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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 49 Documents
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Implementasi Sistem Kriptografi Hybrid RSA dan DES untuk Pengamanan Data Teks Putu Ayu Wulan Satya Dewi; I Gede Santi Astawa
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 1 (2025): JNATIA Vol. 4, No. 1, November 2025
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.2025.v04.i01.p06

Abstract

Data security is a crucial aspect in the digital age. Symmetric algorithms such as the Data Encryption Standard (DES) offer high-speed encryption, but have fundamental weaknesses in terms of secure key distribution. On the other hand, asymmetric cryptography such as RSA can overcome key distribution issues, but is computationally inefficient for encrypting large volumes of data. To address these challenges, this article demonstrates the implementation of a hybrid cryptographic system that integrates the strengths of both algorithms. This system leverages the RSA algorithm, which is based on principles of number theory such as modular arithmetic and the difficulty of prime factorization, to securely encrypt and distribute DES session keys. Subsequently, the DES algorithm is used to quickly and efficiently encrypt textual data. Through an explanation of the mathematical processes and programmed implementation, this research demonstrates that the hybrid approach can both secure and restore plaintext data intact.
Pengenalan Tulisan Tangan Aksara Bali Menggunakan Algoritma CRNN I Kadek Bisma Yoga; Cokorda Pramartha
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 4 (2025): JNATIA Vol. 3, No. 4, Agustus 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

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

Abstract

This research proposes the development of a handwritten Balinese script recognition system using the Convolutional Recurrent Neural Network (CRNN) algorithm as a vital step in preserving this intangible cultural heritage from the threat of obsolescence in the digital era. Given the inherent complexity and variability of handwritten Balinese script, which distinguishes it from printed text, a deep learning approach is essential. CRNN was chosen for its ability to integrate the strengths of CNN in extracting spatial visual features with the power of RNN (specifically BiLSTM) in modeling sequential dependencies. Primary handwritten data was meticulously validated by a Balinese language expert, then processed through grayscale conversion, pixel normalization, and resizing for standardization. The model was constructed with convolutional layers, recurrent BiLSTM layers, and a Connectionist Temporal Classification (CTC) transcription layer, which is effective in translating sequential features into character labels. Performance evaluation of the model using the Character Error Rate (CER) on separate test data showed an average accuracy of 89.9%. These results significantly affirm the great potential of CRNN in supporting the digitalization efforts of Balinese script, as well as facilitating its broader integration into modern environments.
Analisis Klasifikasi Tweet Berdasarkan Topik Sosial Menggunakan SVM Abdurrazik; I Made Widhi Wirawan
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 4 (2025): JNATIA Vol. 3, No. 4, Agustus 2025
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.2025.v03.i04.p15

Abstract

Social media platforms, including Twitter (now X), produce a constant flow of user-generated text that reflects public discourse in real time. However, the informal and unstructured nature of these short messages poses challenges for manual topic classification, especially when handling large volumes. This study aims to categorize Indonesian-language tweets into three topics: Politics, Entertainment, and Others, using a supervised machine learning approach. A total of 1,478 tweets were collected through keyword-based scraping and manually labeled according to predefined guidelines. The preprocessing stage included text cleaning, tokenization, stopword removal, stemming, and label encoding. TF-IDF was employed to convert the cleaned text into numerical features, while classification was performed using the Support Vector Machine (SVM) algorithm with a One-vs-Rest strategy for multi-class classification. The model reached an overall accuracy of 84 percent, with particularly high performance in the Politics and Entertainment categories. These results indicate that the combination of TF-IDF and SVM is effective for classifying short Indonesian-language tweets and can be applied to support the organization and filtering of topical content in social media analytics.
Perancangan Model Ontologi Domain Tanaman Upakara Hutan Yadnya Monkey Forest PadangTegal I Kadek Dwika Pradnyana; I Putu Gede Hendra Suputra
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 4 (2025): JNATIA Vol. 3, No. 4, Agustus 2025
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.2025.v03.i04.p16

Abstract

Upakara, which is frequently constructed from a variety of materials and given with symbolic roles and profound religious philosophical connotations, is an essential tool in Balinese Hindu religious events. "Tanaman Upakara" refers to plants that are offered. The kinds of upakara plants and their ceremonial applications are likewise unknown to most Balinese Hindus. Additionally, traditional, unstructured data on upakara flora is still recorded in the Yadnya Forest of Monkey Forest Padangtegal, a conservation site. This study uses ontology technology to try to solve these problems. As a formal representation of knowledge, ontology can improve data management effectiveness and information system interoperability. The Methontology method was used to create the ontology model for the Upakara Plant domain in the Yadnya Forest of Monkey Forest PadangTegal. The developed ontology model includes 17 classes, 8 object properties, 2 data properties, and 282 individuals. Evaluation was performed using SPARQL queries to test the ontology's correctness and functionality.
Analisis Sentimen Pengguna Terhadap Ulasan Aplikasi Duolingo Menggunakan Metode Logistic Regression Made Dinda Radityaswari; Luh Gede Astuti
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 1 (2025): JNATIA Vol. 4, No. 1, November 2025
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.2025.v04.i01.p07

Abstract

The ability to speak foreign languages, especially English, has become an important skill in the era of globalization and digitalization. However, according to the EF English Proficiency Index 2024, Indonesia ranks 80th out of 116 countries. One of the widely used solutions is the Duolingo application, a gamified language learning platform that has been downloaded over 500 million times. This research aims to analyze user sentiment toward the Duolingo application through reviews on Google Play Store using logistic regression. The data used consists of 8.648 reviews that have been labeled as positive and negative sentiment. The research process includes the stages of data preprocessing, dividing data into test and training data, weighting using TF-IDF, and classification using Logistic Regression algorithm with the parameter class_weight='balanced' to handle class imbalance, and evaluation using a confusion matrix. The evaluation results show that the model can achieve an accuracy of 89.83%, with a precision value of 73.91%, recall of 88.18%, and f1-score of 78.49%. This research shows that Logistic Regression with TF-IDF weighting is effective in sentiment analysis.
Analisis Sentimen Ulasan Aplikasi dengan Multinomial Naïve Bayes, Logistic Regression, dan SVM Rahelita Pasaribu; Ida Ayu Gde Suwiprabayanti Putra
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 1 (2025): JNATIA Vol. 4, No. 1, November 2025
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.2025.v04.i01.p08

Abstract

The swift uptake of mobile health applications has led to an increase in user-generated feedback, providing important insights into public satisfaction. To explore user sentiments, this study analyzes 9,848 reviews from a health-oriented application utilizing three machine learning methods: Multinomial Naïve Bayes, Logistic Regression, and Support Vector Machine (SVM). The feedbacks were classified as positive or negative. The methodology included standard preprocessing such as cleaning and stemming, feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF), and addressing class imbalance with the Synthetic Minority Oversampling Technique (SMOTE). Models were fine-tuned and verified through 5-fold cross-validation. Effectiveness was measured by accuracy, precision, recall, and F1-score. Logistic Regression and SVM reached the greatest accuracy at 92%, while Naïve Bayes trailed at 86%. Logistic Regression showed strong precision (95%) and recall (94%) for positive reviews, with SVM performing comparably. These results emphasize the capability of sentiment analysis in enhancing digital health services through information-based user feedback.
Optimasi Model Gaussian Mixture Model (GMM) untuk Klasifikasi Genre Musik Berbasis Mel-Frequency Cepstral Coefficients (MFCC) Maria Dorteah Rumpumbo; I Made Widhi Wirawan
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 1 (2025): JNATIA Vol. 4, No. 1, November 2025
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.2025.v04.i01.p17

Abstract

Music genre classification is an increasingly relevant field as the number of digital music collections increases. The main challenge in this classification is to effectively capture the acoustic characteristics of different genres. This research proposes an optimization of the Gaussian Mixture Model (GMM) model to improve the accuracy of music genre classification using the Mel-Frequency Cepstral Coefficients (MFCC) feature. The dataset used covers various genres such as rock, classical, and jazz. The feature extraction process is carried out through MFCC and continued by training the GMM model with an optimized number of components. The test results show that the combination of MFCC and optimized GMM is able to improve the classification performance compared to conventional approaches. This study contributes to the development of an efficient machine learning-based music classification system.
Analisis Sentimen Ulasan Aplikasi Loklok Menggunakan Metode Support Vector Machine (SVM) I Gusti Ngurah Adhiwangsa Devananda; Luh Arida Ayu Rahning Putri; I Komang Arya Ganda Wiguna
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 1 (2025): JNATIA Vol. 4, No. 1, November 2025
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.2025.v04.i01.p09

Abstract

Rapid advances in digital technology have led to an increase in the amount of text data available online, including user reviews of mobile applications. The Loklok application, as a popular entertainment platform, is one source of review data that is rich in user opinions. This research focuses on performing sentiment analysis on user reviews of the Loklok application by employing the Support Vector Machine (SVM) algorithm alongside the Term Frequency-Inverse Document Frequency (TF-IDF) method for feature extraction. The review dataset was sourced from the Kaggle platform and underwent several text preprocessing steps, including data cleaning, tokenization, stopword elimination, and stemming. The evaluation results indicate that the SVM model, combined with TF-IDF, achieved an accuracy of 86%, a precision of 88%, a recall of 86%, and an F1-score of 87%. Classification performance tends to be better for positive sentiment classes compared to negative ones, due to data imbalance. This finding demonstrates that the combination of TF-IDF and SVM methods is effective in classifying user review sentiment and can serve as a foundation for decision-making in the development of digital applications.
Rancang Bangun Sistem Informasi Pemilihan Fasilitas Kesehatan Berbasis PCA dan Fuzzy AHP Aditya Chandra Nugraha; I Wayan Supriana; I Made Satria Bimantara
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 4 (2025): JNATIA Vol. 3, No. 4, Agustus 2025
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.2025.v03.i04.p17

Abstract

This paper discusses a proposed development process of an information system for the placement of health facilities in Bali by utilizing a fuzzy analytical hierarchy process and principal component analysis to reduce the dimensionality of the various datasets used in the decision making process. Fuzzy AHP is used to determine the weights of nine distinct criteria, including distance to population centers, service quality, infrastructure availability, population density, and healthcare accessibility, among others. Principal Component Analysis (PCA) helps in extracting the most influential variables from socio-economic, geospatial, and satellite-based datasets, simplifying the input without significant loss of information. The system is built using the Laravel framework and offers functionalities for both users seeking facility recommendations and authorities proposing new locations. Manual Fuzzy AHP calculations showed a consistency index of 0.23, indicating the need for improved consistency in pairwise comparisons. Overall, the system successfully automates the recommendation process, combining expert judgment and data-driven analysis to support equitable and effective placement of health services.
Optimasi Metode Support Vector Machine (SVM) Mengunakan Particle Swarm Optimization pada Permasalahan Klasifikasi Diabetes Anak Agung Gde Agung Pranandita; I Made Widiartha
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 4 (2025): JNATIA Vol. 3, No. 4, Agustus 2025
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.2025.v03.i04.p18

Abstract

Diabetes mellitus is a chronic disease that requires accurate early detection. This study presents a diabetes classification system by integrating Support Vector Machine (SVM) with Particle Swarm Optimization (PSO) to automatically optimize model parameters. The dataset used was obtained from Kaggle, consisting of 100,000 entries and nine medical attributes. Data preprocessing included cleaning, encoding, Min-Max normalization, and undersampling to balance class distribution. Model performance was evaluated using 5-Fold Cross Validation. The results showed that the SVM- PSO achieved an average accuracy of 83.60% which is higher than the conventional SVM with 83.39% accuracy. These findings demonstrate that PSO effectively enhances the classification performance of SVM and is recommended for machine learning-based medical diagnosis, especially in diabetes prediction.

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