Sinkron : jurnal dan penelitian teknik informatika
Not a member yet
1261 research outputs found
Sort by
Computer Vision-Based Intelligent Traffic Surveillance: Multi-Vehicle Tracking and Detection
No full textThe application of vehicle detection in real-time traffic surveillance systems is one of the challenging research fields with different objectives. One of the problems is the detection of many vehicles simultaneously in a video sequence sourced from CCTV cameras. In many works, the focus is only on detecting vehicle classes such as motorcycles, buses, trucks, and cars or special vehicles such as ambulances and others. In this research, we propose to apply 13 classes of vehicle types and implement YOLOv4 in the traffic surveillance task. More specifically, all classes are labeled, and then the YOLOv4 model is trained on 800 images and tested on 23 videos from three intersections in Medan City, namely Juanda Katamso Intersection, Gatot Subroto Intersection, and Uniland Intersection. Based on the test results, YOLOv4 proves successful in detecting many vehicles in frame-by-frame sequence with various types of vehicles. All vehicle detection data will be stored in the file
Comparison PSO And IWPSO Performance In Optimizing Decision Tree Algorithm On Heart Disease Dataset
No full textHeart disease, one of the most common and potentially fatal chronic diseases, has become a major focus in global health efforts. In this study, researchers used the decision tree algorithm on the heart disease dataset with the stages of the decision algorithm including the EDA, Split Data, and Decision tree modeling stages. Furthermore, hyperparameters use PSO and IWPSO to optimize the algorithm. The purpose of this research is to analyze the performance of Particle Swarm Optimization (PSO) and Inertia Weight Particle Swarm Optimization (IWPSO) in heart disease prediction based on relevant datasets. PSO and IWPSO were applied to the heart disease dataset, with the results showing an accuracy rate of 78% for PSO and 84% for IWPSO. These results indicate that IWPSO provides significant performance improvement compared to PSO in the context of heart disease prediction. The implications of these findings can support the development of more efficient prediction systems for early detection of heart disease, making a positive contribution to prevention efforts and further treatment of this critical health condition. In addition, the purpose of this research is to continue research in the form of C4.5 on heart disease with a result of 80.43%. In this study, IWPSO got the best accuracy of 84.23% greater than previous research. The results of this study are to provide insight that PSO and IWPSO hyperparameters can optimize decision trees in handling heart disease datasets and continue research
Development of a Web-Based Alumni Information System at Universitas Hindu Indonesia
No full textThe development of an Alumni Information System based on a Website is an effective solution in managing information and data regarding an institution's alumni. Issues related to non-systemic and manual information dissemination, as well as challenges in gathering alumni data, are expected to be resolved by this system. It is anticipated that this system will facilitate alumni in connecting and interacting. The aim of this research is to develop an effective and efficient alumni information system to enhance alumni engagement and participation in institutional activities. The research follows a waterfall model involving various stages, starting from needs analysis, design, implementation, testing, to maintenance. The developed alumni information system includes features such as alumni profiles, current news and information, job vacancies, and alumni activities. This system is implemented in the form of a website using the CodeIgniter framework. Testing results using black box testing indicate that this system effectively manages various data and information crucial for alumni. Alumni using this system can easily access and update their profile information, as well as connect with fellow alumni and the institution. For future research, it is hoped that a more flexible information system can be developed, perhaps in the form of a mobile-based application
Stunting Disease Classification Using Multi-Layer Perceptron Algorithm with GridSearchCV
No full textStunting is a growth and development disorder caused by malnutrition characterized by a child's height less than the standard deviation set by WHO. In 2022, stunting cases in Indonesia are considered a high prevalence rate, reaching 21.6%. There are several factors that can cause stunting in children, namely maternal and antenatal care factors, home environment factors, breastfeeding practices, and feeding factors during toddlerhood. There are several impacts that occur when children are stunted, namely increased risk of child mortality, susceptibility to illness, impaired brain development, physical disorders and metabolic disorders. Currently, deep learning has been widely used for disease classification and prediction, one of the deep learning methods is Multi-Layer Perceptron (MLP). The purpose of this research is to classify stunting disease using a deep learning method, namely MLP. The dataset used consists of 8 attributes, namely gender, age, birth weight, birth length, body weight, body length, breastfeeding and stunting with a total of 10,000 records. The encoding process is carried out to convert categorical data into numeric attributes of gender, breastfeeding, and stunting. This research produces a higher accuracy value than previous research which used the C4.5 algorithm with an accuracy of 61.82%, whereas in this study using MLP which was integrated with the GridSearchCV hyperparameter it obtained an accuracy of 82.37%. This proves that the MLP method is successful in classifying stunting compared to previous research algorithms
Model Performance Evaluation: VGG19 and Dense201 for Fresh Meat Detection
No full textTo guarantee consumer safety and meet quality expectations, accurate detection of meat quality is a critical component of the food industry. The objective of this research endeavor is to assess and contrast the fresh meat detection capabilities of two distinct artificial neural network architectures, denoted as Dense201 and VGG19. Automated systems that can identify vital qualities in fresh meat, including color, texture, and cleanliness, have become feasible due to the development of image processing technology. For this reason, however, there are still few direct comparisons between various architectures of artificial neural networks, particularly VGG19 and Dense201. Comparing and contrasting the performance of both models in identifying the quality of meat from visual images, this study attempts to fill this void. Utilizing a vast dataset containing a variety of fresh meats exhibiting substantial visible variations constituted the research methodology. The assessment was conducted by examining the efficacy of both models in determining the quality of meat using established performance metrics, including accuracy, precision, recall, and F1-score. Regarding the detection of fresh meat, it is anticipated that the findings of this study will offer a comprehensive understanding of the benefits and drawbacks associated with every artificial neural network architecture. Contributing to a greater comprehension of the application of precise and efficient meat detection technology, this study also furnishes the food industry with a foundation for determining which model best meets the requirements of meat quality detection on a larger production scale
Comparison of CNN and SVM Methods on Web-based Skin Disease Classification Process
No full textSkin, as the outermost layer of the body, is often in contact with bacteria, germs and viruses because of its most external position. According to statistics from the 2009 Indonesian Health Profile, skin illness is the third most common ailment seen in outpatient settings across the country's hospitals. Therefore, maintaining healthy skin is important because it protects the body's internal organs from injury and attack by pathogens. The development of image classification, such as the classification of skin diseases, has become a focus in the health sector. This research analyses the performance of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) in web-based skin disease classification and overcomes the problem of imbalanced training data. With data augmentation and preprocess, this research improves data generalization and compares performance metrics such as Recall, Accuracy, and F1 Score. The results show that the average accuracy of CNN is 83.8%, while SVM reaches 81%. Although both models have high metrics for the normal class, other more complicated classes can only be handled by CNN with a value of more than 0.9. Apart from that, the CNN method also provides a higher Confidence Score than SVM, as well as a faster execution time. In conclusion, the CNN method is superior and recommended for skin disease classification based on web applications based on various performance test results
Analysis of TF-IDF and TF-RF Feature Extraction on Product Review Sentiment
No full textSentiment analysis of product reviews is critical in understanding customer views and satisfaction, especially in the context of e-commerce applications. A marketplace provides channels where users can submit reviews of the products they purchase. However, due to the large number of reviews in a marketplace, analyzing them is no longer feasible to be performed manually. This research proposes a machine learning implementation to perform sentiment analysis on product reviews. In this research, the product review dataset on Shopee marketplace is used for sentiment analysis by comparing TF-IDF and TF-RF feature extraction using the SVM algorithm with stages of dataset, labeling, feature extraction and accuracy results. The importance of the comparison between TF-IDF and TF-RF feature extraction in this research is related to the need to evaluate and determine which feature extraction method is most effective in increasing the accuracy of sentiment analysis. TF-IDF and TF-RF are two methods commonly used in text analysis, and a comparison of their performance can provide deep insight into the effectiveness of each in the context of product sentiment analysis.Thus, through this comparison, this research aims to determine the best approach that can provide the highest accuracy results, so that the results can serve as a guide for further research. Based on the evaluation, the highest accuracy value is achieved at 92.87% by using TF-IDF and SVM classifiers which outperformed previous research
Advancing Fruit Image Classification with State-of-the-Art Deep Learning Techniques
No full textFruit image classification technology using deep learning is making significant contributions in the agriculture and food retail sectors, promising to increase efficiency and productivity. However, there is an identified knowledge gap in dealing with the considerable variation in fruit appearance caused by factors such as type, size, color, and lighting conditions, as well as the precise identification of damage or disease. This research focuses on applying the developed Convolutional Neural Network architecture to fill this gap by using it in an extensive and diverse dataset, covering 67,692 image files categorized into 131 fruit classes. The training process showed substantial accuracy improvement, with training accuracy reaching 98.39% and validation accuracy at 90%, while training loss decreased to 0.0430 and validation loss to 0.2991. In the advanced stage of training, the training accuracy peaked at 99.43% in the 59th epoch with a shallow loss of 0.0251. However, the validation loss showed variation, indicating room for improvement in model generalization. The findings provide insight into the potential and challenges of applying Convolutional Neural Network models and fruit image classification with improved fruit sorting accuracy. Contribution to the literature in the field of information technology and agriculture by showing deep learning models can be improved to address the issue of fruit image variability
Breast Cancer Classification Through CT Scan Using Convolutional Neural Network (CNN)
No full textA common disease suffered by Indonesian women is breast cancer. Early awareness of breast cancer is very important to minimize the negative impact and increase the chances of recovery for breast cancer patients. Breast cancer detection efforts using CT scan image technology. CT scan images provide a detailed picture of the internal structure of the breast, allowing the identification of pathological changes that may be early signs of breast cancer. The purpose of the study is to utilize CNN algorithm for breast cancer classification using CT scan images. The dataset used consists of three labels namely benign cancer, malignant cancer, normal. The three data sets consist of 1096 data. CNN is a type of algorithm in the field of artificial intelligence that has proven successful in pattern recognition on image data. The collected breast CT scan image dataset includes breast cancer and non-breast cancer cases. The data is used to train and test the CNN model. Furthermore, breast cancer classification through CT scans is carried out by applying the CNN method. The results of the research conducted obtained an accuracy of 97.26%. In Benign classification with precision 0.99 (99%), recall 0.96 (96%), f1-score 0.98 (98%), support 186, then Malignant classification with precision 93% or with points 0.93, recall 98% with points 0.98, and f1-score 96% with points 0.96, and support 202. The last is the normal classification with 99% precision with 0.99 points, 97% recall with 0.97 points, 98% f1-score with 0.93 points, and 269 support
Extreme Learning Machine and Multilayer Perceptron Methods for Predicting COVID-19
No full textThe number of positive COVID-19 cases in Semarang City has increased over the last year. In anticipating and preparing proper health facilities, the government must predict the number of cases. This research applies Extreme Learning Machine (ELM) and Multilayer Perceptron (MLP) to indicate the number of positive COVID-19 cases. These newly developed methods are part of Artificial Neural Network (ANN). The type of data used in the study is secondary data. Covid-19 patient data was taken from the Semarang City Health Office. The data on the number of positive Covid-19 cases used is data from April 9, 2020 to December 15, 2022. The prediction results of the ELM and MLP methods were then compared to determine which method was more effective in predicting the number of positive Covid-19 cases. The results of the study showed that both methods had an error of less than 10%, meaning that both methods were feasible for predicting the number of positive Covid-19 cases. However, based on the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) values, the MLP method had a smaller error rate than the ELM method. In predicting the number of COVID-19 positive cases, ELM has 93.436331% accuracy, and MLP has 97.055838% accuracy. The best method for predicting the number of COVID-19 positive cases in Semarang City is Multilayer Perceptron (MLP)