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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, 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 339 Documents
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Perancangan Sistem Monitoring Suhu dan Kelembaban Berbasis IoT untuk Pertanian Cerdas I Komang Jay Yogi Ram; I Gede Surya Rahayuda
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 3 (2026): JNATIA Vol. 4, No. 3, Mei 2026
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.2026.v04.i03.p05

Abstract

The agricultural sector plays a crucial role in ensuring food security, yet it faces numerous challenges such as climate change, inefficient resource usage, and limited access to real-time data. This study proposes the design and implementation of a temperature and humidity monitoring system based on the Internet of Things (IoT) to support smart farming practices. The system uses environmental sensors connected to a microcontroller, which transmits real-time data to a cloud-based platform accessible via a web or mobile interface. By continuously monitoring environmental conditions, farmers can make more informed decisions regarding irrigation, fertilization, and pest control. The goal of this project is to increase efficiency, reduce waste, and enhance crop yields through data-driven agriculture. Testing results show that the system operates reliably in field conditions and provides accurate environmental monitoring, making it a valuable tool for modern agriculture.
Rancang Bangun Sistem Keamanan Benda Pajangan dengan Notifikasi Telegram A. A. Jaya Kesuma Wardana; I Gede Santi Astawa; I Putu Satwika
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 3 (2026): JNATIA Vol. 4, No. 3, Mei 2026
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.2026.v04.i03.p06

Abstract

This paper presents a security monitoring system designed for high-value museum and gallery display items such as statues, paintings, and pottery, which are susceptible to theft due to their aesthetic, historical, and economic worth. The design integrates an ESP32 microcontroller with an HC-SR04 ultrasonic sensor to detect object movement or removal. Upon detecting unauthorised movement or disappearance, the system dispatches a real-time notification via the Telegram application to alert the user. This study consists of problem identification, literature review, system architecture design, prototype implementation, and thorough testing. Experimental results demonstrate the system's effectiveness in detecting object displacements and absences, with notification latency under two seconds and successful message delivery across all test scenarios. These findings demonstrate a practical security measure for art and historical collections
Perancangan Ontologi Produk Fashion Lokal Sebagai Representasi Pengetahuan Pakaian Wanita Putu Athalia Reyna Sanjaya; I Made Widhi Wirawan
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 3 (2026): JNATIA Vol. 4, No. 3, Mei 2026
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.2026.v04.i03.p07

Abstract

Local Fashion Industry in Indonesia has experienced a rapid growth, especially in the women’s clothing segment. Nonetheless, challenges related to data standardization and product information connectivity obstruct visibility and effective product search. To address these issues, this study aims to design an ontology for local women’s fashion products using the Methontology approach and Semantic Web technologies. The ontology represents key entities such as fashion products, brands, sizes, materials, and usage categories, along with the structured relationships among them. The development process utilizes Protégé and Web Ontology Language (OWL), and is validated using SPARQL queries to explore semantic knowledge. The results show that the developed ontology successfully organizes knowledge of local fashion products in a systematic and queryable structure. This ontology is expected to serve as a foundation for semantic search systems and product recommendation platforms to promote and strengthen the presence of Indonesian local fashion brands.
Representasi Pengetahuan Produk Sampo Melalui Model Ontologi Sebagai Dasar Sistem Rekomendasi Ni Kadek Risma Yudyadnyani; I Gede Santi Astawa
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 3 (2026): JNATIA Vol. 4, No. 3, Mei 2026
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.2026.v04.i03.p08

Abstract

Shampoo is one of the most commonly used hair care products, offering various formulations tailored to specific hair and scalp needs. However, users often face challenges in selecting suitable products, especially when dealing with individual conditions such as dandruff, hair loss, or sensitive scalp. This research aims to develop a domain ontology that models knowledge about shampoo products using the Methontology methodology. The ontology was built using Protégé software and consists of 6 main classes, 9 object properties, 3 data properties, and 48 individuals. It represents key aspects such as product brand, active ingredients, usage effects, hair types, and potential incompatibilities. Evaluation was conducted through SPARQL queries to test the accuracy and usefulness of the ontology in supporting semantic search and personalized recommendations. The results demonstrate that the ontology effectively captures relevant knowledge and can serve as a foundation for intelligent recommendation systems, helping users find appropriate shampoo products based on their needs.
Analisis Sentimen Berbasis Aspek dengan LDA dan IndoBERT pada Ulasan Aplikasi Stockbit Dewa Made Sutha Raditya Mahattama; Gst Ayu Vida Mastrika Giri
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 3 (2026): JNATIA Vol. 4, No. 3, Mei 2026
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.2026.v04.i03.p09

Abstract

This study aims to analyze sentiment in user reviews of the Stockbit application using a topic modeling approach combined with IndoBERT-based sentiment classification. Aspect extraction was carried out using Latent Dirichlet Allocation (LDA), and the experimental results indicate that selecting five topics (n_components = 5) provides the most optimal representation, as evidenced by a topic coherence score of 0.6191. These five topics reflect semantic structures that are highly relevant to the content of the reviews. For the sentiment classification stage, the IndoBERT-base model achieved an accuracy of 90.86%. The best performance was observed for the positive class, with an F1-score of 93.73%, while the negative class yielded an F1-score of 83.12%. This performance gap is attributed to the imbalanced data distribution, where positive sentiments are more dominant. Nevertheless, the macro-average F1-score of 88.43% demonstrates that the model is still capable of classifying both classes in a relatively balanced manner.
Implementasi Model Ontologi dalam Representasi Pengetahuan Anjing Ras Menggunakan Metode Methontology Anak Agung Gede Angga Putra Wibawa; I Ketut Gede Suhartana
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 3 (2026): JNATIA Vol. 4, No. 3, Mei 2026
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.2026.v04.i03.p10

Abstract

One of the animals closest to humans is a dog. This animal is widely kept because it is one of the smartest animals and can function as a stress reliever and provide comfort for its owner. Dog breeds are very diverse, with each breed having different characteristics. The large population of dog breeds sometimes makes people confused in choosing the right breed according to their personality and needs. Therefore, this study aims to build a knowledge structure that provides structured information about purebred dog breeds. Ontology is used as an approach to designing knowledge structures. This study focuses on the implementation of ontology using methontology that can describe each design stage in detail. The results of the study include 10 classes, 16 object properties, 8 data properties, and 105 individuals using protégé. Testing was carried out using OntoQA to measure quality and SPARQL Query to measure the accuracy of the answers. The results showed that the ontology model had good quality and was able to provide correct answers to the questions being tested.
Analisis Trade-off Pendekatan Greedy dan Metaheuristic dalam Seleksi Fitur Terhadap Model Ensemble Anak Agung Gede Ngurah Ananda Wirasena; I Wayan Supriana; I Made Satria Bimantara
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 3 (2026): JNATIA Vol. 4, No. 3, Mei 2026
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.2026.v04.i03.p11

Abstract

The increasing volume and dimensionality of medical data pose challenges for effective machine learning model development. Feature selection techniques (FST) are crucial for improving model performance, computational efficiency, and interpretability. This study analyzes the trade-off between greedy and metaheuristic FST approaches in optimizing Decision Tree-based ensemble models. We compare Mutual Information-Sequential Backward Selection (MI-SBS) as a greedy method and Binary Grey Wolf Optimization (BGWO) as a metaheuristic method. FST fitness is evaluated using a Decision Tree Classifier with 5-fold cross-validation. Final classification performance is assessed using AdaBoost and XGBoost on three distinct medical datasets. Results indicate that MI-SBS offers faster feature selection and stable accuracy, often outperforming the baseline. BGWO, while slower in selection, achieves greater feature reduction, leading to significantly faster final model training at the cost of a minor accuracy decrease. This research provides insights into selecting appropriate FST based on desired trade-offs between computational efficiency and classification accuracy in health informatics.
Klasifikasi Sub-Genre Musik Dangdut Menggunakan Jaringan Saraf Tiruan Long Short-Term Memory Jevan Bernard Kaloko; I Made Widiartha
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 3 (2026): JNATIA Vol. 4, No. 3, Mei 2026
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.2026.v04.i03.p12

Abstract

The automatic classification of Dangdut music sub-genres (Klasik, Koplo, and Campursari) presents a significant challenge in the field of Music Information Retrieval (MIR) due to their overlapping yet distinct musical characteristics. This research proposes a classification system based on a Recurrent Neural Network (RNN) with a Long Short-Term Memory (LSTM) architecture to address this problem. The model is trained using Mel-Frequency Cepstral Coefficients (MFCC) audio features to represent the spectral and timbral characteristics of each sub-genre. The LSTM architecture was chosen for its superior ability to learn temporal dependencies from the sequence of MFCC features. By modeling the evolution of timbre over time, the system can recognize the distinctive patterns that differentiate between Dangdut Klasik, Koplo, and Campursari. The proposed system aims to provide an accurate and efficient classification method, contributing to practical applications such as music recommendation and digital archiving.
Klasifikasi Nuansa Emosi Film Berdasarkan Sinopsis Menggunakan Logistic Regression Skye Kanahaya Endrawan; Cokorda Pramartha
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 3 (2026): JNATIA Vol. 4, No. 3, Mei 2026
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.2026.v04.i03.p13

Abstract

With the rapid development of digital era, movie industry has consider to be a dominant form of entertainment. However, with thousands of movies released every year, audiences often face difficulties selecting movies based on their emotional preferences. This study proposes a classification approach to determine the emotional nuance of movies (happy, sad, tense) based on their synopsis. The dataset used in this study was sourced from Kaggle with 676.491 entries and labeled using a DistilBERT pre-trained emotion detection model from Hugging Face. After mapping the labeled entries and undersampling, 6.600 balanced samples were used. Following preprocessing and data splitting, TF-IDF text representation and Logistic Regression model were applied. The model achieved 76% accuracy on validation data and improved to 83% on test data, with macro F1-scores reflecting consistent performance across all classes. These results suggest that movie synopses contain sufficient emotional signals to be automatically classified using a lightweight and effective machine learning approach.
Sistem Deteksi Tingkat Konsentrasi Berbasis Sensor Fisiologis Menggunakan Metode Certainty Factor Firman Fadilah; Cokorda Pramartha
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 3 (2026): JNATIA Vol. 4, No. 3, Mei 2026
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.2026.v04.i03.p14

Abstract

Concentration is a crucial mental focus ability that significantly impacts learning effectiveness and work productivity. Decreased concentration levels can be caused by various factors such as fatigue, stress, environmental disturbances, or specific psychological conditions. Poor concentration affects performance quality and task completion efficiency. Therefore, early detection of concentration levels can help optimize learning processes and improve overall performance. This research designs a concentration level detection system based on physiological sensors using DS18B20 temperature sensor to read body temperature data, pulse sensor to measure heart rate, and GSR sensor to monitor skin conductivity levels. These three physiological parameters serve as inputs for concentration level detection using the certainty factor method with output concentration levels consisting of highly focused, normally focused, less focused, and unfocused. The system design employs System Development Life Cycle (SDLC) Waterfall model approach encompassing requirement analysis, system design, and testing methodology. The system is designed to provide real-time concentration level detection based on physiological parameter changes during cognitive activities. This research produces a system design that serves as a foundation for developing concentration monitoring tools in various applications.

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