Research in Electronic Multi-Sensor Accuracy in the Implementation of Soil Fertility Monitoring System Using LoRA

The use of electronic sensors to track the nutrients in the soil is an interesting tool for farmers. This has led to the sale of many different kinds of electronic sensors with different levels of accuracy. The accuracy of this electronic sensor was figured out by comparing the results of the sensor's measurements with the results of lab tests done in different ways. This study compares the accuracy of electronic devices used to measure soil nutrients like nitrogen, phosphorus, potassium, electrical conductivity, water pH, and humidity to measurements made in the lab using the ICP-OES (Inductively coupled plasma-optical emission spectroscopy) method. We used three electronic sensors and a transmission system based on LoRA (Long Range) to measure the nutrients in the soil and put the results on our website. The similarities between electronic sensors and laboratory test parameters include the standard deviation, accuracy value, and correlation test between sensors and from the sensors to laboratory test results. The standard deviation parameter test showed a big value between the electronic sensor and the lab test results. However, none of the three used electronic sensors had a standard deviation number that differed greatly from the others. Except for the pH value of the soil, the electronic sensor's accuracy tests for the other five parameters were not very good compared to the lab tests. Also, the sensor correlation test showed a high correlation, while the correlation test between sensor data and lab test results showed a low correlation

Mental Health State Classification Using Facial Emotion Recognition and Detection

Analyzing and understanding emotion can help in various aspects, such as realizing one’s attitude, behavior, etc. By understanding one’s emotions, one's mental health state can be calculated, which can help in the medical field by classifying whether one is mentally stable or not. Facial Recognition is one of the many fields of computer vision that utilizes convolutional networks or Conv Nets to perform, train, and learn. Conv Nets and other machine learning algorithms have evolved to adapt better to larger datasets. One of the advancements in Conv Nets and machines is the introduction of various Conv architectures like VGGNet. Thus, this study will present a mental health state classification approach based on facial emotion recognition. The methodology comprises several interconnected components, including preprocessing, feature extraction using Principal Component Analysis (PCA) and VGGNet, and classification using Support Vector Machines (SVM) and Multilayer Perceptron (MLP). The FER2013 dataset tests multiple models’ performances, and the best model is employed in the mental health state classification. The best model, which combines Visual Geometry Group Network (VGGNet) feature extraction with SVM classification, achieved an accuracy of 66%, demonstrating the effectiveness of the proposed methodology. By leveraging facial emotion recognition and machine learning techniques, the study aims to develop an effective method

Measurement Analysis of Non-Invasive Blood Glucose On Sensor Coplanar Waveguide Loaded Square Ring Resonator with Interdigital Coupling Capacitor

This study presents the experimental results of a system with a sensor structure detecting human blood glucose levels. A microwave-based sensor is used for non-invasive blood glucose monitoring. The sensor design uses an asymmetrically loaded CPW structure as a square ring resonator with an interdigital coupling capacitor on the ground side. Simulated with a load of artificial finger tissue made from gelatin, modeled in four layers. The first layer is the skin is the outermost tissue, the next layer is fat, blood and bone. Each layer of tissue has a certain thickness size; skin (0.3mm), fat (0.2mm), blood (1.5mm), and bone (4mm). The measurement simulation is used, HFSS as modeling simulation and VNA as a measurement of the physical representation of the design results with parametric optimization methods. To verify the correlation and the expected sensitivity, media with different dielectrics were mounted on the surface of the sensor resonator with blood glucose levels of 1mg/dl, 72mg/dl, 126mg/dl, 162mg/dl and 216mg/dl. Reflection factor S11 was observed based on dielectric constant blood glucose levels (dB) fluctuations. Analysis of the data on the graph between the independent variables, namely blood glucose concentration and the dependent variable levels of S11 has an “R†correlation value of 0.97. The sensitivity level of the sensor on the S11 reflection factor with HFSS simulation averages 73.36mdB/mgdl-1 and VNA reaches 82.39mdB/mgdl-1. The results are interesting for developing a more optimal glucose sensor system

Tree-Based Ensemble Methods and Their Applications for Predicting Students’ Academic Performance

Students’ academic performance is a key aspect of online learning success. Online learning applications known as Learning Management Systems (LMS) store various online learning activities. In this research, students’ academic performances in online course X are predicted such that teachers could identify students who are at risk much sooner. The prediction uses tree-based ensemble methods such as Random Forest, XGBoost (Extreme Gradient Boosting), and LightGBM (Light Gradient Boosting Machine). Random Forest is a bagging method, whereas XGBoost and LightGBM are boosting methods. The data recorded in LMS UI, or EMAS (e-Learning Management Systems) is collected. The data consists of activity data for 232 students (219 passed, 13 failed) in course X. This data is divided into three proportions (80:20, 70:30, and 60:40) and three periods (the first, first two, and first three months of the study period). Data is pre-processed using the SMOTE method to handle imbalanced data and implemented in all categories, with and without feature selection. The prediction results are compared to determine the best time for predicting students’ academic performance and how well each model can predict the number of unsuccessful students. The implementation results show that students’ academic performance can be predicted at the end of the second month, with best prediction rates of 86.8%, 80%, and 75% for the LightGBM, Random Forest, and XGBoost models, respectively, with feature selection. Therefore, with this prediction, students who could fail still have time to improve their academic performance

Performance of ShuffleNet and VGG-19 Architectural Classification Models for Face Recognition in Autistic Children

This study discusses the face recognition of children with special needs, especially those with autism. Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that affects social skills, ways of interacting, and communication disorders. Facial recognition in autistic children is needed to help detect autism quickly to minimize the risk of further complications. There is extraordinarily little research on facial recognition of autistic children, and the resulting system is not fully accurate. This research proposes using the Convolution Neural Network (CNN) model using two architectures: ShuffleNet, which uses randomization channels, and Visual Geometry Group (VGG)-19, which has 19 layers for the classification process. The research object used in the face recognition system is secondary data obtained through the Kaggle site with a total of 2,940 image data consisting of images of autism and non-autism. The faces of autistic children are visually difficult to distinguish from those of normal children. Therefore, this system was built to recognize the faces of people with autism. The method used in this research is applying the CNN model to autism face recognition through images by comparing two architectures to see their best performance. Autism and non-autism data are grouped into training data, 2,540, and test data, as much as 300. In the training stage, the data was validated using validation data consisting of 50 autism image data and 50 non-autism image data. The experimental results show that the VGG-19 has high accuracy at 98%, while ShuffleNet is 88%

Blockchain Technology in Malaysian Context: Bibliometric Analysis and Systematic Review

Blockchain technology has attracted widespread attention due to its compelling features, such as decentralization, transparency, and smart contracts, which can address significant issues in various industries in developing countries such as Malaysia. However, although several studies have arisen from diverse academic backgrounds addressing blockchain in Malaysia, no studies provide a comprehensive review and classification of the research in this field. The main goal of this research is to conduct a bibliometric analysis and systematic review of all blockchain papers published in Malaysia to understand the evolution of knowledge and present state and identify prospective future research fields. Web of Science and Scopus databases searched for existing literature on blockchain in Malaysia, and 76 papers were reviewed and categorized based on study purpose/focus, domain/sectors, the methodology employed, theories applied, and level of analysis. The findings show that blockchain is under-explored in Malaysia, and most current studies focus on Blockchain adoption in specific industries such as finance and supply chain management. However, in other areas, such as healthcare and education, Blockchain conceptual progress is still in its infancy. These findings are being utilized to suggest future research paths in this discipline, such as the need for methodological improvements and a theoretical basis to study blockchain in different sectors

Position and Temperature Detector for Autism Spectrum Disorder Children based on Sensor and Using IoT System

Children with Autism Spectrum Disorder (ASD) have characteristics where one cannot control emotions, which can cause tantrums that can impact behavior and body temperature. Based on this, they should be supervised by parents/relatives. To reduce the effects of these circumstances, this study seeks to design a technology system that can measure body temperature and detect the position of ASD children who can later monitor the activities they do. This system applies the ESP32 microcontroller and utilizes the GPS module to read the position of objects detected by the system and the MLX90614 temperature sensor, which can detect the body temperature of ASD children. Then, to facilitate checking, the control system is designed with an IoT system through the Blynk application to make it easier for users to supervise ASD children and can be accessed via smartphones in real time. In this study, detection testing was carried out on 3 ASD child subjects by grouping three conditions: namely, the child exits the location when the child is outside the predetermined location; then the child exits the body temperature when the child's body temperature is abnormal, and the child exits the location and body temperature outside normal. The results obtained show that the detector test results provide notifications to application users in the form of "child out of location," "child out of body temperature," and "child out of location and body temperature outside normal"

Optimization and Analysis of Polyhydroxyalkanoate (PHA) by Bacillus sp. Strain CL33 and Bacillus flexus Strain S5a from Palm Oil Mill Waste

Polyhydroxyalkanoate (PHA) is a biodegradable polymer that microorganisms can synthesize amidst non-optimal growth conditions with excess carbon sources. Palm oil, rich in fatty acids, serves as a carbon source for PHA synthesis. The bacterial PHA production can be influenced by carbon concentration in the growth medium. Therefore, determining the optimal concentration of palm oil as a carbon source is crucial for PHA production. Additionally, it is possible to determine the type of PHA generated by bacteria, which can then be utilized as information when processing utilizing the PHA. The experiment employed palm oil concentrations of 0.5%, 1%, and 2% and was carried out for periods of 48, 72, 96 hours. It was discovered that Bacillus sp. strain CL33 and Bacillus flexus strain S5a produced the most effective PHA at a concentration of 25 with an incubation period of 96 hours. The PHA generated by these bacteria was quantitatively analyzed through measurements of total bacterial growth, cell dry weight, and the levels of crotonic acid. PHA types were also analyzed using GC-MS, with monomers including 2-hydroxybutyrate(-2HB), 2-hydroxy-3-phenylpropionate (2H3PhP), 3-Hydroxyhexanoate (3HHx), 3-hydroxyoctanoate (3H2O), and 3-hydroxydecanoate (3HD). The Bacillus sp. strain CL33 yielded a PHA level of 92.23%. Meanwhile, Bacillus flexus strain S5a synthesized a polyhydroxyalkanoate comprising mostly 3-hydroxyhexanoate (3HHx) and polydimethylsiloxane (PDMS). The monomers used were decamethyltetrasiloxane, dodecamethylpentasiloxane, hexamethylcyclotrisiloxane, octamethylpentasiloxane, and dodecamethylcyclohexasiloxane. The type of PHA produced accounted for 85.93% of the total

Comparative Study of 3D Assets Optimization of Virtual Reality Application on VR Standalone Device

The progress of VR technology is undeniably rapid and reaches many sectors unrelated to what it first came out of entertainment. Today, many educational, health care, company training, etc., use and utilize VR in one way or another as their first step to familiarize a concept or procedure with their members or workers. The advantages of implementing educational content with VR are ease of development, cheap operational cost, and safety. This kind of approach is a good step considering the impact of VR technology on those cases. However, because the leading device for VR is a standalone VR, some things to consider are performance and visualization. Some early adaptations have these problems, performance issues, and the realism of visualization shown on the VR application. We can minimize those problems by meticulously optimizing 3D assets used in VR applications. The optimization method improved the average FPS on CBIVT by 14.02% on Quest 1 and 8.99% on Quest 2. The GPU utilization level percentage of Quest 1 decreased by 6.73%, and the Quest 2 GPU utilization level percentage decreased by 11.72%. On the other metrics, the user's comfortability also increases because of the enhancement of performance on the CBIVT application. These changes are marked by the increase in Important and Satisfactory levels to 4.26 and 4.16, respectively

Scenario Planning and Simulation in Disaster Response

This study examines disaster response effectiveness. This workshop was preceded by a case scenario featuring an explosion in a heavily populated Kuala Lumpur City, Malaysia, produced by the researchers. The agencies involved used this case scenario as a storyline to implement the response. The coordination efforts undertaken by each agency can be seen. It is plain to see the efforts of collaboration that each agency has been putting forth. Focus group discussion served as one forum for debating the course of action taken; at the same time, the action taken by each agency involved should align with the roles and responsibilities outlined. During the workshop, it was revealed that it assisted researchers in better understanding agencies' disaster response process in identifying shortcomings, determining gaps, and improving on the processes already in place for disaster response. However, it was noted that the success of implementing SP&S lies in the involvement and participation of each agency. Therefore, it can be concluded that communication and coordination between agencies are very important in the success of an operation, not only during SP&S but also during disaster response