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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, 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
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Quickly Assess the Acceptability Sentiment of White Paracetamol Intake Using KNN-SMOTE Based On Receptive Deciding Rio Andika Malik; Faizal Riza; Sarjon Defitb
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.p05

Abstract

This research aims to develop a fast and adaptive sentiment evaluation approach related to the use of white paracetamol using a combination of the K-Nearest Neighbors (KNN) algorithm, Synthetic Minority Over-Sampling Technique (SMOTE), and the Receptive Deciding concept. Imbalances in the dataset, where positive sentiment may predominate, are addressed using SMOTE to synthesize minority class samples. The KNN algorithm is applied to build a sentiment classification model, while Receptive Deciding is used to provide adaptive intelligence to changes in sentiment. The SMOTE oversampling process is carried out to achieve class balance, while KNN is used to classify sentiment. Receptive Deciding is applied to increase the model's adaptability to changes in sentiment. The research results show that integrating the SMOTE, KNN, and Receptive Deciding methods effectively assesses sentiment accurately and adaptively. The developed model can recognize changes in sentiment over time and provide balanced evaluation results. These findings are expected to contribute to understanding public sentiment towards using white paracetamol and be the basis for developing more effective health communication strategies.
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. 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.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.
Integrated Information System Smart E: Hospital the Innovation and Improvement of the Services and Management Hospital Oka Sudana; Dewa Made Sri Arsa; R. Arif Yudarmawan; I.D.A. Manik Mas Astawastini
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 14 No. 03 (2023): Vol. 14, No. 03 December 2023
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2023.v14.i03.p01

Abstract

The use of technology can transform conventional systems into electronic-based systems. Electronic systems have been widely used in governance, organizations, and companies where administration is carried out electronically (e-Government). Hospitals usually already have systems in place, but they are not yet integrated, including integration with BPJS Services, EClaim, and the SATUSEHAT Platform, a new policy from the Ministry of Health Republic of Indonesia starting July 2022. BPJS integration includes diagnosis standards guided by Minister of Health Regulation No. 76 of 2016 concerning INA-CBGs Technical Guidelines, funds application to BPJS, cost proportions, and medical personnel fees. Another service at the Teaching Hospital is the management of Education for Professional Doctors (Co-ass) and Specialists (Residents). Another service at the Teaching Hospital is the management of Education for Professional Doctors (Co-ass) and Specialists (Residents). The solution provided is to create E-Hospital. It is an integrated hospital management information system with an SSO Model and Multi-Channel Access Technology for notification. This system consists of Front Office Modules, including Admission Queues, Medical Services, Pharmacy, Employment, Payroll and Medical Personnel Fees, Automatic integration with BPJS, EClaim, SATUSEHAT, Finance, and Warehouse and Equipment.
Network Reduction Strategy and Deep Ensemble Learning for Blood Cell Detection I Nyoman Piarsa; Ni Putu Sutramiani; I Wayan Agus Surya Darma
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 14 No. 03 (2023): Vol. 14, No. 03 December 2023
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2023.v14.i03.p04

Abstract

Identifying and characterizing blood cells are vital for diagnosing diseases and evaluating a patient's health. Blood, consisting of plasma and cells, offers valuable insights through its biochemical and ecological features. Plasma constitutes the liquid component containing water, protein, and salt, while platelets, red blood cells (RBCs), and white blood cells (WBCs) form the solid portion. Due to diverse cell characteristics and data complexity, achieving reliable and precise cell detection remains a significant challenge. This study presents a network reduction strategy and deep ensemble learning approaches to detect blood cell types based on the YOLOv8 model. Our proposed methods aim to optimize the YOLOv8 model by reducing network depth while preserving performance and leveraging deep ensemble learning to enhance model accuracy. Based on the experiments, the NRS strategy can reduce the complexity of the YOLO model by reducing the depth and width of the YOLO network while maintaining model performance by 4%, outperforming the baseline YOLOv8 model.
Enhanced Performance of Dynamic Neural Network Model using Wavelet Activation Functions Syamsul Bahri; Lailia Awalushaumi; Nurul Fitriyani
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 14 No. 03 (2023): Vol. 14, No. 03 December 2023
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2023.v14.i03.p03

Abstract

Both static and dynamic adaptive neural networks have been broadly utilized in mathematical modeling and numerical analysis. This study aimed to enhance the accomplishment of Dynamic Neural Networks (DNN) models by applying wavelet functions as activation functions. Research that models and forecasts the intensity of solar radiation in Mataram City shows that combining B-Spline and Morlet wavelet activation functions can significantly increase the DNN model performance. Wavelet-DNN (W-DNN) was modeled with an identical architecture; the best showed the increase in the model achievement (0.7596 points for in-sample and 0.8502 points for out-sample data). Mainly for out-sample data, the model's performance using the W-DNN+ intervention model increased by 4.0492 points.
Computational Parallel on Simulation of Wave Attenuation by Mangrove Forest Putu Harry Gunawan; Irma Palupi; Nurul Ikhsan; Iryanto Iryanto; Naila Al Mahmuda
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 14 No. 03 (2023): Vol. 14, No. 03 December 2023
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2023.v14.i03.p02

Abstract

Coastal ecosystems, specifically mangrove trees, safeguard coastal regions against natural disasters like erosion, floods, and tsunamis. Numerical simulations employing the Shallow Water Equation (SWE), encompassing mass and momentum conservation equations, are used to comprehend how mangroves attenuate wave energy. The SWE incorporates Manning's friction term, which is directly influenced by mangrove forests. However, the SWE's complexity and sensitivity to initial conditions hinder analytical solutions. Despite its increasing computational demands, we utilize the robust staggered grid method to address this challenge. Our study examines mangroves' wave-attenuating effects and introduces a parallel computational model using OpenMP to expedite computations. Findings reveal that mangroves can reduce wave amplitudes by up to 33% when employing a Manning's coefficient of 0.3 within confined basin simulations. Furthermore, our parallel computing experiments demonstrate substantial computation speed enhancements; the speedup improves up to a point, with a notable 7.26-fold acceleration observed when utilizing eight threads compared to a single line. Moreover, more than a 10-fold acceleration is observed when the number of threads is greater than 16. This underscores the significance of parallelization in exploring mangrove contributions to coastal protection.

Page 4 of 4 | Total Record : 36

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