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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Minho Jo
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Intelligent Web-Based Application for Personalized Obesity Management Wijayakusuma, I Gusti Ngurah Lanang; Sudarma, Made; I Ketut Gede Darma Putra; Oka Sudana; Minho Jo
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9151

Abstract

Obesity is a serious global problem due to its association with various chronic diseases. This study explores the utilization of machine learning in particular deep learning technology to predict Body Mass Index (BMI) from individual photos to create an efficient solution for assessing obesity. Using the ResNet152 model and K-Fold Cross Validation, this application integrates filters on individual photos to improve prediction accuracy. The application was developed using React JS for the front end, PHP and MySQL for the backend and database management, and Python as the core of the machine learning system. The application that tested using blackbox method, to see all features is functioning and the web application prototipe is passed all the test scenario.
BERT Uncased and LSTM Multiclass Classification Model for Traffic Violation Text Classification Komang Ayu Triana Indah; I Ketut Gede Darma Putra; Made Sudarma; Rukmi Sari Hartati; Minho Jo
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 02 (2024): Vol. 15, No. 2 August 2024
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i02.p04

Abstract

The increasing amount of internet content makes it difficult for users to find information using the search function. This problem is overcome by classifying news based on its context to avoid material that has many interpretations. This research combines the Uncased model BiDirectional Encoder Representations from Transformer (BERT) with other models to create a text classification model. Long Short-Term Memory (LSTM) architecture trains a model to categorize news articles about traffic violations. Data was collected through the crawling method from the online media application API through unmodified and modified datasets. The BERT Uncased-LSTM model with the best hyperparameter combination scenario of batch size 16, learning rate 2e-5, and average pooling obtained Precision, Recall, and F1 values of 97.25%, 96.90%, and 98.10%, respectively. The research results show that the test value on the unmodified dataset is higher than on the modified dataset because the selection of words that have high information value in the modified dataset makes it difficult for the model to understand the context in text classification.
Comparative Analysis of Denoising Techniques for Optimizing EEG Signal Processing I Putu Agus Eka Darma Udayana; Made Sudarma; I Ketut Gede Darma Putra; I Made Sukarsa; Minho Jo
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 02 (2024): Vol. 15, No. 2 August 2024
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i02.p05

Abstract

Electroencephalogram (EEG) is a non-invasive technology widely used to record the brain's electrical activity. However, noise often contaminates the EEG signal, including ocular artifacts and muscle activity, which can interfere with accurate analysis and interpretation. This research aims to improve the quality of EEG signals related to concentration by comparing the effectiveness of two denoising methods: Independent Component Analysis (ICA) and Principal Component Analysis (PCA). Using commercial EEG headsets, this study recorded Alpha, Beta, Delta, and Theta signals from 20 participants while they performed tasks that required concentration. The effectiveness of the denoising technique is evaluated by focusing on changes in standard deviation and calculating the Percentage Residual Difference (PRD) value of the EEG signal before and after denoising. The results show that ICA provides better denoising performance than PCA, as reflected by a significant reduction in standard deviation and a lower PRD value. These results indicate that the ICA method can effectively reduce noise and preserve important information from the original signal.
Co-Authors Anak Agung Kompiang Oka Sudana I ketut Gede Darma Putra I Made Sukarsa I Putu Agus Eka Darma Udayana, I Putu Agus Eka Komang Ayu Triana Indah Made Sudarma Made Sudarma Rukmi Sari Hartati Wijayakusuma, I Gusti Ngurah Lanang