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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
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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
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Perlindungan Transkrip Akademik Mahasiswa dengan Kombinasi Algoritma Rijndael dan SHA-3 Amsal Hamonangan Butarbutar; Ida Bagus Gede Dwidasmara
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.p22

Abstract

This paper addresses the issue of securing academic transcripts for students by utilizing a combination of Rijndael encryption algorithm and Secure Hashing Algorithm-3 (SHA-3). The primary purpose is to enhance the protection and integrity of academic records against unauthorized access and tampering. SHA-3 is selected for its advanced cryptographic capabilities, surpassing its predecessors in security and efficiency. When combined with the Rijndael algorithm, which serves as the foundation for the Advanced Encryption Standard (AES), the system achieves robust encryption and data integrity verification. Through comprehensive design, implementation, and testing phases, the study demonstrates that the proposed method is effective in safeguarding academic transcripts. The results indicate that the system not only ensures data security but also operates efficiently. This research provides a valuable reference for further development and implementation of secure academic data management systems. 
Klasifikasi Jenis Obat Berdasarkan Gejala Yang Dimiliki Pasien Menggunakan Metode K-Nearest Neighbors (KNN) Ngakan Putu Bagus Ananta Wijaya; Ida Ayu Gde Suwiprabayanti Putra
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.p21

Abstract

This research applies the K-Nearest Neighbors (KNN) algorithm to classify medicine types based on patient symptoms using a dataset from Kaggle with 200 rows and 6 columns. After preprocessing steps such as handling missing values, encoding categorical variables, and splitting data into training and testing sets, exploratory data analysis (EDA) was performed to understand the dataset's structure. The KNN model was evaluated with k values of 1, 2, and 3, finding the optimal k to be 3, achieving an accuracy of 77.50% with average precision of 0.76, recall of 0.69, and f1-score of 0.66. Lower accuracy was observed for k=2 (65.00%) and k=1 (67.50%), indicating that k=3 is the most effective for this dataset. These results suggest that while KNN is a viable method for classifying medicine types based on symptoms, larger datasets are recommended for improved accuracy. 
Analisis Perbandingan Kualitas Citra Hasil Steganografi DCT dan LSB Berdasarkan Parameter RMSE dan PSNR I Putu Krisnawan Putra; I Wayan Supriana
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.p20

Abstract

Steganography is a technique of hiding secret messages in digital media, one of the digital media that is often used is images. Two commonly used steganography methods are DCT (Discrete Cosine Transform) and LSB (Least Significant Bit). This research aims to analyze and compare the image quality of DCT and LSB steganography results based on RMSE (Root Mean Square Error) and PSNR (Peak Signal to Noise Ratio) parameters. DCT and LSB methods are implemented on several images with several variations of secret messages. RMSE and PSNR values are calculated for each stego image and analyzed to see which method produces better image quality. The results show that the LSB method produces lower RMSE values and higher PSNR values compared to the DCT method on all tested images. This shows that the LSB method is significantly better in maintaining the quality of the stego image compared to the DCT method. 
Perlindungan Seed Phrase dengan Enkripsi Dual-Layer Menggunakan Algoritma AES dan Caesar Cipher Raihan Akbar Maulana; I Wayan Santiyasa
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.p19

Abstract

In the digital era, securing sensitive information such as seed phrases are crucial to prevent unauthorized access and potential loss of digital assets. This paper proposes a novel approach for protecting seed phrases using dual-layer encryption with AES algorithm and Caesar cipher. The AES algorithm is utilized to encrypt the seed phrase itself, providing a strong first layer of defense. Additionally, Caesar cipher is employed to encrypt the AES secret key, adding an extra layer of security to the encryption process. By combining these two encryption techniques, the security of the seed phrase is significantly enhanced, as both the phrase and its encryption key are protected. Furthermore, the encrypted seed phrase and key can be securely stored using email as a digital storage medium, enhancing accessibility while maintaining robust security measures. 
Perbandingan Neural Network MLP, KNN, dan Decision Tree untuk Klasifikasi Penyakit Diabetes I Made Prenawa Sida Nanda; I Putu Gede Hendra Suputra
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.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), KNearest 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. 
Implementasi Metode Design Thinking Dalam Perancangan UIUX Aplikasi Wisata Bali I Kadek Agus Candra Widnyana; I Made Widiartha
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.p17

Abstract

Many tourists find it difficult to efficiently plan or find tourist destinations in Bali. They need a convenient way to search for attractions or plan their trips. A system is needed to facilitate the search and planning of their tourist trips, which can be supported by utilizing technology such as smartphones and mobile applications, making UI/UX crucial to help users solve their problems. This design is carried out using Figma tools and the design thinking method, which consists of five stages: empathize, define, ideate, prototype, and test. This research aims to analyze and implement the Design Thinking method in designing the user interface (UI/UX) of a tourist application focusing on tourist destinations in Bali. 
Algoritma K-Means untuk Clustering Provinsi di Indonesia Berdasarkan Kasus Stunting Syelvia Julianti; I Made Widiartha
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.p16

Abstract

Stunting is a nutritional issue that poses a global challenge, especially in developing countries like Indonesia. According to UNICEF, Indonesia ranks among the top five countries with the highest stunting prevalence. To address this issue, clustering provinces in Indonesia each year can help ensure equitable food distribution and other resources. This can be done using the KMeans clustering algorithm, with the optimal number of clusters determined by the elbow method and evaluated using the silhouette coefficient and Davies-Bouldin index. The optimal number of clusters was found to be 3, with a silhouette coefficient of 0.50 and a Davies-Bouldin index of 0.70. In 2020, there were 15 provinces in cluster 1, 6 provinces in cluster 2, and 17 provinces in cluster 3. In 2021, 15 provinces were in cluster 1, 17 in cluster 2, and 6 in cluster 3. In 2022, there were 17 provinces in cluster 1, 14 in cluster 2, and 7 in cluster 3. In 2023, 5 provinces were in cluster 1, 14 in cluster 2, and 19 in cluster 3. By 2024, there were 18 provinces in cluster 1, 17 in cluster 2, and 3 in cluster 3. 
Optimalisasi Sistem Pencarian Produk Sunscreen Melalui Rancangan Ontologi Semantik I Gusti Agung Ayu Gita Pradnyaswari Mantara; I Komang Ari Mogi
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.p15

Abstract

As a tropical country, Indonesia receives exposure to sunlight throughout the year which contains ultraviolet (UV) rays that are harmful to the skin. The use of sunscreen is important to protect the skin, but the lack of structured information regarding the characteristics of sunscreen makes it difficult to choose the appropriate product. This research aims to build an ontology model for a sunscreen product search system that is tailored to skin conditions using the Methontology method. In the implementation stage, the sunscreen ontology was developed with 1 class, 7 subclasses, 7 object property hierarchies, and 29 individuals using Protégé software. Evaluation is carried out by asking a SPARQL query that shows the ontology's ability to provide relevant answers according to the knowledge being modeled. This sunscreen product ontology model can be used as a basis for developing related knowledge management systems such as sunscreen product search systems. 
Sistem Monitoring Kamar Tidur Pintar dan Suhu Berbasis IoT dengan Cisco Packet Tracer Ni Made Ayu Wirasih; I Ketut Gede Suhartana
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.p14

Abstract

This research endeavors to develop an Internet of Things (IoT)-based smart bedroom monitoring system, leveraging Cisco Packet Tracer technology as a robust simulation platform. The system facilitates automatic monitoring and management of indoor environment parameters such as temperature, security, and lighting settings, aiming to enhance occupants' comfort and safety. Key components of the system include a temperature sensor, microcontroller, and an LCD information screen, enabling real-time display of bedroom temperature data. System validation was conducted via simulations using Cisco Packet Tracer, consistently demonstrating the system's efficacy in automating room temperature monitoring and management. These findings lay a solid groundwork for advancing IoT technology, emphasizing deeper integration, addressing challenges, proposing solutions, and exploring potential applications across diverse contexts. 
Rancangan Machine Learning untuk Mendeteksi Lagu Plagiat Dominggo Pratama Sidauruk; I Gusti Ngurah Anom Cahyadi Putra
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.p13

Abstract

Plagiarism in the music industry is a serious issue that requires advanced solutions. This research proposes a Machine Learning-based system for detecting song plagiarism by combining Convolutional Neural Network (CNN) and Dynamic Time Warping (DTW). CNN is used to extract features from the visual representation of music notations, while DTW measures the temporal distance between two sequences of notations. Experimental results show that this system provides a more accurate solution with an accuracy of 92.71%, with a dataset of 4800 data points. 

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