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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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Evaluasi Kelayakan User Interface Prototype Aplikasi Mobile “HeartReco” Menggunakan System Usability Scale Aji, Desak Ketut Puri Trisnantya; Suputra, I Putu Gede Hendra
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

Heart disease is a disease that can not be cured completely, so prevention and early management are needed to prevent the disease progression. Thus, dietary modification is an important factor that needs to be done for people with heart disease. HeartReco is a recommendation system based on a mobile application designed to help people with heart disease, families of people with heart disease, and the general public who are interested in a heart disease diet to get recommendations for diet menus that suit the nutritional needs of people with heart disease. To create a good application, we need a good user interface as well. So that the application prototype needs to be evaluated for its feasibility in terms of the user interface by doing usability testing. In this study, the method used is the System Usability Scale (SUS) which gets an average SUS score of 75.33333. The assessment criteria in the Acceptability Ranges include the Acceptable level, the Grade Scale including class B, and the Adjective Ratings including Excellent. So that the application of the "HeartReco" heart disease diet menu recommendation is good/worthy to be developed to the next stage.
Ekstraksi Ciri Pada Pola Ikan Gabus Hias Dengan Metode Grey Level Coocurency Matrix Bhavanta, I Made Adika; Ayu Rahning Putri, Luh Arida
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

Channa dapat menjadi koleksi dan mengisi akuarium di rumah, selain itu nilai ikan gabus hias ini juga tinggi ketika di ekspor. Identifikasi dari ikan ini secara pengelihatan diperlukan pengetahuan dengan memperhatikan pola dan warna dari setiap spesies ikan tersebut Pola dari setiap spesies memiliki ciri tersendiri, namun karena kurangnya pemahaman, informasi, dan ilmu tentang motif ikan ini, masyarakat mengalami kesulitan dalam mengenali dan mengklasifikasi ikan tersebut. Algoritma GLCM merupakan metode dalam pengenalan pola, metode ini merupakan matriks yang terbentuk berdasarkan citra grayscale dan matriks ini menghitung frekuensi kemunculan suatu nilai piksel horizontal terhadap piksel vertikal yang bersebelahan maupun diagonal. Pada penelitian ini menggunakan data citra sebanyak 30 pada 3 jumlah spesies, yaitu channa gachua, channa maruliodes, dan channa micropeltes. Menggunakan beberapa library pada google colabs dan menghasilkan proses ekstraksi fitur dengan derajat 0 dan 45. Hasil dari ekstraksi fitur ini akan digunakan sebagai data untuk pengenalan pola yaitu pada fitur energy, correlation, dan homogenity.
Pengklusteran Data Iris Menggunakan Metode Fuzzy C-Means Yogeswara, Krishna Sankya
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 study focuses on the application of the Fuzzy C-Means method for clustering the Iris dataset. Clustering is a widely used technique for grouping similar data objects together, and the Iris dataset, which consists of measurements of iris flowers, has been a popular choice for clustering analysis. The Fuzzy C-Means algorithm, based on fuzzy logic, allows for a more flexible and nuanced approach to clustering by assigning degrees of membership to data points, capturing the inherent uncertainty and ambiguity in the dataset. By utilizing fuzzy logic, the Fuzzy C-Means method aims to accurately classify iris flowers into distinct clusters based on their petal width, petal length, sepal width, and sepal length. The results of this study contribute to the understanding of fuzzy clustering techniques and their application in pattern recognition and data analysis. Keywords: Iris dataset, clustering, Fuzzy C-Means, fuzzy logic.
Klasifikasi Emosi Lirik Lagu dengan Long Short Term Memory dan Word2Vec Fortunawan, I Putu Diska; ER, Ngurah Agus Sanjaya
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 focuses on the classification of emotions in song lyrics using LSTM (Long Short-Term Memory) and Word2Vec embedding. Emotion classification in lyrics plays a crucial role in music recommendation systems, sentiment analysis, and understanding the affective aspects of music. The study explores the effectiveness of LSTM, a type of recurrent neural network (RNN), in capturing the sequential dependencies and patterns in lyrics, combined with Word2Vec embedding to represent the semantic meaning of words.The dataset consists of a collection of song lyrics labeled with 2 emotions. The lyrics are preprocessed and convertedinto word vectors using the Word2Vec model. The LSTM model is then trained on the preprocessed lyrics data, aiming to predict the corresponding emotion category for a given set of lyrics. Experimental results demonstrate that the proposed approach achieves a maximum accuracy of 72.8% in classifying emotions in song lyrics. The LSTM model leverages the sequential information in the lyrics to capture the emotional context effectively. The Word2Vec embedding enhances the representation of words, allowing the model to understand the semantic relationships between words and better discriminate between different emotional categories. Keywords: TextProcessing, Classification, LSTM, Word2Vec
Pengembangan Model Semantik Ontologi Pada Domain Kendang Bali Yasa, I Ketut Gede Udha Krisna; Dwidasmara, Ida Bagus Gede; Arya Kadyanan, I Gusti Agung 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

Traditional musical instruments are a form of representation of the cultural identity of each region. Traditional musical instruments are created in different forms and functions in each use. The activity of playing traditional musical instruments for the community is one form of art that is born from within. Art itself is one of the cultural heritages, which is better known as regional arts which are passed down from generation to generation. Traditional musical instruments have their own characteristics, such as the example of the Balinese Kendang musical instrument. Balinese kendang is one of the musical instruments that is closely related to the art of karawitan. Kawawitan art in short is the art of sound by processing the sounds of objects or musical instruments (instruments) in a traditional gamelan way. Kendang is played by slap with the palm of the hand. The drum instrument belongs to the middle class, which functions as the leader of a gamelan barungan. In Bali drum instruments are usually played in pairs and individually. If played in pairs, the drums are called drums lanang and wadon. In this digital era, many people, especially Balinese people, are only limited to knowing the term Balinese drums without knowing the types or other meanings contained in them. Along with the times, many people are more interested in modern musical instruments so that traditional musical instruments are slowly being forgotten. The use of ontology as a documentation or representation technique is a solution to this problem. Ontology in the semantic web is a catalog where schemas are created using ontologies. Ontology is needed because it is useful for improving the development of semantic web applications. The ontology for Balinese cultural heritage, Balinese instruments or gamelan especially Balinese Kendang, can be used to document and represent knowledge surrounding the Balinese Kendang domain and also make knowledge about Balinese Kendang not only tacit but explicit.
Analisis Performa Algoritma K-Nearest Neighbor dalam Klasifikasi Tingkat Kerontokan Rambut Widya Prana, Gede Dikka; Astuti, Luh Gede
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

Hair loss can lead to baldness and affect one's self-confidence. Normally, hair falls out at 80-120 strands per day, and the average number of hair follicles on the head is around 100,000. If the amount is reduced by 50%, it can be considered a disorder. Therefore, a classification of hair loss levels is necessary to determine appropriate actions. Previous study has shown that the K-Nearest Neighbor algorithm is capable of classifying various diseases. In this study, the Luke Hair Loss Dataset from the website kaggle.com, consisting of 400 data points, was used. To evaluate the method's feasibility, a confusion matrix was employed. The objective of this research is to analyze the performance of the K-Nearest Neighbor algorithm. Several scenarios were utilized, including testing the model before and after SMOTE oversampling, testing before and after data normalization, testing based on different K values, and testing with varying ratios of training and testing data. The results of this study indicate that the K-Nearest Neighbor algorithm achieved the highest accuracy value of 0.9853, precision of 0.9886, recall of 0.9833, and f1-score of 0.9856. Keywords: Hair Loss, Classification, K-Nearest Neighbor, Performance Test, Confusion Matrix
Evaluasi Desain Aplikasi Delivery Menggunakan Metode System Usability Scale Sidharta, Matthew Novan; Putri, Luh Arida Ayu Rahning
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

Technology continues to develop from time to time and has been widely used to support various forms of services, such as delivery service. However, not every aspect can be fulfilled by this kind of application. The delivery application which is selected by researcher in this paper is disguised. The application under this research will be evaluated in terms of UI/UX design. The usability testing method that will be used in the evaluation process is the system usability scale. The result shows that the system usability scale’s score on the application is at 54,16. To improve the application performance, especially in terms of UI/UX, the application can be redesigned for the next research. Keywords: Delivery, UI/UX Design, Usability Testing, System Usability Scale
Perbandingan Kinerja Local Database Pada Aplikasi Mobile Dengan Flutter Atmojo, Firman Ali Eka; Widiartha, I Made
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

Android is currently a popular mobile operating system used all over the world. There are many programming languages ??that can be used to build android applications, one of which is Flutter. Flutter is a Software Development Kit that supports multi platform apps with the Dart programming language developed by Google. Two database management Android supported systems using Flutter are sqflite and HIve. Flutter SDK provides installable packages to install for developers and implements an application that stores its data in this local database. In this study, it is necessary to reveal a comparison of the performance of these two databases. For this reason, an Android application to be used as a test that includes basic data operations that are widely used in this database is implemented in this study. The test results clearly show that in each test with a drastically increased number of data samples Hive provides better performance than sqflite for each type of basic operation.
Optimasi SVM untuk Klasifikasi Warna: Investigasi Terhadap Pengaruh Fungsi Kernel dan Penyetelan Parameter Wismagatha, Pande Gede Dani; Santiyasa, I Wayan
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

Color plays a crucial role in visual applications such as object recognition, image processing, computer vision, and computer graphics. Support Vector Machine (SVM) algorithms have gained attention for color classification due to their ability to handle complex data. SVM, a machine learning algorithm for classification and regression, aims to find optimal decision boundaries. In color classification using SVM, color data is represented by feature vectors, and SVM learns patterns to classify colors accurately. The SVM algorithm demonstrates a high accuracy rate, with an average accuracy of approximately 85% in color detection. This indicates the SVM's ability to effectively separate and classify colors with precision. SVM is proven to be effective in handling non-linear color data by utilizing kernel functions to transform the feature space into higher dimensions, enabling accurate classification of complex color data. The outstanding performance of the SVM algorithm in color detection presents vast potential applications in color recognition, image processing, computer vision, and computer graphics. SVM offers accurate and reliable solutions for object classification based on color characteristics in various contexts. Keywords: Color classification, Support Vector Machine (SVM), Image processing, Machine Learning
Implementasi Transfer Learning Dalam Klasifikasi Penyakit Pada Daun Teh Menggunakan MobileNetV2 Dwijayana, I Gede Diva; Wibawa, I Gede Arta
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

Indonesia is currently the seventh largest tea producer in the world. However, tea farmers in Indonesia still use old technology and simple farming methods. Limited knowledge of small farmers about diseases that can attack their tea leaves. a tool is needed to classify the types of tea leaf diseases using digital images. This research uses a deep learning approach with Convolutional Neural Network using MobileNetV2 architecture for tea leaf disease classification with digital image data. With the transfer learning method, the MobileNetV2 model is trained with 3 different epochs. The best accuracy is obtained from the model trained with epoch 20 with an accuracy value of 94.6% with a loss value of 0.287. The MobileNetV2 model that has been trained shows good results in classifying tea leaf diseases.

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