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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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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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Pengenkripsian File Data Pasien untuk Menjamin Kerahasian Informasi Medis Lie, Gary Melvin; Astuti, Luh Gede
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

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Abstract

In the era of digitalization of medical information, safeguarding patient data confidentiality has become paramount. This research aims to address the issue of data leakage by implementing file encryption using the AES-128 algorithm. The research methodology encompasses problem identification, system design, testing, and evaluation. The encryption steps of AES-128, namely SubBytes, ShiftRows, MixColumns, and AddRoundKey, are applied to enhance the security of patient data. Testing is conducted using various types of medical data, and an analysis is performed to assess the level of security and algorithm performance. The results indicate that file encryption with AES-128 can provide a high level of security for patient medical information. The AES-128 algorithm generates secure ciphertext that cannot be easily decrypted without the corresponding key. This research contributes to the field of medical information security by implementing AES-128 file encryption in patient data management systems. By enhancing data privacy and security, the utilization of this algorithm has the potential to provide strong protection against data breaches. Further studies can explore the wider application of AES-128 in the context of medical data security and improve algorithm performance. Keywords: AES, Encryption, Medical
Analisis Penggunaan Metode MFCC Dalam Mendeteksi Emosi pada Musik Indonesia Eka Wijaya, I Komang Sutrisna; Putri, Luh Arida Ayu Rahning
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 4 (2023): JNATIA Vol. 1, No. 4, Agustus 2023
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

This research aims to develop a method for detecting emotions in Indonesian music using the MFCC method. The MFCC method is used to identify emotions in music by measuring acoustic features of music, such as tempo, pitch, and intensity. The study uses a dataset of 40 Indonesian music samples from various regions, which are analyzed to detect emotions. The confusion matrix is used to calculate the precision of emotion prediction. The results show that the MFCC method is effective in detecting emotions in Indonesian music. The research also highlights the importance of using a representative dataset to improve the accuracy of emotion identification in music. This study provides insights into the challenges and opportunities of using the MFCC method for emotion detection in Indonesian music.. Keywords: MFCC, emotion detection, Indonesian music, dataset, confusion matrix.
Uji Performansi Algoritma Linear Regression dan Random Forest Regression pada Implementasi Sistem Prediksi Harga Rumah Wijaya, I Putu Teddy Dharma; Dwidasmara, Ida Bagus
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

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Abstract

Currently the house has become one of the needs that must be met. The price of a house is the main parameter that determines whether a person or organization buys or invests. In general, house prices are influenced by several factors, including building area, land area, number of bedrooms, number of bathrooms and number of garages. Currently, there are many websites devoted to providing information about buying and selling houses. This of course makes it easier for someone when looking for a house with the desired specifications without the need to come directly to the location. However, the house buying and selling platform does not provide a house price prediction feature that is in accordance with user specifications. This means someone who is planning to buy a house does not get an initial idea of the costs that must be spent to own the desired home. Therefore, in this study, researchers will design a web app-based house price prediction system that can make it easier for users to get predictions of the desired house price. In this study the prediction algorithms to be used are linear regression and random forest. Both algorithms will be analyzed for their performance and then the algorithm with the best level of accuracy will be applied as a predictive model which will be integrated with the user interface display. Keywords: House Prices, Linear Regression, Random Forest Regression
Perbandingan Berbagai Metode Segmentasi dan Mechine Learning pada Makanan Khas Tradisional Sumatera Utara Guna Meningkatkan Promosi Budaya dan Kuliner Nusantara Sitinjak, Anugrah Ignatius; Widiartha, I Made
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 4 (2023): JNATIA Vol. 1, No. 4, Agustus 2023
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

This study investigates the categorization of traditional North Sumatran dishes using various segmentation methods. The goal is to participate and participate in the preservation of North Sumatran culture. The study covers 34 types of traditional North Sumatran dishes originating from various regions. Food images are processed using segmentation techniques such as Sobel, Prewitt, Robert, Scharr, and Canny filters. The data set is then used in traditional machine learning algorithms, including Random Fortst, Decision Tree, and four SVM algorithms, for classification purposes. Among the algorithms with the highest performance, the Random Forest algorithm with Robert's segmentation method achieves outstanding results on dataset testing, with 85.52% accuracy, 84.63% recall, 83.77% precision, and 82.49% f1 score . The execution time for most of the best performing algorithms is around 1 minute on average. In addition, the Random Forest algorithm with the Canny operator achieves 81.51% accuracy, 84.97% recall, 86.81% precision, and 85.61% f1 score on dataset testing. The Random Forest algorithm with the Sobel operator obtains an accuracy of 78.41%, a recall of 65.28%, a precision of 62.33%, and an f1 score of 63.71%. Among the four SVM algorithms, the Sigmoid SVM with the Scharr operator achieves the highest performance in its category across all classification metrics. The importance of insight into the traditional cuisine of North Sumatra is invaluable. Emphasizing the importance of this research in promoting the preservation and introduction of traditional North Sumatran food. Keywords: North Sumatera, food, categorization
Klasifikasi Kualitas Buah dengan Menggunakan Convolutional Neural Network (Studi Kasus: Dataset Fresh and Rotten Classification) Dwijayana, I Gede Diva; Mahendra, I Putu Fajar Tapa; Simarmata, Ivan Luis; Giri, Gst. Ayu Vida Mastrika
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 2 No 2 (2024): JNATIA Vol. 2, No. 2, Februari 2024
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

This research aims to develop a deep learning model for fruit quality classification using Convolutional Neural Network (CNN) with the Fresh and Rotten Classification dataset. Two CNN models are compared, with the first model serving as the baseline and the second model resulting from parameter tuning based on the first model. The results indicate that increasing the number of epochs improves the model accuracy, as evidenced by the first model achieving 91% accuracy with 10 epochs and 93% accuracy with 15 epochs. Similar patterns are observed in the second model, with 87% accuracy at 10 epochs and 90% accuracy at 15 epochs. Despite the second model involving the addition of layers and parameters, its accuracy tends to be lower compared to the first model. The research emphasizes that increasing the number of epochs enhances model performance, while adding layers does not always lead to significant improvements, depending on the model's complexity and dataset characteristics. The first model, trained with 15 epochs, demonstrates the highest accuracy, approaching results from similar previous studies. This evaluation provides valuable insights for developing a CNN-based fruit classification model on the Fresh and Rotten Classification dataset. Keywords: Fruit Classification, Rotten, Fresh, Convolutional Neural Network, Accuracy, Epochs
Deteksi Plagiarisme Source Code Tugas Mahasiswa Menggunakan Algoritma Cosine Similarity Dan Pembobotan TF-IDF Eka Putra, I Gede Ariawan; Supriana, I Wayan
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 1 (2022): JNATIA Vol. 1, No. 1, November 2022
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

This research discusses the detection of plagiarism Source Code student assignments. The research purpose to design a system that can detect source code plagiarism on student assignments. The system designed using the Term Frequency weighting method — Inverse Document Frequency (TF-IDF) for word weighting and Cosine Similarity Algorithm to calculate the similarity between documents. By using this method, the test results obtained have similarities that are close to several documents from student assignment.
Implementasi Sistem Monitoring pada Bunga Anggrek Menggunakan Arduino dengan Aplikasi Berbasis Website Wandhana, Gede Krisnawa Sandhya; Agung Gede Arya Kadyanan, I Gusti
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

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Abstract

This article will focus on implementing an orchid plant monitoring system using Arduino in a web-based application. The purpose of this system is to monitor important environmental parameters such as temperature, humidity and soil moisture in real time. By collecting data from orchid plants using sensors, the system enables continuous measurement and analysis of these parameters. Collected data is processed and stored by the Arduino, which acts as the central control unit. A web-based application serves as the user interface, allowing remote access and visualization of monitoring data. Implementation of this system provides a practical solution for improving orchid cultivation through accurate and easily accessible monitoring of key parameters. This allows researchers and hobbyists to closely monitor growing conditions and make informed decisions based on the data collected. By maintaining optimal environmental conditions, the system contributes to improved growth, increased yields and successful cultivation of orchid plants. The combination of an Arduino and a web-based application provides an efficient and easy-to-use solution for orchid plant monitoring and management for better results in your orchid cultivation practice. Keywords : Orchid Plants, Arduino, Web-Based Application, Real-time Monitoring, Sensors
Perancangan User Interface pada Aplikasi Rekomendasi Tempat Wisata di Daerah Gianyar Mahagangga, Made Dhandy Satria; Astuti, Luh Gede
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

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Abstract

Bali, a beautiful island in Indonesia, offers the perfect combination of natural beauty, rich culture, and the friendliness of its people. Known for its stunning beaches, clear sea water, and stunning rice terraces, Bali is a popular tourist destination worldwide. In addition, the rich cultural life with religious ceremonies, traditional dances and sculpture makes Bali a center for artistic and cultural activities. Tourists can enjoy an unforgettable experience while exploring ancient temples, visiting traditional markets or interacting with the friendly locals. Bali is a mesmerizing paradise that promises a life full of adventure and peace. The purpose of this research is to build a recommendation system for tourist attractions which is expected to be able to make it easier for tourists who are on vacation to Bali to find tourist attractions they want to go to, especially in the Gianyar area. Keywords: Applications, Android, UML, Recommendation, Tourist Attraction
Identifikasi Lagu Berdasarkan Lirik Menggunakan Algoritma Boyer-Moore Apriana, I Komang Gede; Wibawa, I Gede Arta
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

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Abstract

Many people want a fast and efficient search method as technology advances. A song search is one example of this kind of search. A song is a collection of sing-along lyrics with rhythms and melodies for many to enjoy. Due to the large number of song lovers, some people are often constrained by the title of the song to be sung. This is caused by one factor, namely only memorizing some of the lyrics of the song to be sung. Given these problems, in this study a solution was developed, namely the application of identifying song titles based on input from the user's lyrics. The algorithm used by researchers in this study is the Boyer-Moore Algorithm, which is considered better in terms of matching substrings in longer texts. The research method used includes literature study, data collection, implementation, and testing. The implementation results show that the system successfully recognizes song titles with high accuracy based on the given piece of lyrics. In conclusion, this study proves that the development of a song title identification system based on snippets of lyrics using the website-based Boyer-Moore algorithm is an effective method. This system can help users recognize song titles based on the snippets of lyrics they remember with high accuracy. Keyword: song, lyrics, boyer-moore
Perbandingan Akurasi Algoritma Regresi Linier Putra, Indra Permana; Suhartana, I Ketut Gede
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 1 (2022): JNATIA Vol. 1, No. 1, November 2022
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

Along with technological advances, there is an approach to giving consideration when buying a house by analyzing prediction system. Research related to the accuracy comparison of the algorithm on the house price prediction gets conducted for precise prediction results. The algorithms used are Linear Regression, Polynomial Regression, and Support Vector Regression. The goal is as a reference for developers to be able to use the suitable algorithm and can provide accurate house price predictions. Linear Regression algorithm modeling produces a prediction score of 69% with a coefficient of determination (R2) of 0.69 and an RMSE value of 4395785322.216207. Support Vector Regression algorithm makes a prediction score of 97% with a coefficient of determination (R2) of 0.97 and an RMSE value of 31.19812999869066. Polynomial Regression algorithm modeling has a prediction score of 99% with a coefficient of determination (R2) of 0.99 and an RMSE value of 0.000403824405323. Based on these results, it can consider that the modeling of the house price prediction system with Polynomial Regression has the best level of accuracy.

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