domains of continual improvement, service continuity management, and infrastructure and platform management. The assessment was carried out using the Capability Maturity Model (CMM) method through interviews and document analysis. The results of the audit indicate that the maturity level of the SIKUAT service is at level 3 (defined) with an average score of 3,86, while the capability level is at level 2 (managed). The continual improvement domain has the highest maturity level, reaching level 4, which indicates that the improvement processes are relatively well-directed. However, the service continuity management domain and the infrastructure and platform management domain still require serious attention to improve their maturity levels, which are currently still at level 3. This condition indicates the need for further improvements in service sustainability and infrastructure management. Based on the audit results, it is recommended to establish a comprehensive infrastructure maintenance policy, define standards for incident handling, and conduct regular evaluations of features and data security. The implementation of these recommendations is expected to enhance the efficiency and sustainability of the SIKUAT service, thereby supporting more transparent and accountable financial governance in Traditional Villages. In addition, this study may serve as a reference for the Provincial Government of Bali in improving the SIKUAT service.

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