Sinkron : jurnal dan penelitian teknik informatika
Not a member yet
1261 research outputs found
Sort by
Image Augmentation for BreaKHis Medical Data using Convolutional Neural Networks
No full textIn applying Convolutional Neural Network (CNN) to computer vision tasks in the medical domain, it is necessary to have sufficient datasets to train models with high accuracy and good general ability in identifying important patterns in medical data. This overfitting is exacerbated by data imbalances, where some classes may have a smaller sample size than others, leading to biased predictive results. The purpose of this augmentation is to create variation in the training data, which in turn can help reduce overfitting and increase the ability of the model to generalize. Therefore, comparing augmentation techniques becomes essential to assess and understand the relative effectiveness of each method in addressing the challenges of overfitting and data imbalance in the medical domain. In the context of the research described, namely a comparative analysis of augmentation performance on CNN models using the ResNet101 architecture, a comparison of augmentation techniques such as Image Generator, SMOTE, and ADASYN provides insight into which technique is most suitable for improving model performance on limited medical data. By comparing these techniques' accuracy, recall, and overall performance results, research can identify the most effective and relevant techniques in addressing the challenges of complex medical datasets. This provides a valuable guide for developing better CNN models in the future and may encourage further research in developing more innovative augmentation methods suitable for the medical domain
Satellite Images Classification using MobileNet V-2 Algorithm
No full textSatellite imagery is an invaluable source of visual information for environmental monitoring and land mapping with high resolution and wide coverage. In this modern technological era, advances in Deep Learning technology have brought great benefits in utilizing satellite images for various purposes. One of the efficient Deep Learning models for satellite image classification is MobileNet V-2, which is specifically designed for devices with limited resources such as smartphones. This study aims to develop an accurate satellite image classification model using Convolutional Neural Network algorithm and MobileNet V-2 model. The data used is taken from the RSI-CB256 dataset developed through crowdsourcing data. This research resulted in the performance of three deep learning models, namely ResNet50, MobileNet V-2, and VGG-16. ResNet50 is the highest model performed best during the training phase, achieve an accuracy of 98.40%. MobileNet V-2 and VGG-16 followed with 95.64% and 96.62% accuracy, respectively. The evaluation results demonstrate the model's strong ability to accurately classify satellite imagery and strengthen the model's ability to generalize well. With high accuracy and the ability to run on smartphone devices, this model has the potential to provide valuable information for governments and scientists in preserving the earth and better responding to environmental changes
Ambon Banana Maturity Classification Based On Convolutional Neural Network (CNN)
No full textThe banana (Musa paradical), is an excellent fruit produced nationally and high in vitamins. In Indonesia, banana production is at a higher level than other fruit products. However, one of them is the issue with bananas' post-harvest, which arises when they are produced in huge quantities on a large scale or by an industry that sorts bananas. So far, the determination of the maturity level of bananas is done by relying on visual analysis limited to the color of the skin by the human eye. However, this identification approach has several drawbacks. First, this method requires significant effort in the banana sorting process. In addition, the perception of the fruit's maturity level can vary, because humans can experience fatigue and lack of consistency in judgment. In addition, human judgment is also influenced by subjective factors that can affect the final result. Considering this problem, developed a system to classify the ripeness level of Ambon bananas. This system utilizes image enhancement features to increase contrast, which is implemented using a Convolutional Neural Network (CNN). The classification process is carried out through image processing using MATLAB R2022a software, which forms the basis of a classification system with 4 classes which include 486 images of unripe Ambon bananas, 235 images of half-ripe Ambon bananas, 309 images of perfectly ripe Ambon bananas, 184 images of rotten Ambon bananas. The dataset analyzed in this study totaled 1214 data divided into 1093 training data and 121 test data. The CNN method is used in this data classification, and the results show an accuracy rate of 95.87%
Mobile Apps-Based Cosmetic Equipment Selection Decision Support System Use Simple Additive Weighting (SAW) Method
No full textCurrently, more and more types of cosmetic products are appearing on the market, thus making cosmetic users confused in choosing cosmetics that suit their skin type and usage. Not a few people who use cosmetics wrongly which has a lot of bad effects on their face and has to be touched up repeatedly. This application facilitates users with 5 types of skin types, lip types and types of use that can be selected to make it easier for users to choose cosmetics that suit their skin type and usage. The application was built with a Decision Support System (DSS) using the Simple Additive Weighting (SAW) method to make calculations for selecting the most appropriate Make Over cushion and lipstick product for the user. In this application, the user can enter their skin type and then the system will calculate using the SAW method to get the Make Over cushion and lipstick that is most suitable for the user. The final result of the system will display the Make Over cushion and lipstick and their description with the highest calculated value to the user. Based on testing, this application is able to display cushion and lipstick products whose compatibility with users reaches 98%
Information System for Monitoring Production Process of Dried Kelor Leaf Dried Using the FAST Method
No full textMoringa or Kelor leaves, rich in nutrients and health benefits, are used in many culinary, supplement, and medicinal items. However, drying moringa leaves is a crucial step that impacts product quality. Companies must maintain product quality and production efficiency to meet rising demand. Since moringa leaf drying production management is difficult, this study uses the Framework for the Application System Thought (FAST) method. Its use in moringa drying allows thorough monitoring of temperature, humidity, drying duration, and other product quality factors. According to this research, using the FAST method in the moringa leaf drying production management monitoring application will help identify production issues, prevent product damage, and improve product quality. This research improves moringa production management and helps explain FAST method implementation in industrial process management. FAST is significant for monitoring applications because it can continually monitor all production conditions that affect drying moringa leaves. FAST can immediately detect dryer humidity issues. The FAST technique and moringa drying production management monitoring applications can be used to improve product quality, operational efficiency, and consumer safety in this research. Thus, this research gives tangible answers for the moringa processing business and can be applied to other industrial sectors facing comparable production process management issues
Performance Comparison ConvDeconvNet Algorithm Vs. UNET for Fish Object Detection
No full textThe precise identification and localization of fish entities within visual data is essential in diverse domains, such as marine biology and fisheries management, within computer vision. This study provides a thorough performance evaluation of two prominent deep learning algorithms, ConvDeconvNet and UNET, in the context of fish object detection. Both models are assessed using a dataset comprising a wide range of fish species, considering various factors, including accuracy of detection, speed of processing, and complexity of the model. The findings demonstrate that ConvDeconvNet exhibits superior performance in terms of detection accuracy, attaining a noteworthy degree of precision and recall in identifying fish entities. In contrast, the UNET model displays a notable advantage in terms of processing speed owing to its distinctive architectural design, rendering it a viable option for applications requiring real-time performance. The discourse surrounding the trade-off between accuracy and speed is examined, offering valuable perspectives for algorithm selection following specific criteria. Furthermore, this study highlights the significance of incorporating a diverse range of datasets for training and testing purposes when utilizing these models, as it significantly influences their overall performance. This study makes a valuable contribution to the continuous endeavors to improve the detection of fish objects in underwater images. It provides a thorough evaluation and comparison of ConvDeconvNet and UNET, thereby assisting researchers and practitioners in making well-informed decisions regarding selecting these models for their specific applications
Automation of Two Ubuntu Servers with Ansible and Telegram as Notifications
No full textThe purpose of this study was to determine the application of the server configuration automation system using Ansible tools on Ubuntu Server. This study describes a descriptive qualitative research that examines a problem based on what is observed through certain media. The stages of research carried out in designing Automation of Two Ubuntu Servers with Ansible and Telegram as Notifications. The results of this study display two execution results, namely the status of Success and Ended. Ended state is caused by unreacable or unconnected host. The suggestion for the further development of this research is that the application of the automation system can also be carried out on the Windows Server operating system, not only on the Ubuntu Server
Utilization and development of Smart Mosque Applications
No full textMosque is pointing to a place (building) whose main function is as a place of prostration and worship of Allah SWT. Mosque digitization is one form of mosque adaptation to technological developments towards digital. This of course will be able to improve the service of the mosque to the congregation. However, many mosques have not moved towards digitization. A system for managing data and information, such as: mosque activities, congregational data, financial reports, and so on. Currently, there are quite a lot of user acceptance model theories for an application. One of them is the model of user satisfaction. Data collection is done by using a survey of user satisfaction with the application. The methods used in research in the development and implementation of networks and digitalization in terms of questions and answers to interviews with several mosque DKM administrators by the development team are: at the Nurul Hidayah mosque, Al Ikhlas Pademangan mosque, Attaqwa Grand Mosque, Asy Syam Mosque, Jami Al Manar Mosque, Jami Al-Hidayah Mosque, Baitussalam Mosque (Bogor), and Al-Ikhlas Mosque, all of which are still in the Jakarta area. As well as the results of the questionnaire that was made, there were Questionnaire Results of the System Usability Scale (SUS) Testing for the Smart Masjid application regarding customer satisfaction, which was carried out by survey research through a questionnaire form and interview. The results of the average answer to the questions given are 80% - 85% Answering Agree and Strongly Agree, the rest only partially answered Doubtful and Disagree
The Comparison of Accuracy on Classification Climate Change Data with Logistic Regression
No full textMachine learning methods can be used to generate climate change models. The goal of this study is to use logistic regression machine learning algorithms to classify data on greenhouse gas emissions. The data used is climate change data of several countries obtained from The World Bank, with total greenhouse gas emissions as the response variable and 61 other attributes as explanatory variables. This data is preprocessed using min-max normalization to handle unbalanced ranges, and then the data is split into 70% training data and 30% testing data. Based on the logistic regression modeling, it was discovered that the data from the min-max transformation resulted in better modeling than the data modeling without the transformation process. The accuracy, precision, sensitivity, and specificity of the transformation are 87.60%, 87.76%, 87.04%, and 88.14%, respectivel
Android-Based Doctor E-Consultation Application Using Scrum
No full textThe standard service hospitals provide for their patients is a doctor's consultation. However, many patients need help accessing this service due to having to go to the hospital, queuing, and long waiting times. So that patients no longer have difficulty accessing consulting services, we need a system that can speed up consulting activities. The system development method used in Android-based e-consulting applications is the Scrum method. The system development process is somewhat accelerated using the Scrum methodology because all development activities require planning and timing for each sprint, starting with sprint planning, continuing through the daily scrum, sprint review, and sprint retrospective, which is repeated at each sprint to avoid delays in application development. By offering consultation services online, this system allows patients to access existing facilities and more efficiently supports the improvement of consulting services at existing hospitals, especially in Indonesia