cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
I Gede Surya Rahayuda
Contact Email
igedesuryarahayuda@unud.ac.id
Phone
+6289672169911
Journal Mail Official
jnatia@unud.ac.id
Editorial Address
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, 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
 1   2   3   4   5 
Penerapan Multinomial Naïve Bayes dan Chi-Square pada Analisis Sentimen Makan Bergizi Gratis Ni Putu Alya Noviyanti; I Gusti Ngurah Anom Cahyadi Putra
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.p08

Abstract

The Free Nutritious Meal Program (MBG) is a policy initiated by the government in order to create a healthy superior generation. This policy has generated various responses from the public, both positive, negative, and neutral. This study aims to analyze public sentiment towards the program by utilizing 2.165 comments from YouTube. Data were analyzed using the Multinomial Naïve Bayes algorithm after going through the labeling, text preprocessing, and TF-IDF representation stages. Feature selection was carried out with 10 threshold values ​​of the Chi-Square method to evaluate the effect of the number of features on model performance. Hyperparameter tuning with alpha and fit_prior was carried out through Grid Search with 5-fold cross validation. The results showed that the best performance was achieved when the threshold Chi-Square was 100% or when all features were used, which resulted in an accuracy of 83.48% and a macro F1 score of 83.42%. Visualization of the sentiment distribution shows the dominance of negative sentiment at 35.6%, followed by positive at 34.9%, and neutral at 29.5%, indicating public dissatisfaction with the policy.
Analisis Kinerja XGBoost Menggunakan Bayesian Optimization dalam Prediksi Harga Ethereum Christian Valentino; Luh Arida Ayu Rahning Putri
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.p09

Abstract

Cryptocurrency is a digital innovation in the financial sector that has revolutionized the global transaction system through blockchain technology. One of the main challenges in the crypto domain today is determining the price of cryptocurrencies, which are highly volatile. Ethereum, one of the largest cryptocurrencies, exhibits complex volatility patterns that require a robust predictive system. This study aims to compare the performance of the standard XGBoost algorithm with XGBoost optimized using Bayesian Optimization in predicting daily Ethereum prices based on time series data from 2016 to June 2025. The dataset includes price-related features such as open, high, low, volume, and percentage price change. The modeling process consists of several stages including feature engineering, time series-based data splitting, and model training. Model performance was evaluated using three primary metrics: MAE, RMSE, and R² Score. The evaluation results show that the standard XGBoost model achieved an MAE of 80.8926 (3.12%), RMSE of 114.1457 (4.40%), and an R² Score of 0.9723. Meanwhile, the optimized model using Bayesian Optimization achieved an MAE of 70.7241 (2.73%), RMSE of 102.5334 (3.96%), and an R² Score of 0.9777. These results indicate that Bayesian Optimization helps improve the model's prediction accuracy. This study concludes that the XGBoost model with a Bayesian optimization approach yields superior and more effective performance in forecasting Ethereum prices based on time series data.
Klasifikasi URL Berbahaya Menggunakan Algoritma Random Forest Berbasis Fitur Struktural I Gede Putra Wiratama; Anak Agung Istri Ngurah Eka Karyawati
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.p05

Abstract

Phishing attacks remain a critical threat in the digital era, often exploiting deceptive URLs to trick users into divulging sensitive personal information. To address this issue, this study proposes a machine learning-based detection system using the Random Forest algorithm to identify phishing URLs based on structural features. The main objective of this research is to build an efficient and lightweight model that can detect phishing attempts in real-time without relying on third-party databases or content-based analysis. From the dataset used, 10 structural features were selected based on relevance and efficiency, such as the presence of IP addresses, use of HTTPS, domain age, and URL length. The model was trained and tested on a labeled dataset and evaluated using accuracy, confusion matrix, and classification report. The Random Forest model achieved a testing accuracy of 92.72%, with strong precision and recall values for both phishing and legitimate classes. The results indicate that the proposed approach is effective in distinguishing malicious URLs using only structural characteristics, making it a practical solution for enhancing cybersecurity at the URL level.
Identifikasi Kematangan Buah Apel Menggunakan Algoritma YOLO I Gede Liyang Anugrah Oktapian; Gst. Ayu Vida Mastrika Giri
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.p16

Abstract

The classification of fruit ripeness plays a vital role in the agricultural product processing industry. Manual sorting based on visual perception is often subjective and inconsistent. This research proposes an automatic detection and classification system for apple ripeness levels, namely unripe, half ripe, and ripe, using the YOLOv8n object detection algorithm. A dataset of 1,800 apple images was collected and annotated using YOLO format, then trained on a lightweight YOLOv8n model for 30 epochs. The evaluation results showed high performance, with mAP@0.5 of 0.975 and mAP@0.5:0.95 of 0.959. Class-wise, the model achieved F1-scores of 0.94 for unripe, 0.93 for half ripe, and 0.88 for ripe apples. The confusion matrix indicated that most misclassifications occurred between the ripe and half ripe classes, suggesting feature similarity. The model demonstrated accurate and efficient detection, making it suitable for real-time fruit sorting applications. Future work may explore data augmentation, deeper YOLO variants, or integration with IoT devices for deployment in agricultural environments.
Analisis Sentimen Era Kepelatihan Shin Tae Yong Dengan Menggunakan SVM dan SMOTE Putu Wahana MahaYoni; I Gusti Ngurah Anom Cahyadi 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.p12

Abstract

This study explores the application of a combined approach using TF-IDF, SMOTE, and Support Vector Machine (SVM) to address sentiment classification on imbalanced text data. The dataset consists of 3,377 social media reviews categorized into three sentiment classes positive, negative, and neutral. Text features were extracted using TF-IDF, and class imbalance was handled using the SMOTE technique. The SVM model was trained and evaluated, achieving an accuracy of 90.82% and a weighted average F1-score of 0.91. The results demonstrate that the proposed method effectively improves sentiment classification performance, particularly in handling class imbalance.
Klasifikasi Cuaca Berbasis Citra Menggunakan ConvNeXt Bagus Ajie Satria; Ida Ayu Gde Suwiprabayanti Putra
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.p10

Abstract

Weather image classification plays a crucial role in many sectors, such as transportation, marine, and agriculture, where automated weather recognition can support decision-making and safety. This study proposes the use of the ConvNeXt architecture with transfer learning for weather classification using image data. The dataset, sourced from Kaggle, comprises 768 images labeled into three categories: cloudy, rain, and shine. Several preprocessing steps were conducted, including noise filtering, resizing, normalization, and augmentation to enhance model performance. Furthermore, a hyperparameter tuning process was applied using six different combinations of learning rates and batch sizes to identify the most optimal configuration. The ConvNeXt model achieved perfect evaluation scores of 100% on validation sets for two hyperparameter combinations and testing sets for a hyperparameter combinations, outperforming models from previous studies such as InceptionV3 and DenseNet169. The evaluation metrics used were accuracy, precision, recall, F1-score, and confusion matrix. The results demonstrate the model’s robustness and effectiveness in classifying weather conditions based on image data. This study shows that ConvNeXt is a highly capable architecture for visual weather classification tasks.
Identifikasi dan Klasifikasi Suara Vokal Menggunakan Metode Fast Fourier Transform I Ketut Manik Ambarawan; I Gusti Agung Gede Arya Kadyanan
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.p11

Abstract

This research develops a vocal range identification and classification system using frequency spectrum analysis and Fast Fourier Transform (FFT) algorithm. The system addresses the need for an accessible vocal range identification tool for amateur singers and the general public without formal musical training. The proposed system combines real-time audio recording capabilities with audio file processing, implementing pitch identification through FFT analysis and windowing functions. The system features two input methods: real-time recording and audio file upload supporting various formats (MP3, WAV, FLAC, AAC, OGG, M4A). Using PyAudio for real-time processing and Librosa for file analysis, the system accurately identifies fundamental frequencies within the human vocal range (80-1100 Hz) and automatically classifies voice types (Bass, Baritone, Tenor, Alto, Mezzo-soprano, Soprano). Testing demonstrates effective frequency identification with pitch conversion accuracy ranging from 95.7% to 98.8% and voice type classification achieving 81.2% accuracy. The system provides an efficient solution for vocal range analysis with low computational complexity and real-time processing capabilities.
Perancangan UI/UX Aplikasi PLATES untuk Perencanaan Gaya Hidup Sehat I Gede Adrian Satria Pratama S.; I Gusti Agung Gede Arya Kadyanan; Putu Praba Santika
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.p13

Abstract

Public awareness of healthy living in Indonesia continues to grow, yet many still struggle to maintain healthy habits consistently. According to UNICEF (2022), Indonesia's adult obesity rate went from 14.8% in 2013 to 21.8% in 2018. caused by poor diet, physical inactivity, and lack of accessible, personalized nutritional information. To address this, PLATES (Personal Lifestyle Assistant for Tracking Eating and Sustainability) was developed as a mobile application that supports users in maintaining a healthy lifestyle by applying the Design Thinking methodology. Features include a calorie calculator, personalized meal plans, screen time reminders, health challenges, and NutriCamera for scanning food intake. Usability testing with 25 respondents aged 18 to over 51 years yielded an average SUS score of 78.3, indicating good usability across diverse user groups. The findings demonstrate that PLATES can support behavior change and health monitoring effectively. Aiming to guarantee healthy lives and advance well-being for everyone at all ages, the Sustainable Development Goal (SDG) number 3 is in line with this answer.
Sistem Rekognisi Akor Instrumen Musik SecaraOtomatis Menggunakan PCP dan SVM Gede Nicholas Tejasukmana Putra; Ngurah Agus Sanjaya ER
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 study presents a system for automatic chord recognition from audio recordings using the Pitch Class Profile (PCP) and Support Vector Machine (SVM). PCP was chosen as the primary feature extraction method because it can represent the standard 12 pitch classes in music accurately. SVM was selected as the classification model because of its proven success in previous chord recognition studies, offering high accuracy while remaining efficient. Using the Piano Triads Wavset dataset, which contains 432 triad chords across 12 root notes and three chord types such as major, minor, and diminished, the model was trained and tested in an experiment. The audio data were processed to extract PCP features and normalized before being classified using SVM. Evaluation was carried out using both a default SVM configuration and GridSearchCV optimization. Results show that the optimized model achieved up to 82% accuracy across all chord classes, indicating that the proposed approach can recognize chords reliably even without using deep learning or additional features. The final system also includes real-time prediction by user audio input, using Python and streamlit framework.
Klasifikasi Genre Buku Berdasarkan Sinopsis Menggunakan Naïve Bayes dan Logistic Regression Anak Agung Anom Witaradiani; I Gede Arta Wibawa; Putu Praba Santika
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.p13

Abstract

Genre is an important element in book categorization based on specific content characteristics or themes. However, manual classification processes are no longer efficient due to the increasing volume of literature. This study aims to compare the performance of Naïve Bayes and Logistic Regression algorithms in book genre classification based on synopses. The dataset used is secondary data obtained from Kaggle. The dataset consists of 4,535 samples after the preprocessing stage, with feature representation using the TF-IDF method. To address class distribution imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The experimental results show that Logistic Regression achieved the best performance with 75.19% accuracy and 75.16% F1-score, while Naïve Bayes achieved 72.22% accuracy and 72.11% F1-score. Based on this evaluation, Logistic Regression is considered more effective in classifying book genres from synopsis text.

Page 2 of 5 | Total Record : 49

 1   2   3   4   5 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 4 No. 1 (2025): JNATIA Vol. 4, No. 1, November 2025 Vol. 3 No. 4 (2025): JNATIA Vol. 3, No. 4, Agustus 2025