cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
I Gede Surya Rahayuda
Contact Email
igedesuryarahayuda@unud.ac.id
Phone
+6289672169911
Journal Mail Official
jnatia@unud.ac.id
Editorial Address
Sekretariat JNATIA Gedung FMIPA Lantai 1, Program Studi Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana
Location
Kota denpasar,
Bali
INDONESIA
Jurnal Nasional Teknologi Informasi dan Aplikasinya
Published by Universitas Udayana
ISSN : 29863929     EISSN : 30321948     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
JNATIA (Jurnal Nasional Teknologi Informasi dan Aplikasinya) adalah jurnal yang berfokus pada teori, praktik, dan metodologi semua aspek teknologi di bidang ilmu komputer, informatika dan teknik, serta ide-ide produktif dan inovatif terkait teknologi baru dan teknologi informasi. Jurnal ini memuat makalah penelitian asli yang belum pernah diterbitkan. JNATIA (Jurnal Teknologi Informasi dan Aplikasinya) diterbitkan empat kali setahun (Februari, Mei, Agustus, November).
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 316 Documents
 21   22   23   24   25 
Memanfaatkan NodeMCU dalam Serangan Jaringan Wi-Fi pada Frekuensi 2,4 GHz Tinjauan Keamanan dan Tindakan Pencegahan Ida Bagus Wahyu Semara Kamajaya; I Gusti Agung Gede Arya Kadyanan
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i03.p12

Abstract

NodeMCU, an open-source development platform based on the ESP8266, has become a significant tool in network security testing. This article investigates the use of NodeMCU in attacks against Wi-Fi networks on the 2.4 GHz frequency. We conducted experiments to launch NodeMCU's capabilities to exploit security weaknesses in Wi-Fi networks, including attacks against WEP, WPA, and WPA2 encryption. We also present preventive measures that can be implemented to protect Wi-Fi networks from attacks possible using NodeMCU. 
Pengembangan Algoritma Caesar Cipher dalam Game-Based Learning untuk Pendidikan Kriptografi pada Anak Putu Arya Dharma Kesuma; I Gede Surya Rahayuda
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i03.p11

Abstract

The decline in children's interest in learning due to easy access to games is the main reason I designed game-based learning. To make learning more engaging, presenting materials in an interactive way is essential. Game-based learning addresses this issue by packaging educational content attractively, increasing children's interest in learning. This game will introduce the world of IT to children, particularly cyber security. One key concept is cryptography, using the Caesar cipher algorithm due to its simplicity and suitability for children. The Caesar cipher enables both encryption and decryption processes, making it perfect for teaching cryptography basics. By designing the game as an interactive mystery, it aims to enhance children's learning interest and introduce them to cyber security effectively, making learning both fun and educational. 
Pengklasifikasian Kualitas Pisang dengan Deep Learning CNN Arsitektur VGG16 Vodka Joe Junior; I Gede Santi Astawa
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i03.p10

Abstract

Bananas are one of the most popular fruits consumed worldwide, valued for their nutritional benefits and versatility in various dishes. However, ensuring banana quality, including ripeness and integrity, remains crucial in meeting consumer expectations and maintaining supply chain standards. Manual classification of banana quality can be tedious, prompting the need for efficient methods. In this study, we explore the classification of banana quality using Convolutional Neural Network (CNN) with VGG16 architecture and image augmentation. Leveraging previous research and considering the superior performance of VGG16, we gathered data from Kaggle and evaluated our model's accuracy. The implementation yielded promising results, achieving a peak accuracy of 97.50% with 15 epochs and an 80%-20% training-validation data split. This surpasses previous methods, indicating the effectiveness of CNN with VGG16 in banana quality classification. 
Klasifikasi Citra Elektrokardiogram untuk Deteksi Penyakit Jantung Menggunakan Metode GLCM dan SVM Andreas Panangian Tamba; I Gede Arta Wibawa
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i03.p09

Abstract

Heart disease is a major cause of death worldwide. Electrocardiogram (ECG) is a common method used to detect heart abnormalities. Analyzing ECG signals requires expertise and can be time-consuming. This study investigated the use of machine learning to classify ECG images for heart disease detection. The proposed method utilizes Gray Level Co-occurrence Matrix (GLCM) for feature extraction such as Dissimilarity, contrast, energy, ASM, homogeneity and Correlation. Meanwhile using Support Vector Machine (SVM) for the classification. We achieved an accuracy of 99.61% using this approach. The results suggest that the combination of GLCM and SVM can be a valuable tool for ECG image classification and potentially aid in early and accurate diagnosis of heart disease. 
Deteksi Pneumonia dengan Ekstraksi Fitur Gray-Level Co-occurrence Matrix (GLCM) dan Support Vector Machine (SVM) I Gusti Bagus Sutha Arianata Putra; Gst. Ayu Vida Mastrika Giri
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i03.p08

Abstract

Pneumonia, a prevalent lung disease globally, poses significant challenges in accurate diagnosis despite its severity. This paper proposes a novel approach leveraging Support Vector Machine (SVM) classification and Gray-Level Co-occurrence Matrix (GLCM) analysis on chest X-ray images to aid in pneumonia diagnosis. By extracting pneumonia-indicative features from digital X-ray images using Gray-Level Co-occurrence Matrix (GLCM) and employing Support Vector Machine (SVM) for classification, the study aims to enhance pneumonia diagnosis effectiveness, particularly crucial in regions with limited healthcare resources. The proposed method focuses on identifying characteristic patterns indicative of pneumonia in chest X-ray images and distinguishing between normal and pneumonia-affected images based on GLCM-extracted features. Furthermore, the study evaluates the impact of hyperparameter tuning using grid search on the proposed diagnostic system's performance, including accuracy, sensitivity, and specificity. By achieving these objectives, the research aims to contribute significantly to the development of more accurate and effective diagnostic tools for pneumonia, especially in resource-constrained areas. 
Klasifikasi Hewan Berbasis Fitur Gray Level Co-Occurrence Matrix dengan Artificial Neural Network Ryan Hangralim; Cokorda Pramartha
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i03.p07

Abstract

Dogs and cats are animals commonly treated as pets by many people. Humans have the ability to differentiate various things, and this ability when converted to a form of system is called Computer Vision. Computer Vision has many applications such as image processing that can be used for various things and one of the techniques in image processing is image classification. Image classification is a problem that aims to organize objects that are then observed into predefined categories. The approach utilized to construct the model involves employing an Artificial Neural Network (ANN) using Gray Level Co-occurrence Matrix (GLCM) as the method for extracting the features. The data used are images of cats and dogs which will be extracted using GLCM using various parameters that include distance and angles. The extracted feature will then be used to train a model and accuracy of each model will be measured to find the best parameter result. In this study, the best parameter that results in the best accuracy is 1 for the distance and 0°, 45°, 90° for the combination of angles resulting in 79% accuracy. 
Klasifikasi Penyakit Gagal Jantung dengan Algoritma XGBoost I Gusti Gde Bagus Bhadrika Artawibawa; Anak Agung Istri Ngurah Eka Karyawati
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i03.p06

Abstract

Heart, as one of the most important organs in the body, carries a risk of death if abnormalities occur. Heart problems are divided into two categories: heart failure and heart attacks. According to WHO data, approximately 7.3 million people worldwide die due to heart disease. This study uses a dataset of heart disease patients and applies the XGBoost algorithm. The objectives of this study are to process and analyze the data, implement the XGBoost algorithm for heart disease classification, and evaluate the performance of the XGBoost algorithm. The result of this study is the performance evaluation of the XGBoost algorithm, which achieved an accuracy of 93%. 
Klasifikasi Ekspresi Wajah Menggunakan Metode CNN Studi Kasus Dataset Kaggle Wayan Restama Yasa; Anak Agung Istri Ngurah Eka Karyawati
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i03.p05

Abstract

This research aims to implement a Convolutional Neural Network (CNN) in facial expression classification using the Kaggle dataset which consists of five types of facial expressions, namely anger, disgust, fear, happiness and sadness. This method is considered important in supporting various applications such as emotion detection, facial recognition, and better human-machine communication. In this research, data preprocessing and augmentation were carried out using ImageDataGenerator to increase data diversity and prevent overfitting. Next, a CNN architecture is built which consists of convolution layers, pooling layers, and Dense layers. The model was trained using the Adam optimizer with a categorical crossentropy loss function for 50 epochs. The results show that the model achieves approximately 51% accuracy on the validation set. However, further analysis showed variations in model performance among facial expression classes, with some classes performing better than others. 
Memprediksi Kelulusan Mahasiswa Graduate dan Dropout dengan Support Vector Machine dan GridSearchCV Ni Putu Eka Marita Anggarini; Agus Muliantara
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i03.p04

Abstract

In today's educational landscape, having a model to predict whether a student will graduate or drop out based on their academic statistics is highly beneficial. Such a model allows for early assessment of academic success. Human calculations alone can be time-consuming and often lack accuracy, hence the introduction of machine learning models to address this issue. This research utilizes a dataset comprising undergraduate students from various majors in higher education institutions. The data were collected while the students were still enrolled, with their grades from the first year serving as a key feature. The response variable in the dataset is labeled as either 'dropout' or 'graduate'. We employ Support Vector Machines (SVM) with GridSearchCV optimization to build the predictive model. The goal of this model is to predict a student’s academic success as early as their first-year statistics are available. If a student is predicted to drop out, targeted interventions can be provided to help them overcome challenges, ultimately aiming to improve graduation rates. 
Deteksi Objek pada Citra Menggunakan Model YOLO Intara Pratama Harahap; Agus Muliantara
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i03.p03

Abstract

Object detection is a crucial task in the field of computer vision and digital image processing, with numerous practical applications. This paper focuses on the implementation of the You Only Look Once (YOLO) model, a deep learning-based approach for object detection. The YOLO model offers several advantages over previous methods, such as simultaneous prediction of bounding boxes and object class probabilities, a relatively simple Convolutional Neural Network (CNN) architecture, and high computational speed, making it suitable for real-time applications. The study utilizes a dataset of 770 images, with 524 for training, 136 for validation, and 110 for testing, specifically focused on detecting various pet animals. The training process involves annotation of the image data, followed by training and validation of the YOLO model. The results demonstrate the model's ability to effectively detect and classify objects, achieving high performance metrics such as precision, recall, and mean Average Precision (mAP) nearing 0.8 towards the end of the training process. Additionally, a confusion matrix is presented, highlighting the model's accuracy in classifying different classes, with the highest accuracy for the 'Cat' class at 95%. The paper concludes by discussing the model's performance and potential areas for improvement. 

Page 23 of 32 | Total Record : 316

 21   22   23   24   25 

Filter by Year

2023 2026


Reset
Filter By Issues
All Issue Vol. 4 No. 2 (2026): JNATIA Vol. 4, No. 2, Februari 2026 Vol. 4 No. 1 (2025): JNATIA Vol. 4, No. 1, November 2025 Vol. 3 No. 4 (2025): JNATIA Vol. 3, No. 4, Agustus 2025 Vol. 3 No. 3 (2025): JNATIA Vol. 3, No. 3, Mei 2025 Vol. 3 No. 2 (2025): JNATIA Vol. 3, No. 2, Februari 2025 Vol. 3 No. 1 (2024): JNATIA Vol. 3, No. 1, November 2024 Vol. 2 No. 4 (2024): JNATIA Vol. 2, No. 4, Agustus 2024 Vol. 2 No. 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024 Vol. 2 No. 2 (2024): JNATIA Vol. 2, No. 2, Februari 2024 Vol. 2 No. 1 (2023): JNATIA Vol. 2, No. 1, November 2023 Vol. 1 No. 4 (2023): JNATIA Vol. 1, No. 4, Agustus 2023 Vol. 1 No. 3 (2023): JNATIA Vol. 1, No. 3, Mei 2023 More Issue