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I Gede Surya Rahayuda
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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,
Computer Science & IT
JNATIA (Jurnal Nasional Teknologi Informasi dan Aplikasinya) merupakan jurnal yang berfokus pada teori, praktik dan metodologi seluruh aspek teknologi di bidang ilmu dan teknik komputer serta ide-ide produktif dan inovatif terkait teknologi baru dan sistem informasi. Jurnal ini memuat makalah penelitian asli yang belum pernah dipublikasikan dan telah melalui jurnal double-blind review. JNATIA (Jurnal Teknologi Informasi dan Penerapannya) diterbitkan empat kali setahun (Februari, Mei, Agustus, November).
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 255 Documents
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Perancangan UI/UX Website Pengenalan Budaya Bali dengan Metode User Centered Design Santiani, Ida Ayu Made Putri; Supriana, I Wayan
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 2 No 4 (2024): JNATIA Vol. 2, No. 4, Agustus 2024
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i04.p17

Abstract

Culture is a collection of beliefs and traditions passed down from generation to generation by a group of people. The island of Bali is one of the places rich in culture and traditions. Each district in Bali has its own unique culture and traditions. This cultural diversity and tradition serve as an attraction for domestic and international tourists who want to visit the island of Bali. To help tourists get to know more about Balinese culture and find places to eat according to their preferences, this research aims to develop a website-based application. This website is developed using the User-Centered Design method. This website can be a solution for tourists who want to visit Bali to see local traditions and culture or enjoy the typical dishes found on the island of Bali. Keywords: User Centered Design, Budaya Bali, UI/UX
Implementasi SHA-256 dalam Program Verifikasi Originalitas Video Sebelum dan Sesudah Proses Kriptografi Wijaya, Daniel Surya; Suhartana, I Ketut Gede
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 2 No 4 (2024): JNATIA Vol. 2, No. 4, Agustus 2024
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i04.p10

Abstract

This research aims to develop a computer program utilizing the SHA-256 algorithm to compare the authenticity between the original video and the video that has undergone cryptographic processes, particularly during the decryption phase. The program is designed to provide additional verification regarding the success of the decryption process in restoring the video to its original condition. The program development is conducted using the Python programming language. The SHA-256 algorithm is employed to generate hash values for both the original video and the decrypted video. The resulting hash values of the two videos are then compared to evaluate their similarity. The developed program successfully compares the authenticity between the original video and the decrypted video. Through the analysis of hash values using SHA-256, the program concludes whether the decryption process successfully restores the video to its original state or not. Keywords: SHA-256, Cryptography,Video,Hash,Python
Memprediksi Kelulusan Mahasiswa: Graduate dan Dropout dengan Support Vector Machine dan GridSearchCV Anggarini, Ni Putu Eka Marita; Muliantara, Agus
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 2 No 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Study Program, 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. Keywords: siswa, akademik, dropout, graduate, SVM, hyperparameter tuning, klasifikasi, prediksi, machine learning, GridSearchCV
Rancang Model Ontologi untuk Representasi Pengetahuan Busana Tradisional Indonesia Ananda, Ngurah Kelvin Febryanta Lila; Mogi, I Komang Ari
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 2 No 1 (2023): JNATIA Vol. 2, No. 1, November 2023
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Indonesia has islands that are inhabited by more than 255 million people, making Indonesia the fourth most populous country in the world. Not far from the population density in Indonesia, there are various kinds of culture, one of which is by showing the identity of each region by wearing their traditional clothes. Traditional clothing in various regions in Indonesia certainly has different uses and meanings and has its own characteristics, so it needs to be studied properly. The appropriate method for documenting Traditional Clothing is with an appropriate onological knowledge base to present the information. In this project, ontology methods are created using the Protege ontology developer tool. We apply the METHONTOLOGY method in the development of the ontology model, which describes in detail each step taken. The designed ontology model has 21 classes, 5 object properties, 2 data properties, and 32 individuals. We focus on explaining which materials, ethnicities and origins are used in Traditional Clothing. Testing is carried out using the ontology model development by performing a SPARQL query. Keywords: Ontologi, Busana Tradisional, SPARQL query, Web Semantik.
Impelementasi Kriptografi RSA dan XOR Cipher untuk Enkripsi Citra Digital KTP Artajaya, Gede Krisna Surya; Muliantara, Agus
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 2 No 4 (2024): JNATIA Vol. 2, No. 4, Agustus 2024
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i04.p01

Abstract

The advancement of technology has led to innovative solutions in administrative sectors, exemplified by the introduction of “KTP Digital”. However, not everyone has adopted “KTP Digital” and is still relying on scanned copies of identity cards that can expose digital image data to security vulnerabilities. This study addresses these vulnerabilities by proposing encryption techniques. Utilizing RSA and XOR Cipher algorithms, this research demonstrates effective encryption and decryption of digital image data. Evaluation metrics, including Peak Signal-to-Noise Ratio (PSNR), confirm minimal similarity between plain and cipher images, indicating robust encryption. Specifically, PSNR values for plain vs. cipher images range from 7 to 8 dB, well below 10 dB, indicating a very significant difference. Additionally, high PSNR values between original and decrypted plain images, which is 100 dB, suggest negligible data alteration post decryption confirming that the decryption process successfully restores the image to its original state. Keywords: Cryptography, Encrypting, Decrypting, RSA, XOR Cipher
Klasifikasi Ngengat dan Kupu-Kupu Menggunakan Metode GLCM dan Support Vector Machine Mardana, I Dewa Made; Astuti, Luh Gede
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 2 No 4 (2024): JNATIA Vol. 2, No. 4, Agustus 2024
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i04.p22

Abstract

Butterflies and moths are two types of insects that share similarities in their appearance and physical characteristics. Both insects exhibit a variety of colors, patterns, and body shapes that are often difficult to distinguish. This research aims to classify butterflies and moths using feature extraction from the Gray-Level Co-occurrence Matrix. The feature extraction process involves extracting values such as correlation, homogeneity, contrast, and energy from angles of 0°, 45°, 90°, and 135° in each butterfly and moth image. Furthermore, the Support Vector Machine method is used for classification. The research results indicate that using feature extraction from the Gray-Level Co-occurrence Matrix and the Support Vector Machine method can achieve an accuracy of 68.11%, with precision, recall, and F1-Score values of 70.0%, 68.0%, and 68.0%, respectively. Keywords: Classification, Gray-Level Co-occurrence Matrix, Feature extraction, Support Vector Machine, Butterflies, Moths
Perbandingan Neural Network MLP, KNN, dan Decision Tree untuk Klasifikasi Penyakit Diabetes Sida Nanda, I Made Prenawa; Suputra, I Putu Gede Hendra
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 2 No 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Study Program, 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.p18

Abstract

Diabetes is one of the diseases that has received global attention due to its extensive impact on public health. Most people with diabetes are unaware that they are suffering from this condition, this situation emphasizes the need for improved understanding and more effective treatment of this disease. In an effort to address these challenges, this study compares three machine learning algorithms for diabetes classification, the three algorithms are: Multi-Layer Perceptron (MLP), K-Nearest Neighbor (KNN), and Decision Tree. Data from the Diabetes Dataset used to train and test these models will go through preprocessing first starting from data cleaning, encoding because there is string data, data distribution analysis where in this study using under sampling to equalize data and normalization using min-max normalization, Evaluation results using Confusion Matrix and Classification Report which contains precision, recall, and f1-score the results of this evaluation show that the Neural Network MLP model achieves the highest accuracy of 90.48%, followed by KNN with 88.15% accuracy, and Decision Tree with 87.24% accuracy. These findings provide important insights in selecting the optimal model for diabetes prediction applications. Keywords: Diabetes, Machine Learning, Neural Network MLP, KNN, Decision Tree
Analisis Performa Sistem pada Pemisahan Database Analitik dengan Transaksional Santosa, I Made Ari Madya; Cahyadi Putra, I Gusti Ngurah Anom
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 3 (2023): JNATIA Vol. 1, No. 3, Mei 2023
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The speed and efficiency of a system at this time has a very important role. Currently there are several solutions to answer these challenges, one of which is to separate the database on the system. In this study, an analysis was carried out on the separation of databases on a transactional and analytic system. Analysis is carried out with a research flow that begins with problem identification followed by sampling data on technology companies that have transactional systems, then implementing transactional and analytical programs on microservices architecture, conducting tests with Load-Testing, followed by analysis of test results and then drawing conclusions. After conducting research in accordance with the research flow, it was concluded that the separation of databases on transactional and analytic systems is better for producing faster system performance compared to transactional and analytic systems using the same database. Keywords: Big Data, Optimization, Transactional, Analytical Systems, Microservices
Klasifikasi Citra Elektrokardiogram untuk Deteksi Penyakit Jantung Menggunakan Metode GLCM dan SVM Tamba, Andreas Panangian; Wibawa, I Gede Arta
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 2 No 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Study Program, 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. Keywords: Electrocardiography, Support Vector Machine, Gray Level Co-Occurrence Matrix, Classification, Myocardial Infarction
Implementasi Metode Analytical Hierarchy Process dalam Sistem Pendukung Keputusan Penerimaan Karyawan Baru Ryan, Ida Bagus Putu; Widiartha, I Made
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 2 No 4 (2024): JNATIA Vol. 2, No. 4, Agustus 2024
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i04.p13

Abstract

Decision support system as problem solving for accepting job applicants with objective assessment. This system has the function of getting the best way or solution in providing decisions that require complex calculations, with the best way being provided by mathematical algorithms combined with computing. In this case, the recommendation system will be able to help find a list of permanent employees from a collection of data in the database easily and efficiently. The application of this decision support system cannot be separated from a method that supports it, as applied in this research, namely the Analytical Hierarchy Process (AHP) to run an appropriate algorithm with weighting for many criterias at the system. Using this method to develop a recommendation system will be in accordance with the aim of creating a system for determining decisions for prospective employees in a business element. Keywords: recommendation system, analytical hierarchy process, applicant, employee, complex calculations, weighting

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