Universitas Ahmad Dahlan Journal
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Application of vegetable oils as pharmaceutical ingredient: the impact of liquid lipid type on the characteristics of nanostructured lipid carrier
Get PDFRecently, drug encapsulation using a Nanostructured Lipid Carrier (NLC) has gained attention in formulation studies due to its high loading capacity and prevent drug expulsion during storage. Drug loading capacity is mainly affected by lipid type and composition, especially liquid lipids. Therefore, this research aims to evaluate the potential of avocado oil as a liquid lipid of NLC replacing pure oleic acid. All components including oil, glyceryl monostearate, Tween 20®, and Span 60® were processed to NLC by solvent injection method. The colloidal characteristics of NLC dispersion in water and 20 mM PBS pH 7 were determined, including transmittance, particle size, size distribution, zeta potential, loading capacity (LC), and loading efficiency (LE) of capsanthin in NLC. The results showed that NLC containing oleic acid (Fola) and avocado oil (Favo) dispersion in PBS exhibited a similar transmittance and zeta potential of 69-74% and -51 to -58 mV, respectively, whereas the particle size and size distribution of Favo were significantly higher than Fola. Moreover, the 1.3-fold higher LC and LE of Favo compared to Fola was insignificant (p>0.05). Additionally, the Tween 20® and Span 60® ratio of Favo should be improved to obtain an ideal particle size and size distribution as in Fola. In conclusion, avocado oil indicated the potential to be utilized as a liquid lipid of NLC formulation regarding zeta potential and drug loading. However, the surfactant composition should be adjusted to reduce the particle size of the NLC, leading to permeability enhancement in delivery, particularly oral administration
The role of assertiveness and self-esteem in enhancing resilience of rehabilitated adolescents drug addict
Get PDFThe rapid increase in adolescent drug abuse in Indonesia requires special attention to adolescent resilience against drug abuse and personal factors, such as assertiveness and self-esteem. This study aimed to investigate the role of assertiveness and self-esteem in enhancing resilience among rehabilitated adolescent drug addicts. This study employed a non-experimental quantitative approach with a correlational design. The research sample consisted of 80 individuals selected using a total sampling technique. The instruments used included the Irish Assertiveness Scale, Coppersmith Self-Esteem Inventory, and Grotberg Resilience Scale. Data analysis techniques involved descriptive statistical analysis and multiple regression analysis using SPSS software version 25. The results indicated that assertiveness significantly role the resilience of adolescent drug rehabilitation (r=0.567, p<0.05), while self-esteem also affected resilience (r=0.471, p<0.05). Furthermore, assertiveness and self-esteem jointly played a significant role in the resilience of adolescent drug rehabilitation, with an R Square value of 0.487 (p<0.05). These findings suggest that assertiveness and self-esteem can serve as strong predictors of resilience among adolescent drug addicts. Rehabilitation programs should focus on assertiveness and social skills education, as well as on developing more effective drug rehabilitation policies
Academic buoyancy training to improve school engagement for high school students in Cimahi
Get PDFHigh school students are more susceptible to feelings of boredom in their learning due to the increasingly complex academic expectations that arise during adolescence, which can lead to a decrease in school engagement among students. This study aims to determine the impact of academic buoyancy training on enhancing school engagement. The research employed an experimental method utilizing a purposive sampling technique to select a sample of 37 grade XI students from Santa Maria 3 High School in Cimahi City, all of whom exhibited low levels of school engagement. The intervention consisted of academic buoyancy training provided to 18 low-engaged students, while 19 students were assigned to a control group. Following the intervention, school engagement data were analyzed using the SPSS 25.0 software, employing differential test analysis techniques, specifically the Mann-Whitney U and Wilcoxon Rank statistical tests. The results indicated that the academic buoyancy training intervention significantly improved school engagement among high school students. Furthermore, the effect size analysis revealed that the academic buoyancy training intervention had a moderate impact on enhancing school engagement in this population
Interactive teaching materials flif pdf corporate using PBL model in elementary schools
Get PDFThis research was motivated by the lack of learning resources available in schools and the lack of variety in learning resources. The available learning resources are still in printed form and do not yet use teaching materials that use technology (Digital). This type of research is development research (R&D) using the ADDIE model. The ADDIE model consists of five steps: analysis, design, development, implementation, and evaluation. The subjects used were 22 grade V elementary school students. The data collection techniques used were interviews, validation sheets, questionnaires, and questions. The data obtained was analyzed quantitatively and qualitatively. The results of research on interactive teaching materials obtained a validity level of 85% from the material aspect, 83.33% from the language aspect, and 93.33 from the media aspect,t with a very valid category. Learning using interactive teaching materials is very practical, with the percentage of teacher responses at the research school being 90% and the percentage of student responses at the research school being 84.31%. Then, the effectiveness of interactive teaching materials showed very effective results, with a percentage increase in student learning outcomes of 86.2%. Thus, it can be concluded that interactive teaching materials using the Flip PDF Corporate application in elementary schools have been declared valid, practica,l and effective for use in the learning process
Malware Detection in Portable Document Format (PDF) Files with Byte Frequency Distribution (BFD) and Support Vector Machine (SVM)
Get PDFPortable Document Format (PDF) files as well as files in several other formats such as (.docx, .hwp and .jpg) are often used to conduct cyber attacks. According to VirusTotal, PDF ranks fourth among document files that are frequently used to spread malware in 2020. Malware detection is challenging partly because of its ability to stay hidden and adapt its own code and thus requiring new smarter methods to detect. Therefore, outdated detection and classification methods become less effective. Nowadays, one of such methods that can be used to detect PDF files infected with malware is a machine learning approach. In this research, the Support Vector Machine (SVM) algorithm was used to detect PDF malware because of its ability to process non-linear data, and in some studies, SVM produces the best accuracy. In the process, the file was converted into byte format and then presented in Byte Frequency Distribution (BFD). To reduce the dimensions of the features, the Sequential Forward Selection (SFS) method was used. After the features are selected, the next stage is SVM to train the model. The performance obtained using the proposed method was quite good, as evidenced by the accuracy obtained in this study, which was 99.11% with an F1 score of 99.65%. The contributions of this research are new approaches to detect PDF malware which is using BFD and SVM algorithm, and using SFS to perform feature selection with the purpose of improving model performance. To this end, this proposed system can be an alternative to detect PDF malware
Review of Peer-to-Peer (P2P) Lending Based on Blockchain
Get PDFPeer-to-Peer (P2P) lending is a financing business model that has gained popularity in recent years due to the ease of loan application, disbursement, and repayment processes. The volume of Peer-to-Peer (P2P) Lending transactions have a significant growth. One of the reasons for the popularity of Peer-to-Peer (P2P) lending is its utilization of technology in both the application and loan repayment processes. One such technology gaining traction in Peer-to-Peer (P2P) lending is blockchain technology. The popularity of blockchain technology lies in its ability to enhance the transparency of the transaction process. This literature study aims to address three main questions: What are the characteristics of blockchain suitable for Peer-to-Peer (P2P) lending , the benefits of implementing blockchain technology in Peer-to-Peer (P2P) lending and the challenges of Peer-to-Peer (P2P) lending based on blockchain. The findings reveal that there are characteristics of blockchain that can be applied to Peer-to-Peer (P2P) lending, bringing numerous benefits to the overall Peer-to-Peer (P2P) lending process. However, challenges persist in the implementation of blockchain technology in Peer-to-Peer (P2P) lending. The insights gained from this literature review are intended to guide researchers interested in studying the application of blockchain technology in the context of Peer-to-Peer (P2P) lending
An Innovative Artificial Intelligence-Based Extreme Learning Machine Based on Random Forest Classifier for Diagnosed Diabetes Mellitus
Get PDFSince 2014, the World Health Organization has accumulated data indicating that 8.5% of 18-year-olds and older have been diagnosed with diabetes. In 2019, diabetes caused the lives of 1.5 million people worldwide, with those under the age of 70 accounting for 48% of all diabetes-related deaths. It is estimated that diabetes causes an additional 460,000 deaths each year due to renal failure and that hyperglycemia contributes to about 20% of all cardiovascular disease-related deaths. Diabetes may have contributed to a 3% rise in the age-adjusted death rate between the years 2000 and 2019. In recent years, the fatality rate attributable to diabetes has increased by 13% in low- and middle-income countries. Statistics collected by the World Health Organization indicate that the number of persons diagnosed with diabetes has increased from 108 million in 1980 to 422 million in 2014. The objective of this study is to construct a model capable of diagnosing persons with diabetes reliably, correctly, and consistently. This research used secondary data offered by Kaggle. The original data came from the National Institute of Diabetes and Digestive and Kidney Diseases. Each of the up to 768 data points consists of nine characteristics and two outputs, such as diabetes and non-diabetes in the provided example. In this study, a single algorithm is constructed by integrating two separate algorithms. Random forest algorithms, which are based on machine learning, and extreme learning machines, which are based on deep learning, have generated extraordinarily accurate results. When the confusion matrix is used, 98.05% accuracy is attained. Therefore, it is feasible to conclude that the suggested method was successful in completing an adequate analysis and classifying the data
Development of Convolutional Neural Network Models to Improve Facial Expression Recognition Accuracy
Get PDFAdvancements in information and computer technology, particularly in machine learning, have significantly alleviated human tasks. One of the current primary focuses is facial expression recognition using deep learning methods such as Convolutional Neural Network (CNN). Complex models like CNNs often encounter issues such as gradient vanishing and overfitting. This study aims to enhance the accuracy of CNN models in facial expression recognition by incorporating additional convolutional layers, dropout layers, and optimizing hyperparameters using Grid Search. The research utilizes the FER2013 public dataset sourced from the Kaggle website, trained and evaluated using CNN models, hyperparameter tuning, and downsampling methods. FER2013 comprises thousands of facial images representing various human expressions, with a specific focus on four facial expression categories (angry, happy, neutral, and sad). Through the addition of convolutional and dropout layers, as well as hyperparameter optimization, the developed model demonstrates a significant improvement in accuracy. Findings reveal that the refined CNN model achieves a highest accuracy of 98.89%, with testing accuracy at 89%, precision 78%, recall 78%, and F1-score 78%. This research contributes by enhancing facial expression recognition accuracy through optimized CNN models and providing a framework beneficial for the social-emotional development of children with special needs and aiding in the detection of mental health conditions. Additionally, it identifies avenues for future research, including exploring advanced data augmentation techniques and integrating multimodal information. Furthermore, this study paves the way for applications across diverse fields like human-computer interaction and mental health diagnostics
A Hybrid CNN-SVR for Airfoil Aerodynamic Coefficient Prediction
Get PDFThe prediction of aerodynamic coefficients on airfoils using machine learning is increasingly popular due to its efficiency in time and cost. Research typically focuses on a single image type without comparing various types and output quantities (single or multi-output). Although convolutional neural networks (CNN) are widely used, their final layer is often suboptimal as a linear operator, and feature extraction results contain many parameters that can still be trained. Support vector regression (SVR) with kernel functions effectively reduces common errors in feature vectors. We propose a hybrid method, AeroCNNSVR, combining CNN as a feature extractor and SVR as a regressor to predict aerodynamic coefficients on airfoils. This study focuses on the shape and position of airfoils according to the angle of attack (AoA) without considering flow conditions. Using 14533 aerodynamic coefficients from 563 airfoil types, we created a dataset of grayscale and RGB airfoil images. Results show the proposed method with grayscale images performs better because combining SVR strengthens the predictive model, while grayscale images accurately represent the airfoil's shape and position. AeroCNNSVR achieves lower RMSE values for Cl (0.101522), Cd (0.016450), and Cm (0.129661) compared to the CNN model’s Cl (0.112493), Cd (0.019060), and Cm (0.130041). Additionally, AeroCNNSVR's R² values for Cl (0.976071), Cd (0.928700), and Cm (0.860574) surpass those of the CNN model (Cl 0.970620, Cd 0.904282, Cm 0.816355). This research contributes by 1) proposing an alternative besides CFD for predicting and identifying trends in aerodynamic coefficients of airfoils in a much shorter time during the design stage; 2) offering wind tunnel practitioners for early detection of configuration errors; 3) providing an overview of the aerodynamic characteristics of the airfoil under test, including the angle at which stall conditions occur
Content-Based Filtering in Recommendation Systems Culinary Tourism Based on Twitter (X) Using Bidirectional Gated Recurrent Unit (Bi-GRU)
Get PDFTo address the challenge of information overload in the rapidly expanding culinary sector, a recommendation system using Content-Based Filtering (CBF) and the Bidirectional Gated Recurrent Unit (Bi-GRU) algorithm was developed. This system can help users to suggest culinary options based on user profiles and preferences. Twitter (X) is frequently used to gather culinary reviews in Bandung, forming the foundation for developing recommendation systems. This research contributes to integrating CBF and Bi-GRU to enhance the relevance of culinary recommendations. The system uses Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction and Cosine Similarity for item matching. Research adapting CBF and Bi-GRU methods specifically for culinary recommendations, especially in Bandung, remains limited. This study focuses on evaluating the performance of a culinary recommendation system. Data collected from Twitter (X) and PergiKuliner includes 2,645 reviews from 44 Twitter (X) accounts and on 200 culinary places. The culinary recommendation model, using CBF with TF-IDF and Cosine Similarity, achieved a Mean Absolute Error (MAE) of 0.254 and Root Mean Square Error (RMSE) of 0.425, indicating high accuracy in rating predictions compared to previous studies. From the experiments conducted, the third experiment using Bi-GRU, SMOTE, and the Nadam algorithm showed the best improvement with a learning rate of 0.014563484775012459, achieving an accuracy of 86.8%, precision of 86.3%, recall of 85.2%, and an F1-Score of 85.5%, with a 16.2% increase in accuracy from the baseline. Thus, this system effectively helps users with culinary recommendations in Bandung, providing good performance based on user preferences