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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 316 Documents
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Analisis Sentimen Ulasan Traveloka Menggunakan Metode Naïve Bayes Classifier dan Information Gain Kadek Yuni Suratri; I Gede Santi Astawa
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.p08

Abstract

In the increasingly rapid digital era, Traveloka is present as an online travel agency that makes it easier for users to order and plan trips. Reviews left by users can reflect the user's experience in using the platform. Indirectly, reviews can also reflect user satisfaction. Therefore, it is important to carry out sentiment analysis of existing reviews so that you can improve service quality. This research examines the performance of the Information Gain feature selection in classifying the sentiment of Traveloka application reviews using the Naïve Bayes method. The research results show that classification using the Naïve Bayes model obtained an accuracy of 83%, precision of 81%, and recall of 98%. Meanwhile, classification with feature selection obtained an accuracy of 79%, precision of 76%, and recall of 100%. This shows that the feature selection performance has not been able to increase the accuracy value. 
Perancangan Desain Antarmuka Aplikasi Soul Notes dengan Metode Design Thinking Berbasis Mobile Ni Komang Purnami; I Gede Surya Rahayuda
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.p09

Abstract

This study aimed to design an interface for the Soul Notes application using design thinking methodology, focusing on mobile-based applications. The study employed a qualitative approach, involving 5 participants who were asked to use the application and provide feedback. The results showed that the participants found the application easy to use and appreciated its features, such as mood tracking and relaxation techniques. The study also found that the participants valued the application's ability to provide a platform for expressing emotions freely and securely. The results of the study were analyzed using the Single Ease Question (SEQ) method, which indicated a high level of ease of use, with an average score of 6.52 out of 7. The study's findings suggest that the Soul Notes application can effectively support mental well-being and emotional management, and that its design should prioritize user experience and security 
Analisis Prediktif Bitcoin dengan Metode SVM serta Pembobotan TIF-IDF Berbasis Data Narrative Danendra Darmawansyah; I Gusti Agung Gede Arya Kadyanan
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.p10

Abstract

The cryptocurrency market has experienced significant volatility in recent years, making it challenging for investors to make informed decisions. This study aims to develop a predictive model for cryptocurrency price increases using TF-IDF (Term Frequency-Inverse Document Frequency) and SVM (Support Vector Machine) based on narrative data. Narrative data, such as news articles and social media posts, can provide valuable insights into investor sentiment and market trends. The proposed model extracts relevant features from narrative data using TF-IDF and employs SVM to classify cryptocurrency price movements into positive, negative, or neutral categories. Experimental results demonstrate the effectiveness of the proposed model in predicting cryptocurrency price increases, with an accuracy of over 70%. The findings suggest that narrative data can be a valuable source of information for cryptocurrency price prediction and that TF-IDF and SVM are effective methods for analyzing narrative data. 
Identifikasi Mekar dan Kuncupnya Bunga Sedap Malam Menggunakan Convolutional Neural Network Kadek Bakti Pramanayoga St; I Gusti Agung Gede Arya Kadyanan
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.p11

Abstract

The utilization of technology can aid humans across various sectors, including agriculture. This study harnesses one such technology to identify a particular agricultural commodity, tuberose flowers (Polianthes tuberosa), based on their blooming using Convolutional Neural Network (CNN). The CNN method can assist farmers in harvesting tuberose flowers by distinguishing between bloomed and budding flowers. In this research, a dataset comprised of 600 primary data points captured via smartphones is utilized, divided into 420 training sets and 180 testing sets. Three scenarios are tested, involving training epochs of 10, 15, and 20. The testing results indicate that the first scenario achieves an accuracy score of approximately 82.44%, falling below the 85% threshold. Meanwhile, the second and third scenarios achieve accuracies of approximately 91.20% and 92%, respectively 
Penerapan Model Ontologi dalam Perkembangan Game Digital I Komang Maheza Yudistia; I Gusti Ngurah Anom Cahyadi Putra
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.p12

Abstract

This research develops an ontology for interactive digital games using the Methontology methodology, which includes the stages of specification, knowledge acquisition, conceptualization, integration, implementation, and evaluation. The development process involved using the Protégé tool to build and manage the ontology. The result is an ontology consisting of 32 classes, 12 property objects, 0 property data. This ontology was tested using SPARQL queries on the Jena Fuseki web, demonstrating its ability to support more effective classification and knowledge management. The application of this ontology improves data interoperability and the gaming experience and enables the development of more structured and complex applications. This research confirms the benefits of ontologies in enriching data structure and integration in the interactive digital game domain. 
Klasifikasi Mood pada Musik Pop dan Jazz dengan Menggunakan Mel Frequency Cepstral Coefficients dan K-Nearest Neighbor I Gusti Bagus Putrawan; I Ketut Gede Suhartana
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.p13

Abstract

This research discusses mood classification in pop and jazz music using Mel Frequency Cepstral Coefficients (MFCC) and the K-Nearest Neighbor (KNN) algorithm. The dataset used consists of900 songs with mood labels angry, happy, relaxed, and sad obtained from Kaggle. The data wasprocessed by extracting 13 MFCC features and then continuing with classification using KNN. The research results show that the best accuracy reaches 64% with K=9. Accuracy at K=7 obtained a value of 60%, while at K=11 an accuracy of 58% was obtained. Evaluation was carriedout using accuracy, precision, recall and f1-score metrics, with the best results found at K=9. Thisresearch emphasizes the importance of selecting K parameters for optimizing mood classificationmodels. 
Penerapan Enkripsi dan Dekripsi Dokumen Data UMKM Menggunakan Algoritma ChaCha20-Poly1305 I Made Chandra Widjaya; I Komang Ari Mogi
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.p14

Abstract

Despite the increasing importance of data security in safeguarding sensitive information, this study addresses the potential risks associated with unauthorized access to critical data. Employing the ChaCha20-Poly1305 algorithm, the research focuses on implementing encryption and decryption processes for Small and Medium Enterprise (SME) documents, supplemented by key derivation from AES-256 for enhanced security. A nonce Initialization Vector (IV) is generated using the ChaCha20-Poly1305 algorithm, with users inputting secret keys for encryption. The system then encrypts the data using the ChaCha-Poly1305 algorithm and derives keys from AES using SHA-256 hashing. For decryption, users input the encrypted document into the program, along with the previously used key. The system design employs a simple web-based application, with the ChaCha20-Poly1305 cryptography algorithm implemented in PHP. The study successfully tests the ChaCha20-Poly1305 algorithm, and the program exhibits secure decryption processes, evidenced by consistent byte sizes of tested SME documents. 
Klasifikasi Genre Musik Menggunakan Metode Support Vector Machine Dengan Multi-Kernel I Gusti Agung Istri Agrivina Shyta Devi; I Made Widiartha
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.p15

Abstract

Music is a universal art that reflects cultural diversity and individual preferences through various genres. This research explores music genre classification using Support Vector Machine (SVM) with multi-kernel methods. The SVM algorithm, known for its effectiveness in handling complex datasets, is employed to classify music genres based on audio features. The research utilizes the GTZAN dataset, comprising 10 music genres, and extracts audio features from WAV files. After normalization and data splitting, SVM models are trained and evaluated. Results indicate a significant accuracy improvement after hyperparameter tuning, with the best models achieving accuracies of 88.92% for the polynomial kernel and 89.32% for the RBF kernel. 
Analisa Rancangan Desain Antarmuka Aplikasi LibrarySense Menggunakan System Usability Scale Gagas Pradipta Jatmiko; I Putu Gede Hendra Suputra
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.p16

Abstract

The comfort of the library plays a crucial role in supporting learning activities. Temperature that is too hot or too cold, high levels noise, or the presence of smoke can affect the focus and effectiveness of learning for library users. LibrarySense is an application that utilizes wireless sensor network to monitor the environmental conditions of the library in real-time. The application is designed to assist library managers in improving operational efficiency and facility management, as well as enhancing user experience. The System Usability Scale (SUS) method is employed to evaluate the usability of the application. Literature review is conducted to understand the context of implementing SUS, while user interface design is carried out through wireframing and high-fidelity design. Usability testing is conducted using SUS questionnaires distributed to respondents. The research findings indicate that the LibrarySense application achieves an average SUS score of 81.74, indicating a high level of user satisfaction with usability and user experience. This suggests that the LibrarySense application has the potential to enhance the quality of the library environment and support managers in making more informed decisions. 
Pengaruh Penanganan Ketidakseimbangan Kelas pada Prediksi Cacat Perangkat Lunak dengan Teknik Oversampling I Gusti Agung Ramananda Wira Dharma; I Wayan Santiyasa
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.p17

Abstract

Software defect prediction plays a vital role in SDLC testing by identifying modules prone to defects. However, imbalanced class distributions, where defect (minority) samples are outnumbered by non-defect ones, can hinder model performance. This study investigates the impact of oversampling techniques (SMOTE, ADASYN) on Naive Bayes classification for defect prediction. While the base Naive Bayes model achieved good overall accuracy (83%), it struggled with defect class recall (30%). Applying SMOTE and ADASYN improved recall (40% and 38%, respectively) but slightly lowered accuracy (77% and 80%). Future work will explore feature selection and deep learning approaches for potentially better performance. 

Page 17 of 32 | Total Record : 316

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