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The relationship between mother's knowledge and the incidence of stunting in toddlers in polindes, mundar village, south labuan amas district, hulu sungai tengah regency in 2024
No full textStunting is stunted growth caused by a lack of nutritional intake due to a diet that does not meet long-term needs. South Kalimantan Province is in the top 3 provinces with the highest reduction in stunting (-5.4%) in 2022 based on the SSGI results released by the Indonesian Ministry of Health. Initially in 2021 stunting in South Kalimantan reached 30.0% and in 2022 it decreased by 24.6%. With a figure of 24.6%, reducing stunting is still a priority for South Kalimantan to achieve the target of 14% in 2024. Based on data from the Hulu Sungai Tengah district health service in 2020, the figure was 10%, experiencing a decrease in 2021 with a figure of 9.44%, and in in 2022 there will be an increase of 31.10%. This research is analytical research. With a Cross Sectional approach. The research results showed that the majority of mothers had sufficient knowledge and 21 children were not stunted (33.9%), 9 children were stunted (14.5%), and there was insufficient knowledge with the incidence of stunted children being 13 people (21.0%). and there were 2 children (3.2%) who were not stunted, while there were 0 people (0%) with good knowledge and stunted babies, and 17 people (27.4%) who were not stunted. Based on the results of the chi square test, it was found that there was a relationship between maternal knowledge and the incidence of stunting at the Mundar Village Polindes with a P value = 0.000. It is recommended that the Mundar Village Polindes, South Labuan Amas District, Hulu Sungai Tengah Regency further increase health education activities related to health and education for mothers with babies and toddlers about preventing stunting through improving child nutrition
Squeeze-and-Excitation networks and attention mechanism in automatic detection of coffee leaf diseases based on images
Get PDFThis research examines the effectiveness of Squeeze-and-Excitation Networks (SENet) combined with Attention Mechanism for automated detection of coffee leaf diseases. The integration of SENet and Attention Mechanism presents a promising technological opportunity as SENet has proven effective in improving CNN performance by modeling channel interdependencies, while Attention Mechanism enables focused feature extraction on crucial leaf areas - a combination that remains underexplored in coffee leaf disease detection. Using a combination of three datasets: Coffee Leaf Diseases, Disease and Pest in Coffee Leaves, and RoCoLe.Original, comprising 3,177 coffee leaf images divided into four classes (Healthy, Miner, Phoma, and Rust), this study compares the performance of SENet against other deep learning architectures such as InceptionV3, ResNet101V2, and MobileNet. Experiments were conducted with variations in epochs (15 and 30), three data split ratios, and three optimizer types. Results demonstrate that SENet with Attention mechanism performs, achieving a peak accuracy of 96% at 30 epochs with an 80:20 data ratio and RMSprop optimizer. InceptionV3 and MobileNet showed competitive performance with 93% accuracy, while ResNet101V2 achieved 81%. Class-wise analysis reveals SENet's proficiency in detecting various coffee leaf diseases, with F1-scores 91% for all classes
The Classification of Hate Comments on Twitter Using a Combination of Logistic Regression and Support Vector Machine Algorithm
No full textThis research was conducted to increase accuracy in classifying sentences containing hate speech and non-hate speech on Twitter. This is important to do because, as technology develops, it also comes with negative impacts, one of which is hate speech. This classification is carried out using a combination of Logistic Regression (LR) and Support Vector Machine (SVM) methods. This combination is based on the ease of implementation and speed of LR as well as SVM's ability to handle more complex and non-linear data. In this context, LR is used to model the probability that a comment on Twitter contains hate elements or not. The model can then provide probability predictions for each class, and a threshold can be set to determine the final class. This research shows that combining these methods can build a good classification model with an accuracy of 96%
Breast Cancer Diagnosis Utilizing Artificial Neural Network (ANN) Algorithm for Integrating Multi-Omics Data and Clinical Features
Get PDFBreast cancer is one of the most common diseases affecting women worldwide, with a significant impact on patient's health and quality of life. Despite advances in medical technology and research, breast cancer diagnosis remains a challenge due to its complexity involving various biological and clinical factors. Several previous studies have focused on detecting this disease with optimal accuracy, but the selection of appropriate algorithms and methods is key to achieving this goal. This study aims to improve the accuracy of breast cancer diagnosis by using the ANN algorithm and data balancing method, SMOTE. This research uses Multi-Omic data and Clinical Features obtained in general from Kaggle. The research process is carried out in several stages, namely Data Collection, Preprocessing, Oversampling, Modeling, and Evaluation. This research successfully obtained an increase in accuracy, which was able to achieve an accuracy of 99.30%. This research shows that early detection of breast cancer with ANN algorithm and data balancing using SMOTE can improve accuracy performance in early detection of breast cancer. Given the use of data in this study is not too large, it is recommended for further research to use a larger dataset to validate the strength of the model that has been built on more varied data
Implementation of Least Significant Bit Steganography to Secure Text Messages in Images
Get PDFSteganography is a technique of securing secret messages in other messages that are not known. Simulation of the steganography method using the Least Significant Bit technique is used to change the last bit in one byte of data by using a text message as the container medium. This study aims to implement the security of text messages in images using the Least Significant Bit technique which is supported by the steganography method. Simulation techniques are used to conduct studies using Cryptool2 which can describe the concept of cryptography. The results obtained from this study regarding the security of text message insertion into an image in *.jpg and *.png format with 5 sampling trials are (1) the encrypted image cannot be distinguished directly through human eyes, (2) there is an increase in file size the image after being encrypted with an average for five trials is 0.31%, this increase depends on the length of the text message and a key to be inserted, the longer the insertion, the larger the resulting file size, (3) The higher the resolution of the image where the description encryption is inserted, the longer the process required, (4) The simulation time of steganographic decryption is faster than steganographic encryption. The decryption simulation process is the same as 50% of the encryption process
Classification of risk of death from heart disease or cigarette influence using the k-nearest neighbors (KNN) method
No full textHeart disease is one of the leading causes of death in Indonesia. In addition to coronary heart disease, smoking is the leading contributor to the death rate in Indonesia. This study aims to analyze the risk of death with the main variables of heart disease history and smoking history. This study classifies the risk of death of heart disease sufferers and smokers using the KNearest Neighbors (KNN) algorithm. The results showed that the KNN model had an accuracy of 52.38% in predicting the risk of death of smokers and heart disease patients. Confusion matrix analysis revealed that the model performed well in predicting classes 0 and 2, but had difficulty in predicting class 1. This study shows that KNN can be used to predict the risk of death of smokers and patients with heart disease with a satisfactory success rate
Automation of aquaponics systems through integration of RTC modules, turbidity sensors, and water level sensors
Get PDFAutomation of aquaponics systems is key in increasing agricultural efficiency and productivity. A system considered an innovative method of sustainable food production that combines fish farming with agriculture simultaneously. The problem that often occurs is crop failure, due to the lack of technology that can monitor automatically, so that farmers experience losses as a result of fish and plant growth does not thrive, and problems in urban areas that require land for planting and fish farming due to limited land in urban areas. There is another problem with the lack of accurate timing and monitoring of water quality in aquaponics. The purpose of this research is to implement an IoT system in aquaponics that is connected to various sensors, such as Turbidity sensors, Water Level sensors, and RTC Modules. To monitor water quality conditions in tilapia habitat and accurate time measurement to provide fish feed automatically so as to improve fish health and growth and support better and consistent yields. The findings of this study show that the implementation of IoT systems in aquaponics can overcome environmental monitoring and control problems effectively. Using the integration of RTC modules, turbidity sensors, and water level sensors effectively improves the automation of aquaponics systems. This optimized system provides better monitoring of environmental conditions, reduces reliance on manual maintenance, and increases overall productivity. It helps increase tilapia growth and plant productivity in a modern aquaponics system. This research demonstrates the great potential of IoT technology in increasing efficiency and productivity in aquaponics aquaculture, so that it can push the fisheries sector towards a more advanced and competitive direction. So the main conclusion is expected that this automation can increase the productivity of ecosystem balance, and can face food security challenges and move towards more environmentally friendly solutions, towards effective management in the future
Email spam detection: a comparison of svm and naive bayes using bayesian optimization and grid search parameters
No full textSpam emails are still a big problem, crowding out inboxes and annoying email users everywhere. SVM and Naive Bayes are frequently used algorithms that have demonstrated excellent performance in performing text classification, including spam detection. The purpose of this study is to evaluate the overall performance of SVM and Naive Bayes in the context of detecting spam emails using default parameters. This research utilizes Bayesian Optimization and Grid Search Parameters for both SVM and Naive Bayes models to help maximize the performance of the constructed models. This study uses a spam email dataset that has 2 sample groups, namely spam and ham. Of the three parameter selection methods that have been tested on the SVM Algorithm, Bayesian Optimization is a parameter tuning method that has the most satisfying results in accuracy, precision, recall, and f1 scores respectively with values of 98.5642%, 99.4048%, 89
Light sensor optimization based on finger blood estimation and IoT-integrated
Get PDFDiabetes mellitus is a prevalent disease in society. This condition results from various causes, such as lifestyle choices or genetic predisposition. To prevent diabetes mellitus, blood glucose levels must be monitored periodically, and dietary consumption must be managed. Blood glucose monitoring still uses the incision or minimally invasive approach. This approach poses a risk of infection and damage. This study devised a method to optimize a light sensor to measure blood glucose levels. This approach uses sensor optimization and an integrated Internet of Things (IoT) technology. The research findings demonstrate that the use of the optimization strategy leads to increased consistency in sensor values, which may then be transmitted wirelessly through the IoT network. The research results demonstrate that using the optimization strategy leads to increased consistency in sensor values, which may then be wirelessly transmitted through the IoT network
Using genetic algorithm feature selection to optimize XGBoost performance in Australian credit
Get PDFTo reduce credit risk in credit institutions, credit risk management practices need to be implemented so that lending institutions can survive in the long term. Data mining is one of the techniques used for credit risk management. Where data mining can find information patterns from big data using classification techniques with the resulting level of accuracy. This research aims to increase the accuracy of classification algorithms in predicting credit risk by applying genetic algorithms as the best feature selection method. Thus, the most important feature will be used to search for credit risk information. This research applies a classification method using the XGBoost classifier on the Australian credit dataset, then carries out an evaluation by measuring the level of accuracy and AUC. The results show an increase in accuracy of 2.24%, with an accuracy value of 89.93% after optimization using a genetic algorithm. So, through research on genetic algorithm feature selection, we can improve the accuracy performance of the XGBoost algorithm on the Australian credit dataset