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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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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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Klasifikasi Ngengat dan Kupu-Kupu Menggunakan Metode GLCM dan Support Vector Machine I Dewa Made Mardana; Luh Gede Astuti
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.p22

Abstract

Butterflies and moths are two types of insects that share similarities in their appearance and physical characteristics. Both insects exhibit a variety of colors, patterns, and body shapes that are often difficult to distinguish. This research aims to classify butterflies and moths using feature extraction from the Gray-Level Co-occurrence Matrix. The feature extraction process involves extracting values such as correlation, homogeneity, contrast, and energy from angles of 0°, 45°, 90°, and 135° in each butterfly and moth image. Furthermore, the Support Vector Machine method is used for classification. The research results indicate that using feature extraction from the GrayLevel Co-occurrence Matrix and the Support Vector Machine method can achieve an accuracy of 68.11%, with precision, recall, and F1-Score values of 70.0%, 68.0%, and 68.0%, respectively. 
Penentuan Akor Piano dengan Algoritma Short Time Fourier Transform Theresia Angel Oktarina Pasaribu; Luh Gede Astuti
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.p23

Abstract

This research aims to develop a system that is able to identify the key of piano chords in a piece of music using the Short Time Fourier Transform (STFT) method, which is carried out based on frequency analysis of the basic notes that form the piano chord. STFT is used to analyze the audio signal of a piano chord and decompose its frequency spectrum over time. This method is implemented in the Google Colab environment with the Python programming language. The data used is the Piano Triads Wavset dataset which contains combinations of notes in a piano chord. The analysis process begins with data exploration to understand the patterns in the chords, then the system carries out analysis to determine the key to the chords. Test results show that this system has an accuracy of 54.83% in determining piano chords. 
Pengelompokan Lagu Populer untuk Musik Gym Menggunakan Metode K-Means Clustering Pande Nyoman Weda Wesnawa; Made Agung Raharja
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.p24

Abstract

Music streaming has emerged as the primary mode for individuals to enjoy music while exercising at the gym. Spotify, among the largest music streaming platforms, surveyed 2,000 gym users in the US, revealing that 82% utilize Spotify during workouts. Studies indicate music significantly influences workout quality. This study aims to cluster popular Spotify songs of 2023 using KMeans based on audio attributes like tempo, energy, and danceability. Data sourced from Kaggle's 2023 Spotify dataset underwent preprocessing. Utilizing the Elbow method, optimal cluster count determination yielded two clusters: one apt for gym use and another unsuitable. Out of 954 songs, 72.3% were gym appropriate. Visualizations via pie charts and 3D scatter plots depicted clusters based on BPM, energy, and danceability. Purity evaluation scored 1.0, ensuring accurate cluster formation. This research aids gym proprietors in crafting strategies to select motivating music, enhancing members' workout experiences. 
Analisis Sentimen dengan Logistic Regression untuk Deteksi Kata pada Livin’ by Mandiri Ni Made Gita Satviki Nirmala; Ngurah Agus Sanjaya ER
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.p25

Abstract

Livin by Mandiri is one of the most frequently used mobile banking. To find out the quality of the application, you can carry out sentiment analysis from reviews. The data taken from the Google Play Store was 6334 data from January 2022 to December 2022. The training data and test data used had a ratio of 80:20. This data goes through preprocessing and then TF-IDF weighting is carried out. After that, the analysis used logistic regression which produced 91.5% with C = 0.75. As well as getting negative sentiment results, namely precision 89%, recall 95%, f1-score 92%. Meanwhile, positive sentiment produces 94% precision, 88% recall, 91% f1-score. There is a word detection program that can help search for keywords including positive sentiment or negative sentiment from the Livin by Mandiri application. 
Implementasi Algoritma Yolo untuk Deteksi Buah Durian dan Manggis I Putu Aditya Pradana; Ngurah Agus Sanjaya ER
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.p26

Abstract

This study aims to implement the YOLOv8 algorithm in detecting images of durian and mangosteen fruits. The research methodology includes literature review, image collection, data processing, YOLOv8 algorithm implementation, model evaluation on validation data, and drawing conclusions. Image collection is done through online sources, and data is processed through annotation, pre-processing, and augmentation using the Roboflow platform before exporting to YOLOv8 format. The algorithm implementation is carried out in Google Colab with model training, object detection, and evaluation stages on validation data. Evaluation results include accuracy, recall, precision, and F1 score values, with model performance evaluated using mean average precision (mAP) metric. The results indicate that the model can recognize objects well, with a mAP above 0.27%. This study successfully implements YOLOv8 for durian and mangosteen fruit detection with satisfactory evaluation results. 
NutriMinder Aplikasi Pemantauan Gizi dan Panduan Makanan dengan Informasi yang Mudah Dipahami Muhamad Hidayat; Anak Agung Istri Ngurah Eka Karyawati
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.p27

Abstract

Effective nutritional monitoring to improve public health has become increasingly important in the modern era. NutriMinder is an application intended to provide easy-to-understand food guidance and help users monitor their daily nutritional intake. In this article, the NutriMinder algorithm and application design are discussed. It includes initialization, user registration, food logging, nutrition monitoring, food recommendations, analysis and reports, and user interface. 
Perlindungan pada Citra Motif Kain Songket dengan Teknik Watermarking Menggunakan RSA Encryption dan MSB Steganography I Wayan Gede Gemuh Raharja RL; Ngurah Agus Sanjaya ER
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 3 (2024): JNATIA Vol. 2, No. 3, Mei 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.i03.p26

Abstract

This research develops a watermarking steganography technique using the MSB method to protect songket cloth motifs. In the MSB-based steganography method for embedding watermarks in images, the MSB transformation is used to replace data bits in image segments with secret data bits. The embedded watermark functions as an identification mark that is difficult to remove or change without destroying the authenticity of the original motif. Accuracy testing using PSNR and MSE produced an average PSNR of 75.177 dB and MSE of 0.0018, which shows that this technique is effective in maintaining the authenticity and integrity of Songket cloth motifs. 
Analisis Sentimen pada Ulasan Aplikasi myIM3 Menggunakan Multinomial Naive Bayes dengan TF-IDF Ni Komang Ayu Juliana; Made Agung Raharja
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 3 (2024): JNATIA Vol. 2, No. 3, Mei 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.i03.p25

Abstract

The digital service adoption in Indonesia has emerged as a primary trend to meet the needs of the millennial generation, seeking greater convenience and speed. Amidst this trend, self-service apps like MyIM3 by Indosat Ooredoo Hutchison have become a trusted solution for users to manage their services more efficiently. Sentiment analysis is crucial for understanding user responses to such apps. This study employs the Multinomial Naïve Bayes algorithm with hyperparameter alpha 0.8 and TF-IDF to analyze sen timent towards user reviews on Google Play Store for MyIM3. The dataset, sourced from Kaggle, consists of 8475 reviews, pre-processed and labeled to 8212 reviews. Model evaluation with an 80:20 split reveals an overall accuracy of 89%, with a precision of 86% for negative (0) and 93% for positive (1) labels. The recall for negative is 95% and positive is 81%. Thus, this research contributes to understanding user perspectives on MyIM3 and provides a basis for enhancing the quality of app-based services. 
Analisis Sentimen Ulasan Aplikasi RedBus Menggunakan Metode SVM dan AdaBoost Shelomita Putrinda Culio; Luh Gede Astuti
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 3 (2024): JNATIA Vol. 2, No. 3, Mei 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.i03.p24

Abstract

Bus transportation is a mode of transportation relied upon by the public due to its accessibility and affordable ticket prices. Sentiment analysis of RedBus app reviews on the Google Play Store can provide insight into user sentiment toward the app. The aims of this study are to analyze sentiment in reviews of the RedBus app using two approaches: the Random Forest model and a combination of Random Forest with AdaBoost. The analysis classifies user opinions as positive or negative. The study uses the TF-IDF method for feature extraction, and the evaluation methods include K-Fold Cross Validation and Confusion Matrix. The findings indicate that the combination of Random Forest with AdaBoost significantly enhances performance compared to the standard Random Forest model. Using a combination of Random Forest with AdaBoost results in an average accuracy of 89.0%, while the standard Random Forest model attains an average accuracy of 85.0%. 
Klasifikasi Kematangan Sayuran Pare dengan Metode KNN I Gede Yogananda Adi Baskara; Luh Arida Ayu Rahning Putri
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 3 (2024): JNATIA Vol. 2, No. 3, Mei 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.i03.p23

Abstract

The bitter melon plant (Momordica charantia L) is a vegetable commodity that has commercial potential if cultivated on an agribusiness scale. The bitter melon plant products currently have quite a lot of consumers and have even entered supermarkets. However, the selection of bitter melon vegetables still uses human eye assessment which has the weakness of being subjective and inconsistent, so the level of accuracy is low. Based on these problems, researchers will create a system that is able to classify the level of maturity of bitter melon vegetables using HSV feature extraction with the KNN method at the classification stage and with the help of the Python programming language. In this research, 160 datasets will be used which are divided into 3 types of classes, namelcategy cooked bitter melon vegetables and raw bitter melon vegetables. The dataset is divided into two ories, namely 128 training data and 32 test data. The next stage is testing the data using the K-Nearest Neighbor method using the value k=3. From the test results, an accuracy rate of 88% was obtained. 

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