Jurnal Nasional Teknologi Informasi dan Aplikasinya
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).
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316 Documents
<![CDATA[Pengenkripsian File Data Pasien untuk Menjamin Kerahasian Informasi Medis ]]>
<![CDATA[Gary Melvin Lie]]>;
<![CDATA[Luh Gede Astuti]]>
<![CDATA[ Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 1 (2023): JNATIA Vol. 2, No. 1, November 2023 ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
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DOI: 10.24843/JNATIA.2023.v02.i01.p30
<![CDATA[In the era of digitalization of medical information, safeguarding patient data confidentiality has become paramount. This research aims to address the issue of data leakage by implementing file encryption using the AES-128 algorithm. The research methodology encompasses problem identification, system design, testing, and evaluation. The encryption steps of AES-128, namely SubBytes, ShiftRows, MixColumns, and AddRoundKey, are applied to enhance the security of patient data. Testing is conducted using various types of medical data, and an analysis is performed to assess the level of security and algorithm performance. The results indicate that file encryption with AES-128 can provide a high level of security for patient medical information. The AES-128 algorithm generates secure ciphertext that cannot be easily decrypted without the corresponding key. This research contributes to the field of medical information security by implementing AES-128 file encryption in patient data management systems. By enhancing data privacy and security, the utilization of this algorithm has the potential to provide strong protection against data breaches. Further studies can explore the wider application of AES-128 in the context of medical data security and improve algorithm performance. ]]>
<![CDATA[Klasifikasi Kualitas Sayuran Menggunakan Metode Support Vector Machine ]]>
<![CDATA[Ignatius Charles Hans Burwos]]>;
<![CDATA[I Gusti Ngurah Anom Cahyadi Putra]]>
<![CDATA[ Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 3 (2025): JNATIA Vol. 3, No. 3, Mei 2025 ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
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DOI: 10.24843/JNATIA.2025.v03.i03.p01
<![CDATA[The advancement of technology, particularly in the field of machine learning, has provided promising opportunities for enhancing agricultural practices. This study presents the development of a Support Vector Machine (SVM) based classification system for assessing the quality of vegetables. The system utilizes image processing techniques, including Histogram of Oriented Gradients (HOG) and color histogram, to extract relevant features from vegetable images. The extracted features are then used to train an SVM model capable of distinguishing between good and bad quality vegetables. The effectiveness of the proposed system was evaluated using a dataset comprising various types of vegetables. The results demonstrate high accuracy and efficiency in classifying vegetable quality, highlighting the potential of machine learning technologies in agricultural management. ]]>
<![CDATA[Simulasi Sistem Monitoring Kebun Pintar Berbasis IoT dengan Cisco Packet Tracer ]]>
<![CDATA[Ni Kadek Dwi Marhaeni]]>;
<![CDATA[Agus Muliantara]]>
<![CDATA[ Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 3 (2025): JNATIA Vol. 3, No. 3, Mei 2025 ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
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DOI: 10.24843/JNATIA.2025.v03.i03.p02
<![CDATA[The agricultural sector faces the ongoing challenge of enhancing productivity while ensuring sustainability. Inspired by previous research on automatic irrigation sensors, the concept of a smart garden has emerged as a promising solution. This study extends prior work by developing and implementing four distinct sensors: temperature, automatic watering, air humidity, and motion sensors. Leveraging machine-to-machine communication, these sensors integrate into an environmental monitoring system to optimize resource utilization and maintain ideal plant conditions. By utilizing Cisco Packet Tracer software, this research simulates a comprehensive smart garden system, offering insights into monitoring garden environments and automating agricultural processes. With a focus on data-driven decision-making, this study contributes to a smarter, more sustainable agricultural vision. ]]>
<![CDATA[Analisis Sentimen pada Sengketa Pilpres 2024 dengan Multinomial Naïve Bayes dan Chi-Square ]]>
<![CDATA[I Gede Widnyana]]>;
<![CDATA[Anak Agung Istri Ngurah Eka Karyawati]]>
<![CDATA[ Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 3 (2025): JNATIA Vol. 3, No. 3, Mei 2025 ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
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DOI: 10.24843/JNATIA.2025.v03.i03.p03
<![CDATA[The Constitutional Court officially rejected all appeals against the 2024 Presidential Election results in Jakarta, drawing public attention. People expressed their opinions through social media platforms, including YouTube. Therefore, this research was conducted to analyze public sentiment regarding this political phenomenon. This study uses primary data on comments from 3 YouTube videos that were crawled with a total of 4103 data. The data underwent labeling and then went through 5 stages of text pre-processing. Subsequently, modeling was performed using the Multinomial Naïve Bayes Algorithm with testing on 10 variations of the Chi-Square threshold, followed by validation with 10-fold cross-validation. The research yielded an optimal model performance, including accuracy, precision, recall, and F1-score, stabilizing at 72% with a ChiSquare feature selection of 70% and 90%. Without feature selection, the model's performance was at 71.5%. Model validation with the highest and average K-Fold Cross-Validation scores were sequentially at 74% and 69%. The built model is sufficiently good at analyzing sentiment and provides insights that 52% of Indonesians are dissatisfied with the Constitutional Court's decision based on YouTube comments. ]]>
<![CDATA[Perancangan Model Ontologi untuk Representasi Pengetahuan Cagar Budaya Masa Kolonial di Indonesia ]]>
<![CDATA[I Gede Widiantara Mega Saputra]]>;
<![CDATA[Gst. Ayu Vida Mastrika Giri]]>
<![CDATA[ Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 3 (2025): JNATIA Vol. 3, No. 3, Mei 2025 ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
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DOI: 10.24843/JNATIA.2025.v03.i03.p04
<![CDATA[This study aims to develop an ontology model that represents knowledge about cultural heritage from the colonial era in Indonesia, an important effort in preserving and educating about cultural heritage. In the process, this ontology model is constructed using Protégé, a free and open-source ontology development platform that supports various formats including OWL (Web Ontology Language). By utilizing the Methontology methodology, this model ensures systematic and structured development. The structure of the ontology model includes one main class with four subclasses, which assists in the logical categorization of information and supports in-depth analysis. Testing the model using SPARQL queries confirms the accuracy and correctness of the data structure. The findings of this study indicate that the developed ontology successfully achieves its set objectives, not only in documenting information effectively but also in enriching methods of presenting and analyzing knowledge about cultural heritage. This opens the way for further research and practical applications in the preservation and education of cultural heritage, underlining the importance of digitalization in historical preservation. ]]>
<![CDATA[Perbandingan Performa Ensemble Classifier dan Model Klasifikasi Tunggal dalam Clickbait Detection ]]>
<![CDATA[Ilham Arsy Dwi Atmojo]]>;
<![CDATA[I Gede Arta Wibawa]]>
<![CDATA[ Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 3 (2025): JNATIA Vol. 3, No. 3, Mei 2025 ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
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DOI: 10.24843/JNATIA.2025.v03.i03.p05
<![CDATA[The rapid advancement of the digital era has led to a significant increase in online news content, accompanied by a growing issue of clickbait—sensational headlines designed to attract readers. This study aims to develop a clickbait detection system to classify Indonesian news headlines as clickbait or non-clickbait. Building upon previous research, this work explores the performance of ensemble classifiers compared to single classifiers such as Multinomial Naïve Bayes, Support Vector Machine (SVM), and Random Forest. Using a dataset of 4,500 headlines with 51.6% clickbait and 48.4% non-clickbait ratio. With an 80-20 train-test split, four machine learning models were applied. The best-performing model was the ensemble classifier using hard voting, achieving an accuracy of 84.22%, precision of 85.81%, recall of 82%, and an F1-score of 83.86%. These results indicate that the ensemble classifier outperforms single classifiers in identifying clickbait in Indonesian news headlines. The findings suggest that ensemble classifiers are a promising approach for improving clickbait detection in digital news media. ]]>
<![CDATA[Pengujian Penerimaan Pengguna pada Website “Wariga” Menggunakan Metode Technology Acceptance Model (TAM) ]]>
<![CDATA[I Ngurah Komang Agus Suryadiyatmika. S]]>;
<![CDATA[I Gede Santi Astawa]]>;
<![CDATA[I Gede Sri Agus Putrawan]]>
<![CDATA[ Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 3 (2025): JNATIA Vol. 3, No. 3, Mei 2025 ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
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DOI: 10.24843/JNATIA.2025.v03.i03.p06
<![CDATA[The "Wariga" website is an information system to determine a person's harmony condition based on the wariga framework, namely wewaran and wuku. This website has only been developed since this research was conducted. To support the development carried out towards the next stage, certain measurements can be carried out such as acceptance testing. One measure of acceptance of an information system is to use the TAM (Technology Acceptance Model) method. This research aims to assess "Wariga" using the TAM method, with the aim of obtaining information on the level of perception of each website acceptance variable. The method used in this research is a quantitative method, with the help of a questionnaire as a data collection medium. It was found that 94 respondents provided questionnaire answers for each statement submitted. The results of this research obtained a positive influence from the three TAM variables used, such as perceived comfort of 89.87%, perceived usefulness of 87.96%, and perceived acceptance of 87.83%. This shows that the “Wariga” website can be upgraded to the next stage of development. ]]>
<![CDATA[Optimasi C4.5 Berbasis PSO untuk Prediksi Kanker Payudara dengan Data BC Wisconsin ]]>
<![CDATA[Tun Pasek Sarwiko Dipranoto]]>;
<![CDATA[I Gede Surya Rahayuda]]>
<![CDATA[ Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 3 (2025): JNATIA Vol. 3, No. 3, Mei 2025 ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
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DOI: 10.24843/JNATIA.2025.v03.i03.p07
<![CDATA[Breast cancer is a type of cancer that often arises from the development of abnormal cells in breast tissue, which then grow uncontrollably. In Indonesia, breast cancer cases are the highest compared to other types of cancer and are one of the main causes of death. This research aims to optimize the C4.5 algorithm using Particle Swarm Optimization (PSO) to predict breast cancer using the Wisconsin Breast Cancer dataset. Breast cancer remains one of the leading causes of death in women worldwide, emphasizing the importance of early detection and accurate classification. Previous research has demonstrated the effectiveness of various algorithms, including Decision Tree, Naive Bayes, and K-Nearest Neighbors, in diagnosing breast cancer, with K-Nearest Neighbors often demonstrating superior accuracy. This research evaluates the performance of the C4.5 algorithm, both before and after being optimized with PSO. Preliminary results show that the C4.5 algorithm without optimization achieves 94% accuracy. After optimization with PSO, the accuracy increased to 96%, highlighting the potential of PSO in improving prediction models for breast cancer diagnosis. ]]>
<![CDATA[Implementasi Particle Swarm Optimization pada Sistem Rekomendasi Tanaman Hortikultura Berbasis Naïve Bayes ]]>
<![CDATA[Kadek Belvanatha Gargita Satwikananda]]>;
<![CDATA[I Gede Surya Rahayuda]]>
<![CDATA[ Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 3 (2025): JNATIA Vol. 3, No. 3, Mei 2025 ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
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DOI: 10.24843/JNATIA.2025.v03.i03.p08
<![CDATA[Indonesia is an agrarian country with an environment that strongly supports agricultural processes. Because of this, crops are one of Indonesia’s main commodities and choosing a right crop to cultivate becomes a crucial process. Nowadays, lots of recommendation system has been made to help with decision making. A crop recommendation system is one of them and prove to be helpful in helping farmers decide what crop to plant based on the condition of the environment. An idea of implementing particle swarm optimization on a crop recommendation system occurred. Particle swarm optimization can be implemented to choose the presumably best parameter for a Gaussian Naïve Bayes model. The result of implementing particle swarming optimization to find the best smoothing parameter is the accuracy of the model reaching 99.5%. However, this is not different to the result of the model without particle swarm optimization which reach the accuracy of around 99.5% too. ]]>
<![CDATA[Penggunaan Algoritma C4.5 dan Random Forest guna Meningkatkan Efisiensi Klasifikasi Penyakit Stroke ]]>
<![CDATA[I Agus Indra Dipta Prayoga]]>;
<![CDATA[I Gusti Agung Gede Arya Kadyanan]]>
<![CDATA[ Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 3 (2025): JNATIA Vol. 3, No. 3, Mei 2025 ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
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DOI: 10.24843/JNATIA.2025.v03.i03.p09
<![CDATA[Stroke is a very serious problem throughout the world. According to a report from the World Heart Organization (WHO), in 2022, more than 12.2 million, or one in four people aged 25 years will experience a stroke, and more than 7.6 million new stroke sufferers every year throughout the world. An irregular lifestyle is the main cause of someone having a stroke. Therefore, we need a system that can be used as a stroke classification or detection tool based on a person's disease history. The stroke disease data used in this study was obtained through Kaggle with a total of 5110 data. Based on the results of research that has been carried out using two algorithm models, namely the C4.5 algorithm and Random Forest. A combination of these two algorithms has been obtained which produces a stroke classification system with fairly good accuracy, with an accuracy value of 92.4%. ]]>
