cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Dewa Made Sri Arsa
Contact Email
dewamsa@unud.ac.id
Phone
-
Journal Mail Official
lontarkomputer@unud.ac.id
Editorial Address
Research institutions and Community Service, University of Udayana, Kampus Bukit Jimbaran Bali
Location
Kota denpasar,
Bali
INDONESIA
Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Published by Universitas Udayana
ISSN : 20881541     EISSN : 25415832     DOI : 10.24843/LKJITI
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering
Lontar Komputer: Jurnal Ilmiah Teknologi Informasi focuses on the theory, practice, and methodology of all aspects of technology in the field of computer science and engineering. It provides an international publication platform to boost the scientific and academic publication of research in the field. Submissions are invited concerning any theoretical or practical implementation of algorithm design, methods, and development. The subject of articles contributed may cover, but is not limited to: Data Analysis Natural Language Processing Artificial Intelligence Neural Networks Pattern Recognition Internet of Things (IoT) Remote Sensing Image Processing Fuzzy Logic Genetic Algorithm Bioinformatics/Biomedical Applications Biometrical Application Computer Network and Architecture Network Security Content-Based Multimedia Retrievals Information System
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 36 Documents
 1   2   3   4 
Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Principal Component Analysis (PCA) for Particulate Matter (PM) Anomaly Detection Hanna Arini Parhusip; Suryasatriya Trihandaru; Bambang Susanto; Johanes Dian Kurniawan; Adrianus Herry Heriadi; Petrus Priyo Santosa; Yohanes Sardjono
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.p01

Abstract

This research addresses a critical issue in industrial environments: air quality, specifically regarding PM 1.0 and PM 2.5. High concentrations of these particles pose significant health risks. The study measures temperature, humidity, pressure, altitude, PM 1.0, and PM 2.5 and shows the effectiveness of using AIOT-Particle devices to analyze these features with Principal Component Analysis (PCA). Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to detect anomalies during the observation period. Anomalies occur when the altitude ranges from 65 to 70 units, according to PM 1.0 and PM 2.5 values. The positions where anomalies occur are illustrated based on altitude, temperature, pressure, and concentration. The results demonstrate that altitude dominates as the first feature. Finally, the research concludes that altitude, PM 1.0, and PM 2.5 are the dominant features. The study confirms the effectiveness of PCA and recommends using these three features for anomaly detection in DBSCAN. Overall, the research highlights the novelty and success of AIOT-Particle in industrial environments.
Deep Learning Implementation Using CNN to Classify Bali God Sculpture Pictures Ni Luh Gede Pivin Suwirmayanti; I Made Budi Sentana; I Ketut Gede Darma Putra; Made Sudarma; I Made Sukarsa; Komang Budiarta
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.p02

Abstract

Efforts to preserve Balinese culture can be carried out by integrating art and technology as new steps that need to be developed. This research is motivated by the existence of various forms of God statues which have a central role in Balinese culture. The Deep Learning method is proposed because it has unique features that can be extracted automatically. The technique used in Deep Learning is Convolutional Neural Network (CNN). The training process is first performed to perform the classification process, and then the testing process is performed. We compared our CNN model with two other models, AlexNet and ResNet, based on the experimental results. Using a data split of 70%- 30%, our CNN model has the highest accuracy in managing statue image data. Specifically, our model achieves 97.14% accuracy, while Alexnet and Resnet achieve 24.44% and 33.33%, respectively. Apart from contributing to introducing the Balinese God Statue, this research can also be a basis for developing more comprehensive applications in culture and tourism.
Aspect Based Sentiment Analysis on Shopee Application Reviews Using Support Vector Machine Dyah Ayu Wulandari; Fitra Abdurrachman Bachtiar; Indriati
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.p03

Abstract

One of the e-commerce in Indonesia is Shopee. Feedback from users is needed to improve the quality of e-commerce services and user satisfaction. This research process includes data scraping, labeling, text pre-processing, TF-IDF, aspect, and sentiment classification. The novelty of this research is using the SVM method with SGD to classify Indonesian language application reviews based on aspect categories consisting of 7 dimensions of service quality and sentiment so that the website created in this research can display the aspects and sentiments of the input reviews. This research also builds an Indonesian normalization dictionary to optimize the terms used to increase model accuracy. The test in aspect classification resulted in a precision value of 90%, recall of 88.73%, accuracy of 88.57%, and f1-score of 89%. Meanwhile, the sentiment classification resulted in a precision value of 96.15%, recall of 91.91%, accuracy of 94.28%, and f1-score of 93.98%. In addition, the test results (accuracy, f1-score, precision, recall) show that the lemmatization process is better than stemming and term weighting using the TF-IDF method is better than other methods (raw-term frequency, log-frequency weighting, binary-term weighting).
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.
Comparative Analysis of YOLOv8 and HSV Methods for Traffic Density Measurement I Gede Pasek Suta Wijaya; Muhamad Nizam Azmi; Ario Yudo Husodo
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.p06

Abstract

Traffic density measurement is a critical component in traffic management and urban planning. This study addresses the challenge of accurately measuring traffic density by comparing the performance of the YOLOv8 segmentation method with the traditional HSV method. At the beginning of the abstract, we clearly present the problem of accurately measuring traffic density. The primary objective is to highlight the strengths and limitations of each method in terms of accuracy and reliability in traffic density estimation.The choice of segmenting the asphalt area rather than vehicle objects is justified by the need to understand how different segmentation approaches affect traffic density measurements. The HSV method involves converting images to the HSV color space, creating masks for specific areas, and measuring traffic density based on the asphalt area. This method, while straightforward, may not accurately capture the dynamic nature of vehicle movement. In contrast, the YOLOv8 segmentation method utilizes a deep learning approach to detect and segment vehicles, providing potentially more precise measurements. Experimental results from three locations demonstrate varying levels of traffic density. The YOLOv8 method results in a graph with a wavy pattern, reflecting the more detailed detection of vehicles. Conversely, the HSV method produces a linear pattern, indicating a more consistent but potentially less detailed measurement. Quantitative analysis shows that Location 2 has a higher traffic density compared to Locations 1 and 3, as indicated by the average number of detected vehicles per frame. This study provides a comprehensive understanding of the differences between HSV and YOLOv8 segmentation methods for traffic density measurement. The findings suggest that while YOLOv8 offers more detailed and dynamic detection, the HSV method provides a simpler yet effective approach for certain applications.
Utilization of Augmented Reality Technology in Independent Speech Therapy Applications Linda Perdana Wanti; Oman Somantri; Titin Kartiyani
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 01 (2024): Vol. 15, No. 01 April 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.i01.p01

Abstract

One of the uses of information technology is augmented reality technology in the health sector. Augmented reality is used in the development of applications that are used for speech therapy for children with autism or children with speech delays. The method used for the development of speech therapy applications is the extreme programming method. This method can adapt to the development of an application in a short time and quite a lot of changes. The stages in the extreme programming method include identifying system requirements, planning activities during system/application development, system development process, iteration for system improvement until the final iteration, and no more user feedback, system/application production, and system maintenance with data backup and system recovery. After testing the system, it was concluded that three iterations occurred during the development of the speech therapy application. The last test showed that the user accepted the speech therapy application with a percentage of 77,14%. The output of this research is an augmented reality-based speech therapy application that is useful for independent speech therapy for children with speech delays.
Fine-Tuned RetinaNet for Real-Time Lettuce Detection Eko Wahyu Prasetyo; Hidetaka Nambo
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 01 (2024): Vol. 15, No. 01 April 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.i01.p02

Abstract

The agricultural industry plays a vital role in the global demand for food production. Along with population growth, there is an increasing need for efficient farming practices that can maximize crop yields. Conventional methods of harvesting lettuce often rely on manual labor, which can be time-consuming, labor-intensive, and prone to human error. These challenges lead to research into automation technology, such as robotics, to improve harvest efficiency and reduce reliance on human intervention. Deep learning-based object detection models have shown impressive success in various computer vision tasks, such as object recognition. RetinaNet model can be trained to identify and localize lettuce accurately. However, the pre-trained models must be fine-tuned to adapt to the specific characteristics of lettuce, such as shape, size, and occlusion, to deploy object recognition models in real-world agricultural scenarios. Fine-tuning the models using lettuce-specific datasets can improve their accuracy and robustness for detecting and localizing lettuce. The data acquired for RetinaNet has the highest accuracy of 0.782, recall of 0.844, f1-score of 0.875, and mAP of 0,962. Metrics evaluate that the higher the score, the better the model performs.
Sentiment Analysis of Indonesian YouTube Reviews About Lesbian, Gay, Bisexual, and Transgender (LGBT) using IndoBERT Fine Tuning Teddy Oswari; Murniyati; Trityanti Yusnitasari; Nurasiah; Seviyanti Wijay
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 01 (2024): Vol. 15, No. 01 April 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.i01.p03

Abstract

Lesbian, gay, Bisexual, and Transgender (LGBT) is an individual who has a sexual orientation or gender identity that is different from the heterosexual majority. The LGBT community now dares to appear openly on social media; nowadays, social media is used as a source of information and a place to provide comments. The Indonesian state generally still views the LGBT community as deviant behavior. This research was conducted to understand Indonesian society's views on LGBT through YouTube and social media. The text mining method analyzes and classifies the counter or pro sentences expressed in the comments. The model used in this research is IndoBERT because the research object studied is Indonesian. IndoBERT is part of the Bidirectional Encoder Representation From Transformers (BERT) model. The data sources used were 1,493 data. The stages carried out in this research included the preprocessing stage, which included case folding, data cleaning, tokenization, stopword removal, stemming, and normalization, then the data labeling stage, and finally, the model building stage with IndoBERT Fine Tuning. The level of accuracy achieved using IndoBERT is 74%.
Comparative Analysis of SVM and CNN for Pneumonia Detection in Chest X-Ray Ni Wayan Sumartini Saraswati; Dewa Ayu Putu Rasmika Dewi; Poria Pirozmand
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 01 (2024): Vol. 15, No. 01 April 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.i01.p04

Abstract

Recognizing pneumonia can be done by analyzing chest X-rays. Pneumonia sufferers experience pleural effusion, fluid between the lungs’ layers. It causes the lungs’ X-ray picture to be cloudy. It differs from the X-rays on normal lungs, which are dark. This difference is the characteristic of the data so that it can be classified. Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) were employed in this study to identify pneumonia in X-ray images. SVM optimizes the hyperplane to separate data classes, while CNN uses convolution and pooling layers to learn patterns in the image. The data are obtained from General Hospital Ganesha Gianyar Bali and research by J.P. Cohen et al. CNN has several capabilities, such as automatic feature extraction, divided parameters, position invariance, and good generalization, so that it can classify limited data. This research applied Principal Component Analysis (PCA) and Wavelet Transformation to support both methods. The PCA-SVM model gave the best performance. The SVM model outperforms the CNN model in recognizing images; in this case, it could be due to the relatively small amount of training data.

Page 3 of 4 | Total Record : 36

 1   2   3   4 

Filter by Year

2023 2025


Reset
Filter By Issues
All Issue Vol. 16 No. 02 (2025): Vol.16, No. 02 August 2025 Vol. 16 No. 01 (2025): Vol.16, No. 01 April 2025 Vol. 15 No. 03 (2024): Vol. 15, No. 03 December (2024) Vol. 15 No. 01 (2024): Vol. 15, No. 01 April 2024 Vol. 15 No. 02 (2024): Vol. 15, No. 2 August 2024 Vol. 14 No. 03 (2023): Vol. 14, No. 03 December 2023