Indonesian Journal of Computer Science最新文献

{"title":"Skin Cancer Segmentation On Dermoscopy Images Using Fuzzy C-Means Algorithm","authors":"Febri Aldi, Sumijan","doi":"10.33022/ijcs.v13i2.3797","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3797","url":null,"abstract":"Millions of people around the world suffer from skin cancer, a common and sometimes fatal disease. Dermoscopy has become an effective diagnostic technique for skin cancer. Precise segmentation is essential for skin cancer diagnosis. Segmentation allows more precise analysis of dermoscopic images by defining the boundaries of the lesion and separating it from surrounding healthy tissue. Dermoscopy images served as a source of research data, and Fuzzy C-Means (FCM) segmentation techniques were used. FCM is a promising method and has received a lot of attention lately. FCM is able to distinguish the various components within the lesion and effectively separate the lesion from the surrounding area. As a result, the distribution of membership degree values of each pixel in the image for each cluster represents the segmentation results obtained through FCM. The FCM technique for segmenting dermoscopic images is expected to significantly improve the precision and effectiveness of skin cancer diagnosis.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"45 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140766930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeepX-Ray: A Comparative Study of Deep Learning-Based Classification and Segmentation Techniques for Automated Detection and Diagnosis of COVID-19 from Chest X-ray Images","authors":"Arna Chakraborty, Arnab Chakraborty, Anisa Nowrin, Abhijit Pathak","doi":"10.33022/ijcs.v13i2.3827","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3827","url":null,"abstract":"The relentless spread of the SARS-CoV-2 virus, causing COVID-19, has underscored the urgent need for efficient early detection and diagnosis methods to mitigate its impact. While traditional techniques like RT-PCR are valuable, they often suffer from time-consuming processes. In this study, DeepX-Ray is presented, a comprehensive investigation into deep learning-based classification and segmentation approaches for the automated detection of COVID-19 from chest X-ray images. Specifically, the authors focus on constructing a custom convolutional neural network (CNN) model to distinguish between COVID-19 and standard X-ray images and benchmark its performance against established models. For segmentation tasks, the effectiveness of various backbone architectures, including ResNet34, ResNet101, DenseNet201, and ResNet50 within the UNet model framework, is explored, with ResNet50 exhibiting superior performance. Furthermore, a novel dataset comprising 7657 images sourced from three publicly available authentic datasets is introduced, and the labelMe tool is employed by the authors to generate ground truth mask datasets for 4137 images to facilitate segmentation of infected lung areas. The authors' custom CNN model achieves an outstanding classification accuracy of 100%, while the segmentation approach attains a mean Intersection over Union (IoU) score of 96.19%. These results underscore the efficacy of the proposed model in enabling early automatic detection and diagnosis of COVID-19, particularly in resource-constrained and remote settings where establishing traditional laboratories may be impractical. This research significantly advances medical imaging techniques for combating the COVID-19 pandemic.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140768508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GEQ and PENS Applications for Analyzing Levels of Player Experience and Satisfaction in Mobile Video Games","authors":"Zicko Muhammad Alrizki, Dedy Kurniawan","doi":"10.33022/ijcs.v13i2.3824","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3824","url":null,"abstract":"Nowadays, video games are getting popular due to the rapid growth of technology, especially mobile technology. Video games are now able to be played on mobile devices whether in single or multiplayer mode by using the internet and it's free and available everywhere. Understanding player experience in mobile video games is essential for developers and researchers. This research evaluates player experience in a mobile video game using the Game Experience Questionnaire (GEQ) and Player Experience Questionnaire (PENS). The evaluation was performed using an online questionnaire form to the participants (N = 110) who had played the specific mobile game. Using the Core Questionnaire from the Game Experience Questionnaire and Player Experience of Need Satisfaction module, this research found that the Game Experience Questionnaire shows high scores values in the aspects of Immersion (4.22), Competence (3.25), and Positive Aspects (4.38) which indicates a good player experience, and the Player Experience of Need Satisfaction shows a rather balanced yet high score for each value that indicates a satisfying player experience.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"60 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140789621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improvement of Data Operations Management using CMMI and DMBOK in Soekarno-Hatta Meteorology Station","authors":"Jihan Nur Ramdhani, Yova Rudelviani, I. M. A. Pradnyadipa, Irwan Shofwan","doi":"10.33022/ijcs.v13i2.3786","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3786","url":null,"abstract":"Soekarno-Hatta Meteorology Station is a Technical Implementation Unit within Badan Meteorologi, Klimatologi, dan Geofisika (BMKG). It has the duties to manage, and process data for safety, regulation, and aviation navigation efficiency by distributing the processed data to their stakeholder, such as Air Traffic Service (ATS), flight operators, and pilots. Furthermore, research was also conducted in the station regarding the creation of Aerodrome Climatological Summary (ACS) annually. Loss of flight document data occurred in 2019 due to a ransomware attack, causing the cessation of the data operation process on the flight document. Unfortunately, there is no data recovery resulting in the unavailability of flight document data. This accident is very crucial because flight document data should not be lost within a 30-day timeframe as it is needed in case of any flight accident mechanism. It shows that Data Operations Management needs to be improved to support the business process. This study aims to evaluate the maturity level of data operations management in Soekarno-Hatta Meteorology Station by a conceptual model based on DAMA-DMBOK and the level of maturity is assessed using the Capability Maturity Model Integration (CMMI). The result showed that the average activity reaches level 2. Six activities are at level 1, namely obtaining externally sourced data, plan for data recovery, set database performance service levels, archive, retain, and purge data, support specialized database inventory and track data technology licenses, support data technology usage and issues. The rest of the activities will require action to improve their maturity level as given in the recommendation.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"229 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140776034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Analysis of XGBoost Performance for Text Classification with CPU Parallel and Non-Parallel Processing","authors":"Omar Ahmed Al-Zakhali, Subhi R. M. Zeebaree, Shavan K. Askar","doi":"10.33022/ijcs.v13i2.3798","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3798","url":null,"abstract":"This paper shows the findings of a study that looks at how CPU parallel processing changes the way Extreme Gradient Boosting (XGBoost) classifies text. XGBoost models can sort news stories into set groups faster and more accurately, with or without CPU parallelism. This is the main goal of the study. The Keras dataset is used to prepare the text so that the TF-IDF (Term Frequency-Inverse Document Frequency) features can be found. These features will then be used to train the XGBoost model. This is used to check out two different kinds of the XGBoost classifier. There is parallelism between one of them and not it in the other. How well the model works can be observed by how accurate it is. This includes both how long it takes to learn and estimate and how well predictions work. The models take very different amounts of time to compute, but they are all pretty close in terms of how accurate they are. Parallel processing on the CPU has made tasks proceed more rapidly, and XGBoost is now better at making the most of that speed to do its task. The purpose of the study is to show that parallel processing can speed up XGBoost models without affecting their accuracy. This is helpful for putting text into categories.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"171 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140777809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing AdaBoost Performance: Comparative Analysis of CPU Parallel Processing on Breast Cancer Classification","authors":"Vaman Ashqi Saeed, Subhi R. M. Zeebaree","doi":"10.33022/ijcs.v13i2.3793","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3793","url":null,"abstract":"The implementation of time-sharing across processes in a real-time way has the potential to increase the execution efficiency of multiprocessor systems like the one described above. The system is able to carry out tasks that make use of a large number of processors in an effective way as a result of this. The aim of this research is to design a system with two primary goals: to enhance accuracy and to minimise the amount of time necessary with processing. This will be accomplished by integrating the ADABoost model with the decision tree algorithm. Furthermore, the statistics unambiguously demonstrate that the accuracy remains the same regardless of whether or not the central processing unit (CPU) makes use of parallel processing, which suggests that there is no variation in parallelization. As a consequence of this, there is a direct connection between the amount of time that is spent and an increase in the amount of parallel processing that is carried out by the central processing unit pertaining to the breast cancer dataset that is being investigated. This research was carried out using Python, which was the programming language that was used for the coding technique that was carried out during the course of its execution.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"36 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140756265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cloud Architectures for Distributed Multi-Cloud Computing: A Review of Hybrid and Federated Cloud Environment","authors":"Karwan Jameel Merseedi, Dr. Subhi R. M. Zeebaree","doi":"10.33022/ijcs.v13i2.3811","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3811","url":null,"abstract":"The concept of several clouds has greatly extended the use of cloud computing and gained popularity in academic and business circles. The use of multi-cloud techniques has increased as businesses use cloud computing more and more to meet their computational demands. A thorough analysis of cloud architectures intended for distributed multi-cloud computing is presented in this study, with an emphasis on federated and hybrid cloud systems. The study looks at the opportunities and difficulties of adopting and overseeing a variety of cloud resources from several providers. The review starts out by going over the basic ideas and reasons for using multi-cloud strategies, emphasizing how important flexibility, scalability, and resilience are in contemporary computing settings. The study then explores the nuances of hybrid cloud architectures, with a focus on how private and public cloud resources can be seamlessly combined. In the context of hybrid cloud installations, important factors including data sovereignty, security, and workload orchestration are covered. In addition, the research delves into federated cloud architectures, clarifying how enterprises can coordinate and oversee workloads across several cloud providers. An examination of resource identification, policy enforcement, and interoperability procedures sheds light on the intricacies of federated cloud computing. The review delves into new developments in standards, best practices, and technology that help multi-cloud ecosystems mature. The study analyses the state of research and industry practices now, pointing out gaps and possible directions for future development. The intention is to provide decision-makers, researchers, and practitioners with a comprehensive grasp of the changing cloud architectural scene so they can plan and execute distributed multi-cloud solutions with knowledge. In conclusion, this article provides a thorough overview of hybrid and federated cloud architectures by combining information from many sources. Through a comprehensive analysis of the difficulties and possibilities associated with multi-cloud computing, the study hopes to add to the current conversation on cloud environment design and optimization in the rapidly changing technological landscape.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"92 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140758956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed Systems for Machine Learning in Cloud Computing: A Review of Scalable and Efficient Training and Inference","authors":"Shereen Sadiq, Subhi R. M. Zeebaree","doi":"10.33022/ijcs.v13i2.3814","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3814","url":null,"abstract":"Traditional computer systems have been pushed to their limits as a result of the exponential rise of data and the rising complexity of machine learning (ML) models. As a result of its on-demand scalability and resource agility, cloud computing has emerged as the platform of choice for training and deploying large-scale machine learning models. However, in order to make good use of cloud resources for machine learning, it is necessary to make use of distributed systems. These systems are responsible for coordinating computations over several nodes in order to manage the demanding workloads. The purpose of this paper is to investigate the realm of distributed systems for machine learning in cloud computing, with a particular emphasis on training and inference that is both scalable and efficient. During the discussion on the need of distributed systems in machine learning, it was made clear why conventional single-machine techniques are not enough for the requirements of current machine learning and how distributed systems might help solve these difficulties. Scalability and Efficiency Considerations were reviewed in relation to the primary elements that contribute to the effectiveness of a distributed system for machine learning. These elements include task partitioning, communication overhead, fault tolerance, and resource optimization that were discussed. In the context of cloud computing, the purpose of this review research is to provide a complete overview of the fascinating topic of distributed systems for machine learning. In order to successfully traverse the intricate and ever-changing world of cloud-based machine learning, it provides vital insights and information.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"311 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140763845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling the Synergistic Relationship between Distributed Systems and Cloud Computing: A Review of Architectural Trends","authors":"Sardar Salih, Subhi R. M. Zeebaree","doi":"10.33022/ijcs.v13i2.3801","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3801","url":null,"abstract":"Cloud providers use distributed systems for scalability, availability, performance, automation, multi-tenancy, and innovation. Distributed cloud computing distributes workload across multiple locations, improving application performance and responsiveness. Significantly potential computational resources are developed in cloud, where large-scale, intricate tasks are performed with the backbone of distribute infrastructure in cloud systems, similar to supercomputing.  Cloud computing development has significantly impacted software development and testing, necessitating applications compatible with the cloud, large data users, and high security. Distributed applications hoist on to cloud platforms where increased efficiency, reliability and low costs are favored and further be stored in the cloud for flexibility and scalability.  Cloud service models include Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS), each offering different application services, programming languages, and hosting environments. The synergistic aspects of Distributed Systems and Cloud Systems with respect to their basic capabilities are discussed and systematically reviewed.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"557 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140757800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data science for energy applications: A Bibliometric Analysis","authors":"Sello Prince Sekwatlakwatla","doi":"10.33022/ijcs.v13i2.3781","DOIUrl":"https://doi.org/10.33022/ijcs.v13i2.3781","url":null,"abstract":"Global digitalization is altering the energy sector, demanding the adoption of data science applications to improve efficiency and innovation, despite the industry's existing data analytics. Data science is revolutionizing the energy and utilities industries, enabling efficient, sustainable, and innovative decision-making through data analysis and smart grid optimization. In the energy industry, organizations are turning to data science to reduce waste, optimize energy usage, and provide alternative energy sources. With the different parts of Africa facing energy crises, different applications are needed to provide a solution. Data science has the potential to provide good information and knowledge that could be used to contribute to energy solutions. To address these concerns, data science models enable utilities to accurately forecast energy demand, enabling efficient generation, distribution, waste reduction, and informed investment decisions by leveraging historical consumption data, weather patterns, and economic indicators. This article aims to explore data science for energy applications. The findings show tools and techniques that can be utilized to provide energy efficiency and energy sustainability through data science applications.","PeriodicalId":52855,"journal":{"name":"Indonesian Journal of Computer Science","volume":"368 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140779644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}