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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 Performa Algoritma K-Nearest Neighbor dalam Klasifikasi Penyakit Tumor Otak Komang Gede Bagus Devit Aditiya; 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.p18

Abstract

Brain tumor disease poses a significant health challenge globally, including in Indonesia. Detecting brain tumors early is crucial for effective treatment. In this study, we investigated the performance of the K-Nearest Neighbor (KNN) algorithm in classifying brain tumor disease using brain image data. Our findings reveal that the choice of K value significantly impacts the KNN algorithm's performance. The highest accuracy of 81% was achieved with K=3, while the lowest accuracy of 66% occurred at K=7. On average, across all scenarios, the accuracy was 72.8%. These results underscore the importance of selecting the appropriate K value for optimal classification accuracy in brain tumor disease using the KNN algorithm. 
Implementasi Algoritma Yolo untuk Deteksi Kebusukan pada Sayur Kembang Kol Alexander Ibrahim; I Wayan Supriana
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.p19

Abstract

This research utilizes the YOLOv8 algorithm to detect spoilage in cauliflower vegetables. Image data was collected from Google, processed using Roboflow, and tested using Google Colab. The study results indicate an accuracy of 59%, recall of 58%, and MAP of 60%. The YOLOv8 algorithm significantly contributes to image recognition and visual data processing. Additionally, the article discusses the application of the YOLOv8 algorithm for object detection in 360-degree panoramic images. The training process was conducted to recognize objects in the images, and evaluation was performed using a confusion matrix and mAP50. The evaluation results demonstrate the model's good performance in object recognition. Several references cited in the article are also included. 
Deteksi Hate Speech pada Unggahan Media Sosial dengan Naive Bayes Menggunakan Seleksi Fitur Chi-Square Putu Steven Belva Chan; Ida Ayu Gde Suwiprabayanti 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.p20

Abstract

In the digital age, social media's pervasive use has revolutionized global communication but also introduced challenges like hate speech. This study proposes a Multinomial Naive Bayes model optimized with Chi-square feature selection to detect hate speech efficiently from large-scale social media data. Leveraging machine learning, this approach aims to combat harmful content by identifying relevant text features crucial for distinguishing hate speech from non-hate speech. The study utilizes TF-IDF for feature extraction and Chi-square for feature selection, showing significant performance improvements in hate speech detection. The Chi-square feature selection model yielded average precision, recall, F1-score, and accuracy values of 92%, 92%, 91%, and 92% respectively. In contrast, the model without feature selection achieved values of 89%, 89%, 88%, and 89% for the same metrics. Results demonstrate enhanced accuracy, precision, recall, and F1-score across various hate speech categories. 
Rancang Model Ontologi Dalam Representasi Digital Loloh Cemcem Penglipuran Putu Chandra Mayoni; Ida Bagus Gede Dwidasmara
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.p21

Abstract

Bali is renowned for its diverse cultural heritage, including traditional herbal drinks like Loloh Cemcem from Penglipuran Village. This immunity-boosting beverage dates back to the Majapahit era but gained popularity during the COVID 19 pandemic. Despite being recorded as a Communal Intellectual Property, information about Loloh Cemcem is not well-structured, hindering knowledge dissemination. This research aims to address this issue by designing an ontology model to represent knowledge about Loloh Cemcem digitally. The Methodology approach is employed, consisting of specifying, knowledge acquisition, conceptualization, integration, implementation using Protégé software, evaluation through SPARQL queries, and documentation. The ontology model captures concepts, properties, relationships, and individuals related to Loloh Cemcem, facilitating structured information access and preservation of this traditional beverage’s knowledge. 
Simulasi IoT Pemantauan Tanaman Lidah Buaya Berbasis Algoritma Fuzzy Bayu Yudistira Ramadhan; Luh Arida Ayu Rahning Putri
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.p22

Abstract

In the digital era, technological advancements have entered the Industrial Revolution 4.0, and the agricultural sector in Indonesia is adapting to this revolution. Using a fuzzy algorithm, this research simulates an IoT-based monitoring system for Aloe vera plants. The system aims to assist farmers in real-time and accurately monitor Aloe vera plant conditions. Aloe vera has a narrow optimal temperature range of 16-33°C and an ideal soil moisture range of 40-75% for optimal growth. By implementing fuzzy logic, a mathematical concept that is easy to understand, the system can accurately map input conditions to output decisions. The simulation uses MATLAB and the Tinkercad website to design a fuzzy logic system that controls a water pump and fan based on soil moisture and temperature inputs from SEN1 and LM35 sensors, respectively. The fuzzy rules maintain the ideal conditions for Aloe vera growth, reducing energy consumption and water waste. The results demonstrate the high accuracy of the fuzzy logic system in making control decisions for maintaining optimal growing conditions. 
Analisa Perancangan Sistem Rekomendasi Makanan Untuk Defisit Kalori “Calcraft” Melalui Evaluasi UI UX Putu Ananda Darma Wiguna; Luh Gede Astuti
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.p23

Abstract

This research raises issues related to unbalanced diets in modern society that cause obesity problems. As a solution, calorie deficit was chosen as a program to solve this obesity problem. Developed an application called "Calcraft" which is integrated with a recommendation system to provide food selection recommendations by considering the calculation of BMI (Body Mass Index) and BMR (Basal Metabolic Rate). Application evaluation is carried out using the System Usability Scale (SUS) method, with a focus on user interface and user experience. Not only that, but the user’s understanding of the knowledge also gained from the features on Calcraft is one of the things the author hopes for. Of the 35 respondents who filled out the SUS questionnaire, "Calcraft" obtained a total score of 2907.5, with an average of 83.07142857. These results indicate very good quality, corresponding to the "A" or "Excellent" category in SUS interpretation. Thus, this app has great potential to assist individuals in running a calorie deficit program effectively and conveniently. 
Analisis dan Klasifikasi Genre Musik Menggunakan Algoritma STFT dan Random Forest Merry Royanti Manalu; Made Agung Raharja
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.p24

Abstract

This research analyzes the classification of music genres using the Short Time Fourier Transform (STFT) algorithm. The main objective is to identify the effectiveness of STFT, along with the Random Forest classification algorithm, in distinguishing music genres based on their spectral characteristics. The STFT method is utilized to transform audio signals into a spectral representation within a short time window. The extracted spectral features are then fed into the Random Forest classification algorithm to classify different music genres. This research involves the use of representative datasets from various music genres for performance evaluation. Experimental results show that using STFT as a feature and employing the Random Forest classification algorithm in the process are able to provide satisfactory results in distinguishing music genres, with an accuracy of 86%. These findings demonstrate the potential of STFT, in combination with Random Forest, as a useful tool in music analysis and automatic classification of music genres. 
Pengamanan Gambar dengan Metode Cipher Block Chaining Made Yayang Eka Prananda; 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.p25

Abstract

Data security is something that everyone needs to maintain their privacy, for example images. Images are very familiar to everyone, because we can capture various moments in the form of images. But there are some people who steal someone's image to misuse it for personal gain. Image pixel encryption using cryptography can be an alternative in securing the image. In this study, the cipher block chaining method is used to encrypt each pixel. The results of using this method are quite satisfactory, the resulting image becomes more abstract. The encrypted image can also be decrypted into the original image. However, the resulting pattern is still the same as the previous image pattern. So the use of this method may be more effective when combined with other cryptographic methods. 
Pengembangan Model Ontologi Pada Domain Oleh-Oleh Khas Bali Putu Ardi Sudarmika; Ngurah Agus Sanjaya ER
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.p26

Abstract

Bali's typical souvenirs not only represent a high cultural heritage but also serve as a primary attraction for tourists. However, detailed information on these souvenirs remains limited, making it challenging for users to choose. To enhance accessibility, the concept of representing Bali's souvenir data in ontology-based knowledge emerged. The aim is to provide references to tourists by regularly structuring an ontology model. Knowledge about Bali's souvenirs is represented using RDF in triple concepts of subject, predicate, and object, accessible through SPARQL. The development process employs the methodology methodology, encompassing specification, knowledge acquisition, conceptualization, integration, implementation, evaluation, and documentation phases. The outcome is an ontology model featuring 4 classes, 3 Object Properties, data properties, 34 ontographs, and 28 individuals or instances, offering regularly relevant information about Bali's souvenirs. This method utilizes Protégé 5.5.0 software and a search-based application for testing its efficacy. 
Impelementasi Kriptografi RSA dan XOR Cipher Untuk Enkripsi Citra Digital KTP Gede Krisna Surya Artajaya; Agus Muliantara
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 4 (2024): JNATIA Vol. 2, No. 4, Agustus 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.v02.i04.p01

Abstract

The advancement of technology has led to innovative solutions in administrative sectors, exemplified by the introduction of “KTP Digital”. However, not everyone has adopted “KTP Digital” and is still relying on scanned copies of identity cards that can expose digital image data to security vulnerabilities. This study addresses these vulnerabilities by proposing encryption techniques. Utilizing RSA and XOR Cipher algorithms, this research demonstrates effective encryption and decryption of digital image data. Evaluation metrics, including Peak Signal-toNoise Ratio (PSNR), confirm minimal similarity between plain and cipher images, indicating robust encryption. Specifically, PSNR values for plain vs. cipher images range from 7 to 8 dB, well below 10 dB, indicating a very significant difference. Additionally, high PSNR values between original and decrypted plain images, which is 100 dB, suggest negligible data alteration post decryption confirming that the decryption process successfully restores the image to its original state. 

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