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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,
Computer Science & IT
JNATIA (Jurnal Nasional Teknologi Informasi dan Aplikasinya) merupakan jurnal yang berfokus pada teori, praktik dan metodologi seluruh aspek teknologi di bidang ilmu dan teknik komputer serta ide-ide produktif dan inovatif terkait teknologi baru dan sistem informasi. Jurnal ini memuat makalah penelitian asli yang belum pernah dipublikasikan dan telah melalui jurnal double-blind review. JNATIA (Jurnal Teknologi Informasi dan Penerapannya) diterbitkan empat kali setahun (Februari, Mei, Agustus, November).
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 255 Documents
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Sistem Rekomendasi Game dengan Metode K-Nearest Neighbor (KNN) Wiguna, I Putu Marcel; Dwidasmara, Ida Bagus Gede
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 2 No 1 (2023): JNATIA Vol. 2, No. 1, November 2023
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

The rapid growth of the gaming industry has resulted in an overwhelming number of game titles available to users. However, the abundance of choices makes it challenging for users to find games that match their preferences and interests. To address this issue, this research paper focuses on the development of a game recommendation system. The goal is to create an effective system that assists users in discovering games that align with their tastes and enhances their gaming experience.In this study, the K-Nearest Neighbor (KNN) method is employed as the underlying algorithm for the game recommendation system. The KNN method is a popular machine learning technique known for its ability to classify data based on similarities.This allows the system to recommend games that are likely to be of interest to users based on their preferences and the characteristics of games they have previously enjoyed. This research contributes to the field by showcasing the potential of the K-Nearest Neighbor (KNN) method in developing an efficient game recommendation system. The system's capability to assist users in discovering engaging games tailored to their interests has implications for improving user experience and driving game sales. Keywords: Game, Recommendation System, KNN
Implementasi Algoritma K-Nearest Neighbor (K-NN) dalam Deteksi Dini Penyakit Hepatitis C Kusuma, Ni Made Rika Padeswari; Astuti, Luh Gede
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 1 (2022): JNATIA Vol. 1, No. 1, November 2022
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

According to the World Health Organization (WHO), Hepatitis is an inflammatory condition that can evolve into Cirrhosis or liver cancer. Hepatitis is a disease that is caused by several types of viruses that attack and cause inflammation and damage to the cells of the human liver. Hepatitis C Virus (HCV) is one of the viruses that caused hepatitis and is considered the biggest impact among the other viruses that caused hepatitis. This study uses a classification method with the K-Nearest Neighbor (KNN) algorithm to detect the onset of hepatitis C in patients based on data from the patient’s laboratory checks. The classification method with K-Nearest Neighbor (KNN) algorithm is carried out by comparing the neighbors between test data and train data based on the patient’s medical history. The tuning parameter is used to determine the number of neighbors or the value of K in K-Nearest Neighbor (KNN) which obtains 92% of accuracy, 92% of precision, and 99% of recall with an 80:20 ratio of training data and test data.
Analisis Data Berbentuk Teks dalam Sistem Diagnosis Penyakit dengan Supervised Learning Ferry Mahayudha, I Gusti Ngurah Bagus; Dwidasmara, Ida Bagus Gede
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 4 (2023): JNATIA Vol. 1, No. 4, Agustus 2023
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

In computer science, text refers to a sequence of characters that can be represented and processed by a computer. It is the basic unit of data for representing human-readable information, such as letters, numbers, symbols, and spaces. In computer programming, text is typically represented as a string of characters. Textual data can be stored in variables, manipulated using various string operations, and displayed to users through input/output operations.Text plays a crucial role in many areas of computer science, including natural language processing, information retrieval, data mining, and text-based communication systems like email, chat applications, and social media. It serves as a fundamental component for storing, analyzing, and processing vast amountsof textual information in various applications. Keywords: string of characters, textual data, NLP, information retrieval, data mining
Case Based Reasoning (CBR) Mendiagnosa Kerusakan Motor Matic Menggunakan Metode Forward Chaining Pratama, Fahmi Ahmad Arum; Dwidasmara, Ida Bagus Gede
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 1 (2022): JNATIA Vol. 1, No. 1, November 2022
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

Tingginya jumlah pengguna sepeda motor matic di Indonesia, sehingga tidak semua pengguna sepeda motor matic memiliki kemampuan untuk melakukan perbaikan kerusakan sepeda motornya. Sehingga pengguna lebih cenderung mempercayakan masalahnya kepada mekanik bengkel dengan jam kerja yang terbatas. Dalam perawatan sepeda motor matic, bisa dilakukan sendiri, dan tanpa harus datang ke bengkel dengan membawa kendaraan. Kurangnya menentukan kerusakan motor, solusi yang ada di internet kurang meyakinkan. CBR telah menjadi teknik yang terbukti untuk sistem berbasis pengetahuan di banyak domain. Memiliki arti menggunakan pengalaman sebelumnya dalam kasus serupa untuk memahami dan memecahkan masalah baru. CBR dapat mengumpulkan kasus-kasus sebelumnya yang mirip dengan masalah baru dan mencoba untuk memodifikasi solusi agar sesuai dengan kasus baru. Maka diimplementasikannya sistem pakar ini yang bertujuan untuk memudahkan pengguna sepeda motor matic saat mengalami kerusakan dengan hanya mencantumkan gejala-gejala yang dialami pengguna, sehingga sistem akan membantu mencarikan solusi terbaik atas kerusakan yang dialami pengguna sepeda motor matic menggunakan forward chaining dimana setiap kondisi sistem akan mencari aturan dalam basis pengetahuan yang sesuai dengan kondisi if.
Analisa Sistem Rekomendasi Konten Youtube Berdasarkan Durasi Menonton Menggunakan Content-Based Filtering Ananta, I Gede Ngurah Wahyu; Supriana, I Wayan
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 3 (2023): JNATIA Vol. 1, No. 3, Mei 2023
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

n today's era, the internet is a facility in social life that causes phobias, complete and necessary information is difficult to obtain again. However, how YouTube provides consistently using an algorithm designed for YouTube content recommendations which is an online video media that can witness important moments instantly to individuals who are not on television media so that all users can get useful information and entertainment from the media website. For some reason Youtube is used as social media with the highest user level from Instagram. Therefore, we make an experiment to categorize the right content to be a crucial factor in producing accurate and meaningful recommendations. In a system analysis, it recommends content on Youtube based on individual categories using the basic concept of the content-based filtering algorithm and how it is implemented in the context of YouTube. The model training is carried out using the cosine similarity method which aims to compare the similarity between the contents of these representations. Evaluation of the model can provide insight into how effective the algorithm is in producing relevant recommendations. The steps in the recommendation system analysis are literature study, data collection, model training, and model evaluation by increasing understanding of content-based filtering algorithms. Keywords: YouTube, content recommendations, content-based filtering, cosine similarity
Perintah Menggunakan Sinyal Suara dengan Mel-Frequency Cepstral Coefficients (MFCC) dan K-Nearest Neighbor (KNN) Aryo Widano, Gusti Putu; Karyawati, Anak Agung Istri Ngurah Eka
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 1 (2022): JNATIA Vol. 1, No. 1, November 2022
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

Technological developments today allow every action that originally required more energy to complete will become more practical, an input to give a command can not only be done with a mouse, keyboard, or touch screen technology but can also be done with the help of voice with the help of data by the user computer. Before it can be used as a command, first go through the voice feature extraction process using the Mel-Frequency Cepstral Coefficients (MFCC) method and the classification process using the K-Nearest Neighbor (KNN) method. Previously someone conducted a similar study, with the classification used being Learning Vector Quantization (LVQ) and the highest accuracy obtained ? 0.1.
Klasifikasi Hoax Menggunakan Algoritma Naïve Bayes Putri, Riana Pramesti; Sanjaya ER, Ngurah Agus
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 1 (2022): JNATIA Vol. 1, No. 1, November 2022
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

The use of social media which is so mushrooming today, has many positive impacts but does not cover the negative impacts, one of which is the misuse of information. Hoax is one of the causes of disinformation and public unrest. The speed of spread, which sometimes cannot be controlled, is one of the reasons why hoax news is still being spread every day. Therefore, it is necessary to classify hoax news with the aim of helping the public in separating the news that is being spread. This study uses the Naive Bayes algorithm as a classification model with the addition of hyperparameter tuning. The best model is produced with an alpha of 0.01 which has an accuracy of 87.9%.
Klasifikasi Kematangan Buah Apel dengan Ekstraksi Fitur Haralick dan KNN Putra, I Kadek Bagus Deva Diga Dana; Suhartana, I Ketut Gede
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 4 (2023): JNATIA Vol. 1, No. 4, Agustus 2023
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

This research aims to classify the ripeness level of apple fruits based on texture features using the Haralick method and color features using histograms. A dataset of 76 apple fruit images was collected. In the preprocessing stage, the apple images were converted to grayscale, followed by the application of a median filter to remove salt and pepper noise, and histogram equalization to enhance image contrast. Texture features were extracted using the Haralick method to obtain contrast, correlation, energy, homogeneity, and entropy features. Color features were extracted using histograms to obtain mean, standard deviation, skewness, and kurtosis. A K-Nearest Neighbor (KNN) model with k = 6 was used for classification. The evaluation results showed an accuracy of 89.47%, precision of 93.75%, recall of 93.75%, and F1-score of 93.75%. This research indicates that texture and color features can effectively classify the ripeness level of apple fruits. Future research can explore more diverse datasets and parameter adjustments to further improve model performance. Keywords: apple fruit, ripeness classification, texture features, color features.
Analisis Sentimen Ulasan Pengguna Aplikasi Pelayanan Masyarakat Dengan Menggunakan Algoritma Random Forest Ardika, I Nyoman Arlan Kusuma; Wibawa S. T., M. Kom., I Gede Arta
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 1 (2022): JNATIA Vol. 1, No. 1, November 2022
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

Public services by the government generally have an impact that is quickly responded to by the community. One form of public response is through their opinions through writings written on social media or reviews of applications developed by the government. Machine learning has been widely used for automatic opinion mining to classify sentiment classes. The classification method that can be used to classify public opinion into positive or negative sentiment classes is random forest. Based on the test results of the random forest algorithm in classifying sentiments from user reviews of public service applications by the government, the highest accuracy value was obtained at 84% by performing hyperparameter tuning.
Penerapan RMS Contrast sebagai Penentu Citra Terbaik Berdasarkan Tingkat Kontras Adiriyanto, Shiennyta Florensia; Wibawa, I Gede Arta
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 3 (2023): JNATIA Vol. 1, No. 3, Mei 2023
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

This research centers on the objective of identifying the finest image from a burst shot through the utilization of Root Mean Square (RMS) contrast as a guiding metric. The primary focus lies in selecting the visually captivating image by assessing the contrast levels present in each individual shot. By conducting calculations of the RMS contrast values, the optimal image can be determined. The core purpose of this study is to employ RMS contrast as a reliable criterion for selecting the most outstanding image from a burst shot, thereby guaranteeing the production of high-quality visuals, and aiding in the organization and decluttering of smartphone galleries. This research holds significance in addressing the growing need for efficient and effective image selection methods, ensuring that users can effortlessly identify and showcase the best images captured in burst mode. By embracing the power of RMS contrast analysis, individuals can confidently curate their image collections with exceptional visual content while optimizing limited storage space on their smartphones. Keywords: Burst Shot, RMS contrast, Contrast Level, Best Image, Storage Space.

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