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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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Analisis Sentimen Ulasan Aplikasi Citilink Menggunakan Metode Support Vector Machine dengan TF-IDF David Brave Moarota Zebua; Ida Bagus Gede Dwidasmara
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 2 (2025): JNATIA Vol. 3, No. 2, Februari 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.i02.p24

Abstract

In line with the advancement of the Industry 4.0 era, Indonesian society has been living side by side and is inseparable from the existing technological advancements. One of the conveniences experienced by today's society is that transactions no longer need to be conducted face-to-face in a particular place but can now be done online. In the context of air transportation, technological advancements have been very helpful to the public. Airline applications are one of the most widely used by passengers. In this study, the researchers focused on analyzing public sentiment towards the Citilink application, one of Indonesia's leading airlines. The researchers used the Support Vector Machine (SVM) method enhanced with TF-IDF (Term Frequency-Inverse Document Frequency) text representation to analyze sentiment from user reviews. The stages of this research began with data collection containing reviews from the Citilink application to analyze its sentiment. Then, it proceeded to the data preprocessing stage, where the collected data was cleaned until it became tokens ready for testing. After that, it moved to the weighting stage using Term Frequency-Inverse Document Frequency (TF-IDF). Then it continued to the stage of applying the Support Vector Machine (SVM) model. The last one is the evaluation to measure the accuracy level of the model used. Based on the results of this study, it can be concluded that the Support Vector Machine model that has been adapted to the dataset of Citilink application reviews from Google Playstore and supported by TF-IDF feature extraction successfully classified the sentiment of reviews with high accuracy, reaching 88%. Further evaluation also showed satisfactory values of precision, recall, and F1-Score, namely 90%, 83%, and 85%, respectively. This study shows that the Support Vector Machine model can be an effective instrument in understanding user responses to the performance of the Citilink application. 
Implementasi Ekstraksi Fitur VGG-16 dan Pemodelan LSTM untuk Pembangkitan Caption Gambar Otomatis Made Pranajaya Dibyacita; Luh Gede Astuti
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 2 (2025): JNATIA Vol. 3, No. 2, Februari 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.i02.p25

Abstract

Image captioning, the task of automatically generating descriptive captions for images, has gained significant attention due to its potential applications in various domains. This paper addresses the challenges associated with integrating computer vision and natural language processing techniques to develop an effective image caption generator. The proposed solution leverages the VGG-16 model for feature extraction from images and an LSTM (Long Short-Term Memory) model for caption generation. The Flickr8k dataset, containing approximately 8000 images with five different captions per image, is utilized for training and evaluation. The methodology encompasses several steps, including data preprocessing, feature extraction, model training, and evaluation. Data preprocessing involves cleaning captions by removing punctuations, single characters, and numerical values, while incorporating start and end sequences. Image features are extracted using the pre-trained VGG-16 model, and similar images are clustered to ensure accurate feature extraction. Subsequently, the captions and corresponding image features are merged and tokenized for model training. The LSTM model is designed with input layers for image features and captions, as well as an output layer for caption generation. Extensive hyperparameter tuning is conducted to optimize the model's performance, involving variations in the number of nodes and layers. The generated captions are evaluated using BLEU scores, where a score closer to 1 indicates higher similarity between predicted and actual captions. The proposed system demonstrates promising results in generating meaningful captions for images, with potential applications in assisting visually impaired individuals, medical image analysis, and advertising industry automation. 
Implementasi Internet of Things dengan Smart Faucet pada Sistem Irigasi Subak Bali Ida Bagus Rahadi Putra; Ngurah Agus Sanjaya ER
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 2 (2025): JNATIA Vol. 3, No. 2, Februari 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.i02.p26

Abstract

This research proposes the implementation of the internet of things with smart faucets in Bali's Subak irrigation system by using algorithms used to implement the internet of things. Sensors including temperature sensors, soil moisture sensors, rainfall sensors, and water level sensors, are installed to monitor real-time environmental and crop conditions. The data collected from these sensors is used in algorithms that are used to optimize water usage according to crop needs and environmental conditions. The results of this study show that there is a reduction in water wastage and an increase in water use efficiency with more accurate control. The optimized irrigation system ensures a water supply that matches the needs of the plants, improves plant growth, and reduces the risk of disease. Users can monitor and control the irrigation system remotely through the website, providing accurate information about the condition of the plants and their water needs. It is an innovative solution to improve crop yields, plant health, and water use efficiency in Bali's Subak irrigation systems. 
Rancang Model Ontologi Representasi Pengetahuan Kuliner Tradisional Kabupaten Bangli, Bali Celia Maureen Chandra; Agus Muliantara
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 1 (2024): JNATIA Vol. 3, No. 1, November 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.v03.i01.p01

Abstract

The Province of Bali is a preferred tourist destination in Indonesia, including one of the developing tourist areas, Bangli Regency. The tourism development in Bangli is closely linked to traditional cuisine as a key attraction. However, in preserving the traditional cuisine, it is evident that many residents lack in-depth knowledge about the variety of Bangli’s traditional dishes. This research aims to develop an ontology model representing the traditional culinary knowledge of Bangli Regency using Methontology method. The implementation, conducted using Protégé software, resulted in 6 classes, 8 object properties, 5 data properties, and 55 individuals. The ontology was evaluated using the Ontology Quality Analysis (OntoQA) method with schema metrics techniques, including RR, IR, and AR measurements. The evaluation revealed that the ontology has a high diversity of information and specific information representation, though the amount of information provided is still limited. With these promising results, the constructed ontology is expected to effectively represent the knowledge of traditional Bangli cuisine and serve as a foundation for developing an information system that facilitates access to this knowledge, thereby supporting its preservation. 
Analisis Dimensi Gambar Terhadap Klasifikasi Batik Indonesia dengan CNN Ida Bagus Gde Ardita Mahaprawira; Agus Muliantara
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 1 (2024): JNATIA Vol. 3, No. 1, November 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.v03.i01.p02

Abstract

Batik motif classification has gained significant attention due to its cultural significance and practical applications in various fields. This study explores the impact of image dimensions on the classification of batik motifs using Convolutional Neural Networks (CNN). The research investigates how variations in image dimensions affect the accuracy and robustness of CNNbased classification models. Through experimentation with different image resolutions and aspect ratios, the study aims to identify optimal settings for achieving high classification performance. Additionally, it examines the computational efficiency of CNN models under varying image dimensions. The findings contribute to enhancing the understanding of image preprocessing techniques and model optimization strategies for batik motif classification tasks. 
Enkripsi Resep Dokter untuk Meminimalisir Penyalahgunaan Obat Menggunakan Algoritma AES Mode CBC Komang Wahyu Agastya; Anak Agung Istri Ngurah Eka Karyawati
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 1 (2024): JNATIA Vol. 3, No. 1, November 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.v03.i01.p03

Abstract

Patient safety has always been a top priority, and medication error is a significant concern in this domain. Prescriptions are a crucial factor that can elevate the risk of medication errors. A study by Prof. Dr. Nurul Idrus involving 1,200 drug users revealed that a substantial number of respondents misused prescriptions and experimented with obtained medications to substitute for those they couldn't easily access. To address this issue and enhance patient safety, we propose utilizing the AES encryption algorithm to safeguard the confidentiality of prescriptions. AES encryption offers robust protection against unauthorized access and data breaches. Furthermore, employing the CBC (Cipher Block Chaining) mode provides an additional layer of security. In CBC mode, each block of the message is encrypted not only with the encryption key but also with the ciphertext of the previous block, resulting in a unique encrypted message even for identical plaintexts. This combination of AES encryption and CBC mode effectively safeguards prescription data, minimizing the potential for prescription misuse and medication errors, ultimately contributing to improved patient safety. 
Analisis Penggunaan Logika Fuzzy Mamdani dan Sugeno untuk Memprediksi Shade Foundation Aprinia Salsabila Roiqoh; Hanin Fatma Soraya; Dela Ayu Putri Mayona; Nabila Anggita Luna; Anggraini Puspita Sari
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 1 (2024): JNATIA Vol. 3, No. 1, November 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.v03.i01.p04

Abstract

The magnificence industry has developed quickly in later decades, expanding request for items that meet an assortment of shopper needs. One imperative angle in choosing magnificence items is finding an establishment color that suits your skin color. This investigates points to analyze the utilize of fluffy rationale, particularly the Mamdani and Sugeno strategy, in foreseeing the correct establishment shade based on varieties in skin color and suggestion. Definition of membership functions, definition of fuzzy rules, fuzzy interference and defuzzification are used as research methods. The data used was obtained from a random experiment by entering skin tone and undertone values into the program. Research results show that the shade of the foundation is greatly influenced by the color and undertone of the skin. Although there is a significant difference between the Mamdan and Sugeno method values, the final predicted base colors are not significantly different.This research strengthens the position of fuzzy logic as an effective method in improving the quality of products and services in the beauty industry, as well as solving the complex problem of determining the appropriate foundation shade for various skin types. It is hoped that the results of this research will make it easier for consumers and beauty professionals to choose the right foundation shade according to individual needs and preferences. 
Segmentasi Pengguna Spotify Berdasarkan Preferensi Musik dengan Algoritma K-Means Clustering Kadek Bisma Dharmasena; Cokorda Pramartha
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 1 (2024): JNATIA Vol. 3, No. 1, November 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.v03.i01.p05

Abstract

Music streaming platforms like Spotify have become integral to the daily lives of millions globally, offering personalized listening experiences. However, managing a vast music catalog to present relevant content to each user remains a challenge. This study explores the application of the Kmeans clustering algorithm to segment Spotify users based on their music preferences. The goal is to group users into clusters with similar tastes to enhance targeted marketing and user engagement. We utilized a secondary dataset of trending Spotify songs and their attributes from 2023. Through data preprocessing, feature selection, and normalization, we prepared the data for clustering. The optimal number of clusters was determined using the Elbow Method, resulting in six distinct clusters. Each cluster represents unique music preferences, analyzed through metrics such as danceability, energy, and popularity. The findings demonstrate that K-means clustering effectively identifies user segments, providing insights for improving personalized recommendations and marketing strategies. This research underscores the potential of machine learning in optimizing user experiences on music streaming platforms. 
Pengujian Prototipe Sistem Jasa Pengembangan Aplikasi Menggunakan Metode System Usability Scale I Kadek Agus Wijaya Kusuma; Gst. Ayu Vida Mastrika Giri
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 1 (2024): JNATIA Vol. 3, No. 1, November 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.v03.i01.p06

Abstract

The utilization of websites to spread information related to services is commonly used by service providers, including those in application development services. In developing a website, designing an interactive interface becomes an important aspect. This has an impact on the effectiveness and efficiency of the website, as well as the user experience. Therefore, the participation of users in designing the website, considering their needs and issues they face is essential. To ensure the website meets user’s standards and expectations, usability testing is conducted on the website prototype. This research uses the System Usability Scale (SUS) method. The SUS scores from the testing serve as a benchmark to evaluate the effectiveness, efficiency, and optimization of the user experience of the website prototype. To achieve high SUS scores, the website prototype must be capable of providing effective and efficient solutions to user’s issues while also supplying their needs. A website prototype with high scores can be considered to have met user’s standards and expectations, therefore it is ready for implementation. 
Klasifikasi Tingkat Keparahan Kecelakaan Lalu Lintas Menggunakan Random Forest Classifier I Gusti Ngurah Bagus Lanang Purbhawa; I Gede Arta Wibawa
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 1 (2024): JNATIA Vol. 3, No. 1, November 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.v03.i01.p07

Abstract

Traffic accidents are a common problem that often occurs. Many factors cause and determine the severity of traffic accidents. These factors can include road conditions, weather, light conditions, driver age, and the cause of the accident. In this study, researchers will try to apply the Random Forest method to classify the severity of traffic accidents. The Random Forest method was chosen because of its excellent ability to handle high-dimensional data and tolerance for overfitting. The dataset used in this research was taken from Kaggle, consisting of 12316 records and 32 features covering various attributes related to traffic accidents. Before applying random forest, it is necessary to carry out a preprocessing stage on the dataset to remove irrelevant features, fill in empty values and divide the data into training and testing data. The results of this research show that Random Forest can produce a good level of in classifying the severity of traffic accidents with 92% accuracy. This shows the potential of this method as a useful tool in the analysis and prediction of traffic accidents. Therefore, this research makes a significant contribution to efforts to improve road safety. 

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