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Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Published by Universitas Udayana
ISSN : 20881541     EISSN : 25415832     DOI : 10.24843/LKJITI
Core Subject : Science,
Computer Science & IT
Lontar Komputer [ISSN Print 2088-1541] [ISSN Online 2541-5832] is a journal that focuses on the theory, practice, and methodology of all aspects of technology in the field of computer science and engineering as well as productive and innovative ideas related to new technology and information systems. This journal covers research original of paper that has not been published and has been through the double-blind reviewed journal. Lontar Komputer published three times a year by Research institutions and community service, University of Udayana. Lontar Komputer already indexing in Scientific Journal Impact Factor with impact Value 3.968. Lontar Komputer already indexing in SINTA with score S2 and H-index 5.
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Articles 226 Documents
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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 1 (2024): Vol. 15, No. 1 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.
Comparison of Gain Ratio and Chi-Square Feature Selection Methods in Improving SVM Performance on IDS Ricky Aurelius Nurtanto Diaz; I Ketut Gede Darma Putra; Made Sudarma; I Made Sukarsa; Naser Jawas
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 15 No 1 (2024): Vol. 15, No. 1 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.p06

Abstract

An intrusion detection system (IDS) is a security technology designed to identify and monitor suspicious activity in a computer network or system and detect potential attacks or security breaches. The importance of accuracy in IDS must be addressed, given that the response to any alert or activity generated by the system must be precise and measurable. However, achieving high accuracy in IDS requires a process that takes work. The complex network environment and the diversity of attacks led to significant challenges in developing IDS. The application of algorithms and optimization techniques needs to be considered to improve the accuracy of IDS. Support vector machine (SVM) is one data mining method with a high accuracy level in classifying network data packet patterns. A feature selection stage is needed for an optimal classification process, which can also be applied to SVM. Feature selection is an essential step in the data preprocessing phase; optimization of data input can improve the performance of the SVM algorithm, so this study compares the performance between feature selection algorithms, namely Information Gain Ratio and Chi-Square, and then classifies IDS data using the SVM algorithm. This outcome implies the importance of selecting the right features to develop an effective IDS.
Fine-Tuned RetinaNet for Real-Time Lettuce Detection Eko Wahyu Prasetyo; Hidetaka Nambo
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 15 No 1 (2024): Vol. 15, No. 1 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.
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 1 (2024): Vol. 15, No. 1 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 sufferers can be done by analyzing chest X-ray images. Pneumonia sufferers experience pleural effusion, where fluid is between the lungs’ layers. It causes the lungs’ X-ray picture to be cloudy or hazy. It differs from the appearance of X-rays on normal lungs which are dark in color. These differences in X-Ray images can be classified automatically with the help of Artificial Intelligence This research used convolutional neural networks and support vector machine methods to recognize X-ray images of pneumonia. This research applied Principal Component Analysis and Wavelet Transformation support to both methods. This research aimed to evaluate the performance of each model combination. The PCA-SVM model gave the best performance, with an accuracy of 94.545% and an F1 score of 94.675%. 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.
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 that is widely used to record the electrical activity of the brain. However, often the EEG signal is contaminated by noise, including ocular artefacts 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, namely 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. Evaluation of the effectiveness of the denoising technique is carried out 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.
Aspect Based Sentiment Analysis on Shopee Application Reviews Using Support Vector Machine Dyah Ayu Wulandari; Fitra A. Bachtiar; Indriati 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).
Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Principal Component Analysis (PCA) for Anomaly Detection Hanna Arini Parhusip; Suryasatriya Trihandaru; Bambang Susanto; Adrianus Herry Heriadi; Petrus Priyo Santosa; Yohanes Sardjono; Johanes Dian Kurniawan
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.
IoT-Based a Control System for Household Waste Management Machines at Waste Disposal Sites using Human Machine Interface Method Pawenary pawenary; Hendri Hendri; Dwi Listiawati; Andi Dyah Harum Hardyanti; Yessy Asri; Anton Satria Prabuwono
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 15 No 03 (2024): Vol.15, No. 3 December 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.i03.p02

Abstract

To manage waste efficiently and sustainably, Integrating Algoritma Bellman-Ford in IoT-Based Control Systems for Household Waste Management Machines, the use of automation technologies based on the Internet of Things (IoT) is becoming increasingly relevant. The integration of HMI allows operators to manage and control the machine with precision and convenience. The synergy between IoT and HMI promises significant improvements in waste processing speed, accuracy, and safety. Developing a control system using the HMI method is not just a solution but an innovative approach that aims to find an effective solution in managing or destroying waste that requires less labor, so there is no need to increase assistance. The method that will be used in this research is the descriptive statistical method, namely, assessing the technical data needed to develop a control system that complies with the standard. This innovative approach is one of the solutions to the problem of labor shortages in landfills that simplify work and speed up the process of operating machines.
C2C Startup Model of Balinese Ceremony Ticketing System in Ubud Bali I Wayan Dharma Suryawan; Ni Wayan Sumartini Saraswati; Eddy Hartono
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 15 No 03 (2024): Vol.15, No. 3 December 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.i03.p05

Abstract

Ubud is one of the tourist destinations in Bali. Ubud combines natural attractions, culture, and spiritual life harmoniously. One of the elements of cultural attraction in Ubud is the implementation of traditional and religious ceremonies that uphold ancestral customs. With this potential, Ubud can be a tourist destination supporting Balinese cultural tourism's progress. Currently, information regarding the implementation of traditional and religious ceremonies in Ubud is limited to tourists. Tourists can witness the traditional and religious ceremonies held through information from tour guides, who are ceremony organizers. The availability of a ceremony ticketing system that connects tourists with traditional village/banjar communities can directly address the problem of access to information and services for the implementation of traditional and religious ceremonies in Ubud. This study aims to develop a C2C e-commerce model that involves klian adat and tourists directly selling tickets for traditional and religious ceremonies in Singakerta Village, Ubud District. Given the limited time for system development, the RAD software development method was selected as the system development method. The user experience test's Likert scale results showed that the system's quality reached 84.13% for tourist satisfaction and 84.19% for klian adat satisfaction. This indicates that the system is at an excellent level based on the user.
The BERT Uncased and LSTM Multiclass Classification Model for Traffic Violation Text Classification Komang Ayu Triana Indah; I Ketut Gede Darma Putra; I 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.

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