https://ojs.unud.ac.id/index.php/lontar/issue/feedLontar Komputer : Jurnal Ilmiah Teknologi Informasi2023-12-29T16:10:36+00:00Ni Kadek Ayu Wirdiani, ST, MTayuwirdiani@unud.ac.idOpen Journal Systems<p style="text-align: justify;"><strong>Lontar Komputer [<a href="http://u.lipi.go.id/1298637487" target="_blank" rel="nofollow">ISSN Print 2088-1541</a>]</strong> <strong>[<a href="http://u.lipi.go.id/1470033684" target="_blank" rel="nofollow">ISSN Online 2541-5832</a>] </strong>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 paper that has not been published and has been through the double-blind reviewed journal. Lontar Komputer is published three times a year by Research institutions and community service, University of Udayana. 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Arif Yudarmawanarifyudarmawan97@gmail.comI.D.A. Manik Mas Astawastinimanikbaliage@gmail.com<p><em>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, </em><em>Employment</em><em>, Payroll and Medical Personnel Fees, Automatic integration with BPJS, EClaim, SATUSEHAT, Finance, and Warehouse and Equipment.</em></p>2023-12-05T00:00:00+00:00##submission.copyrightStatement##https://ojs.unud.ac.id/index.php/lontar/article/view/107396 Computational Parallel on Simulation of Wave Attenuation by Mangrove Forest2023-12-29T16:10:36+00:00Putu Harry Gunawanphgunawan@telkomuniversity.ac.idIrma Palupiirmapalupi@telkomuniversity.ac.idNurul Ikhsanikhsan@telkomuniversity.ac.idIryanto Iryantoiryanto@polindra.ac.idNaila Al Mahmudanmahmuda@algomau.ca<p><em>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, </em><em>which is </em><em>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</em><em>; the</em><em> speedup improves up to a point, with a notable 7.26-fold acceleration observed when utilizing eight threads compared to a single </em><em>line</em><em>.</em><em> Moreover, more than a 10-fold acceleration is observed when the number of threads is greater than 16.</em><em> This underscores the significance of parallelization in exploring mangrove contributions to coastal protection.</em></p>2023-12-05T13:09:52+00:00##submission.copyrightStatement##https://ojs.unud.ac.id/index.php/lontar/article/view/99945 Enhanced Performance of Dynamic Neural Network Model using Wavelet Activation Functions2023-12-29T16:10:36+00:00Syamsul Bahrisyamsul.math@unram.ac.idLailia Awalushaumiawalushaumi@unram.ac.idNurul Fitriyaninurul.fitriyani@unram.ac.id<p><em>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<sup>+</sup> intervention model increased by 4.0492 points.</em></p>2023-12-05T12:57:16+00:00##submission.copyrightStatement##https://ojs.unud.ac.id/index.php/lontar/article/view/108906 Network Reduction Strategy and Deep Ensemble Learning for Blood Cell Detection2023-12-29T16:10:36+00:00I Nyoman Piarsapiarsa.nyoman@unud.ac.idNi Putu Sutramianisutramiani@gmail.comI Wayan Agus Surya Darmasurya@instiki.ac.id<p><em>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.</em></p>2023-12-05T10:21:35+00:00##submission.copyrightStatement##https://ojs.unud.ac.id/index.php/lontar/article/view/106786 The Influence Of Applying Stopword Removal And Smote On Indonesian Sentiment Classification2023-12-29T16:10:36+00:00Arif Bijaksana Putra Negaraarifbpn@untan.ac.id<p><em>Information, like public opinions or responses, can be obtained through Twitter tweets. These opinions can expressed as a sentiment. Sentiments can be positive, neutral, or negative. Sentiment analysis (opinion mining) on a text can performed through text classification. This research aims to determine the influence of implementing Stopword Removal and SMOTE on the sentiment classification model for Indonesian tweets. The algorithms used in this research are Logistic Regression and Random Forest. Based on the evaluation, the best classification model in this research was achieved by implementing the Random Forest algorithm along with SMOTE, with an f1-score value of 75.03%. Meanwhile, implementing the Random Forest algorithm and Stopword Removal achieved the worst classification model, with an f1-score value of 68.09%. Implementing Stopword Removal in both algorithms has a negative impact in the form of a decrease in the resulting f1-score. Meanwhile, the performance of SMOTE provides a positive impact in the form of an increase in the resulting f1-score. This happened since Stopword Removal could reduce information and alter the meaning of processed tweets, causing the tweet to lose its sentiment. </em></p>2023-12-05T00:00:00+00:00##submission.copyrightStatement##